Evaluating the impact of COVID-19 on cancer care: a comprehensive analysis of treatment modifications, risk factors, and patient outcomes

TL;DR

This study examines how cancer treatments affect the risk and outcomes of COVID-19 in cancer patients, finding that chemotherapy and radiotherapy increase infection risk but treatment interruptions don't harm outcomes.

Contribution

The study identifies chemotherapy and radiotherapy as risk factors for COVID-19 in cancer patients and shows treatment interruptions have no significant impact on efficacy or toxicity.

Findings

Chemotherapy and radiotherapy significantly increase the risk of COVID-19 infection in cancer patients.

Treatment interruptions during the pandemic did not significantly affect treatment efficacy or toxicity profiles.

Chemotherapy had the highest treatment suspension rate (42.86%) during the pandemic.

Abstract

Cancer patients will have an increased incidence of corona virus disease 2019 (COVID-19) infection. The severity of COVID-19 infection varies among cancer patients who have other complications and are being treated.

This retrospective cohort study evaluated the impact of cancer treatments on COVID-19 incidence and outcomes in 603 cancer patients. Key objectives included assessing treatment-related risks, severity, and treatment efficacy, along with the effects of treatment interruptions on patient recovery and therapy resumption.

This study included 603 cancer patients, of whom 68 (11.28%) were infected with COVID-19, and 398 (66%) were vaccinated. Logistic regression analysis revealed that underlying comorbidities, chemotherapy, and radiotherapy were significantly associated with an increased risk of COVID-19 infection (P < 0.05 for chemotherapy and radiotherapy). Factors such as sex, smoking status, cancer pathology, and staging showed no significant correlation with COVID-19 incidence. Treatment disruptions during the pandemic were observed, with chemotherapy being most affected (42.86% suspension), while surgery and TKI therapy showed minimal interruption. Efficacy analysis indicated no significant difference in treatment outcomes between suspended and uninterrupted treatments (P = 0.758). Treatment suspensions did not significantly alter toxicity profiles, with bone marrow suppression being the most frequent toxicity.

Chemotherapy and radiotherapy increased the risk of COVID-19 in cancer patients, with treatment interruptions not affecting efficacy or toxicity, underscoring the need for tailored management.

Full text

Introduction

Coronavirus disease 2019 (COVID-19), caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global public health crisis [1]. As a newly emerged infectious disease, COVID-19 poses a significant threat to populations without prior immunity, particularly to individuals who are elderly or have underlying conditions such as diabetes, coronary heart disease, and hypertension [2, 3]. Cancer patients are at increased risk of both COVID-19 infection and mortality, with clinical studies demonstrating a significantly higher infection rate and case fatality rate compared to the general population [4]. Specifically, a case fatality rate of 5.6% was reported in cancer patients, nearly three times higher than the overall population’s rate [5, 6].

Epidemiological data reveal that cancer patients represent approximately 2% of all individuals infected with SARS-CoV-2, with the majority being elderly, predominantly in the 76 to 85-year age group (36.5%), and male (59.5%). Among these patients, solid tumors account for 59% of cases, and 54% are in an active stage of their cancer [7]. The tumor disease itself, coupled with ongoing cancer treatments such as chemotherapy and radiotherapy, further elevates the risk of severe outcomes in COVID-19-infected patients. Periodic treatments like chemoradiotherapy may compromise immune function, potentially enhancing vulnerability to infections such as COVID-19 [7]. A study found that cancer patients infected with COVID-19 had a significantly higher mortality rate (41.3% vs. 17.2%, P = 0.0001) compared to non-cancer patients [8]. The risk of death was notably increased in cancer patients, independent of other factors. Cancer patients also exhibited fewer common symptoms such as fever, chills, and cough. Another study demonstrated that cancer patients infected with COVID-19 had a significantly higher rate of mortality and mechanical ventilation compared to patients without a cancer history [9]. Specifically, 41.7% of cancer patients died from COVID-19, compared to 6.8% in the non-cancer group, and 25.0% of cancer patients required mechanical ventilation, compared to 4.7% in the control group.

Despite these observations, there is a lack of definitive clinical data directly supporting this notion.

Studies have shown that the main clinical manifestations of COVID-19 are fever, cough, and dyspnea [10–12]. While the clinical manifestations of COVID-19 in cancer patients are generally similar to those in non-cancer patients, cancer patients are more likely to experience dyspnea and exhibit higher levels of pro-inflammatory cytokines, such as TNF-α, IL-6, and IL-2R, as well as significant decreases in lymphocytes and T cells [12, 13]. Moreover, the clinical presentations of COVID-19 pneumonia and immune-associated pneumonia are often similar, with both conditions showing symmetrical patchy ground-glass opacities (GGO) and consolidation areas on CT scans. To distinguish between these conditions, routine laboratory tests and CT imaging are recommended after RT-PCR confirmation [13, 14]. In addition, elevated C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) are commonly observed in patients with immune-associated pneumonia, while D-dimer, liver enzymes, lactate dehydrogenase (LDH), myoglobin, and troponin levels are increased in severe cases [13, 15].

In conclusion, while previous research has highlighted various risk factors associated with COVID-19 infection—such as disease type, critical illness indicators, long-term complications, and the risk of reinfection—there remains ongoing debate regarding the impact of prior cancer treatments on the incidence and severity of COVID-19 in cancer patients. This study seeks to address this gap by analyzing data from clinical questionnaires collected from cancer patients who underwent chemotherapy and radiotherapy during the pandemic. The results provide compelling evidence of a significant association between these cancer treatments and an increased susceptibility to COVID-19 infection.

Methods

Study design and objectives

This retrospective cohort study aimed to evaluate the relationship between cancer treatment regimens and the incidence, severity, and outcomes of COVID-19 infection in cancer patients. The primary objectives were to assess the impact of chemotherapy, radiotherapy, targeted therapy, immunotherapy, antiangiogenic therapy, and other treatments on the likelihood of contracting COVID-19, and to examine the outcomes of infection, including disease severity, treatment efficacy, and toxicity. The secondary objectives included evaluating changes in treatment outcomes following interruptions due to the COVID-19 pandemic and identifying the impact of these treatments on patient recovery, treatment suspension rates, and resumption of therapy.

Study population

The study included 603 cancer patients diagnosed with SARS-CoV-2 infection at Shandong Cancer Hospital and Institute between December 2019 and 2023. COVID-19 diagnosis was confirmed using either nucleic acid testing (RT-PCR) or chest CT scans, with the timing of infection and the onset of symptoms, including fever, cough, and sore throat, recorded. The inclusion criteria required that patients be diagnosed with cancer and have a history of at least one cancer treatment, including chemotherapy, radiotherapy, targeted therapy, immunotherapy, or anti-angiogenesis therapy. Patients who had not received any anti-cancer treatment during the study period or were undergoing surgery were excluded. Demographic information, clinical characteristics, and cancer-specific data (including cancer type, stage, and Karnofsky Performance Status (KPS)) were collected for each patient.

COVID-19 diagnosis and clinical management

Detailed data on the diagnosis and clinical management of COVID-19 were collected for all enrolled patients. COVID-19 diagnosis was confirmed by nucleic acid testing (RT-PCR) or chest CT scans, with the timing of infection and the onset of symptoms, such as fever, cough, and sore throat, documented. Patients diagnosed with COVID-19-associated myocarditis underwent further evaluations, including hematological assessments (e.g., BNP, TNT), blood coagulation profiles, and electrocardiogram abnormalities. Information on COVID-19 treatments, including antiviral agents, corticosteroids, and supportive care, was also recorded, along with any interruptions in cancer treatment due to COVID-19. Furthermore, vaccination status was documented, noting whether patients had been vaccinated and the type of vaccine administered.

Cancer treatment regimens

Detailed information was gathered on the types of anti-tumor treatments received by the patients. These treatments included chemotherapy (67.50%), radiotherapy (32.01%), immunotherapy (40.80%), targeted therapy (17.58%), and anti-angiogenic therapy (18.74%). Patients were grouped based on whether they had received these treatments, and the relationship between treatment modality and the risk of contracting COVID-19, as well as disease severity, was analyzed. Additionally, the timing of treatment relative to COVID-19 infection was recorded to assess any impact on infection rates. Patients who underwent surgery were excluded from the analysis to focus on systemic treatments.

Endpoints

The primary endpoint was the incidence of COVID-19 infection among cancer patients, categorized by treatment modality. Secondary endpoints included the severity of COVID-19 infection, defined by progression to severe pneumonia, need for intensive care, and mortality. Additional endpoints were the efficacy of anti-tumor treatments following the infection, including Complete Response (CR), Partial Response (PR), Stable Disease (SD), and Progressive Disease (PD). The analysis also assessed treatment toxicity profiles, including fatigue, electrolyte disturbances, dermatitis, secondary pneumonia, hepatic or renal impairment, gastrointestinal reactions, and bone marrow suppression, particularly in relation to treatment interruptions.

Data collection

Data were collected using customized data collection forms that included information on demographics, comorbidities, cancer type, treatment regimens, and the timing of treatment interruptions. The occurrence of COVID-19 infections was documented, as well as any related interruptions in anti-tumor therapies. The data was independently reviewed by two investigators to ensure accuracy and completeness. This study was approved by the Ethics Committee of Shandong Cancer Hospital and Institute.

Data statistics

Statistical analysis was performed using SPSS 26.0. Descriptive statistics were used to summarize demographic characteristics, with continuous data expressed as medians and quartile ranges, and categorical data presented as frequencies and percentages. Univariate analysis was performed to assess the association between various demographic factors (such as age, gender, smoking status, and comorbidities) and the incidence of COVID-19 infection. Chi-square (χ^2^) test were used to evaluate the association between treatment modalities and COVID-19 incidence.

Multivariate logistic regression analysis was conducted to identify independent risk factors for the occurrence of COVID-19 infection and its severity. Variables included in the multivariate model were age, gender, comorbid conditions, cancer type, cancer stage, and KPS, as these have been identified as potential confounders in the relationship between cancer treatment and COVID-19 outcomes. A P-value of < 0.05 was considered statistically significant.

Results

Basic data

Basic patient information

This study analyzed 603 cancer patients, of whom 68 (11.28%) were infected with COVID-19, while 535 (88.72%) were not (Table 1). The patient population was predominantly male (67.50%), with females comprising 32.50%. Most patients were under 65 years old (64.34%), and 35.66% were 65 or older. A significant proportion of patients had a Karnofsky Performance Status (KPS) score above 80 (97.51%).

Table 1. Basic patient informationNameCategoryCountsPercentage (%)Cumulative Percentage (%)Sexmale40767.5067.50female19632.50100.00Age< 65 yr38864.3464.34≥ 65 yr21535.66100.00KPS score0<80152.492.49≥ 8058897.51100.00SmokeNo35258.3758.37Yes25141.63100.00HypertensionNo47178.1178.11Yes13221.89100.00Myocardial infarctionNo58897.5197.51Yes152.49100.00Cerebral infarctionNo59097.8497.84Yes132.16100.00Kidney injuryNo60299.8399.83Yes10.17100.00DiabetesNo55291.5491.54Yes518.46100.00Coronary heart diseaseNo58196.3596.35Yes223.65100.00Operation HistoryNo59198.0198.01Yes121.99100.00Disease of respiratory systemNo58396.6896.68Yes203.32100.00OthersNo58496.8596.85Yes193.15100.00Basic Diseasewithout39164.8464.84115625.8790.71≥ 2569.29100.00PathologyAdenocarcinoma32954.5654.56Squamous carcinoma10717.7472.31Small cell carcinoma13923.0595.36Other carcinoma284.64100.00Staging050.830.831213.484.312203.327.63317128.3635.99438664.01100.00Ttotal603100.0100.0

Regarding comorbidities, 58.37% of patients were non-smokers, and 41.63% were smokers. Hypertension was present in 21.89% of patients, while 78.11% were free of it. Diabetes affected 8.46%, and other conditions such as myocardial infarction, cerebral infarction, and kidney injury were rare (2.49%, 2.16%, and 0.17%, respectively). Most patients had no underlying diseases (64.84%), while 25.87% had one, and 9.29% had two or more. The most common cancer type was adenocarcinoma (54.56%), followed by small cell carcinoma (23.05%), squamous cell carcinoma (17.74%), and others (4.64%). Interestingly, patients with more advanced cancer, particularly stage 4 (64.01%), were more susceptible to COVID-19, highlighting the potential link between cancer progression and increased vulnerability to infections.

Treatment patterns

Among the cohort, 48.26% had received two prior treatments, 36.48% one, and 15.26% three or more. Surgical interventions were rare (2.82%, Table 2). Chemotherapy was the most common prior treatment (67.50%), while 32.50% had not received it. Radiotherapy and immunotherapy were less frequent, administered to 32.01% and 40.80% of patients, respectively.

Table 2. Treatment patternsNameCategoryCountsPercentage (%)Cumulative Percentage (%)Number of previous treatment modes122036.4836.48229148.2684.74≥ 39215.26100.00Previous operationNo58697.1897.18Yes172.82100.00Prior chemotherapyNo19632.5032.50Yes40767.50100.00Prior radiotherapyNo41067.9967.99Yes19332.01100.00Prior immunotherapyNo35759.2059.20Yes24640.80100.00Previous TKI treatmentNo49782.4282.42Yes10617.58100.00Previous antivascular therapyNo49081.2681.26Yes11318.74100.00VaccinationNo20534.0034.00Yes39866.00100.00Total603100.0100.0Treatment of COVID-19 (n = 68)Antipyretic4464.7164.71Anti-inflammatory1420.5920.59Traditional Chinese Medicine68.828.82Steroid + Anti-inflammatory45.885.88Total68100.0100.0

A total of 68 patients diagnosed with COVID-19 were treated with various therapies: 44 patients (64.71%) received antipyretic therapy, 14 patients (20.59%) received anti-inflammatory treatment, 6 patients (8.82%) received traditional Chinese medicine, and 4 patients (5.88%) received steroid combined with anti-inflammatory therapy. Additionally, among all patients included in the study, 398 patients (66%) were vaccinated, while 205 patients (34%) were not vaccinated.

Analysis of high-risk factors for the occurrence of COVID-19

Univariate analysis of COVID-19 and comprehensive demographic

No significant associations were found between COVID-19 and sex, smoking status, or comorbidities such as hypertension, myocardial infarction, cerebral infarction, and kidney injury (P > 0.05, Table 3). Although age ≥ 65 years had a higher COVID-19 incidence (45.59% vs. 54.41%), the difference was not significant (P = 0.069). Similarly, no significant difference was found in KPS (P = 0.056). Cancer pathology and staging showed no significant correlation with COVID-19 infection (P = 0.247 and P = 0.256, respectively). These findings indicate that none of the analyzed factors, including demographic characteristics, comorbidities, cancer pathology, and staging, were significantly associated with COVID-19 incidence in this cohort.

Table 3. Univariate analysis of COVID-19 and basic informationCategoryNameCOVID-19 (%)Totalχ^2^/Z p NoYesSexMale361(67.48)46(67.65)407(67.50)0.0010.977Female174(32.52)22(32.35)196(32.50)Total53568603Age< 65 yr351(65.61)37(54.41)388(64.34)3.2960.069≥ 65 yr184(34.39)31(45.59)215(35.66)Total53568603KPS0 < 8011(2.06)4(5.88)15(2.49)3.6410.056≥ 80524(97.94)64(94.12)588(97.51)Total53568603SmokeNo311(58.13)41(60.29)352(58.37)0.1160.733Yes224(41.87)27(39.71)251(41.63)Total53568603High blood pressureNo416(77.76)55(80.88)471(78.11)0.3450.557Yes119(22.24)13(19.12)132(21.89)Total53568603Myocardial infarctionNo521(97.38)67(98.53)588(97.51)0.3270.568Yes14(2.62)1(1.47)15(2.49)Total53568603Cerebral infarctionNo523(97.76)67(98.53)590(97.84)0.1710.680Yes12(2.24)1(1.47)13(2.16)Total53568603Kidney injuryNo534(99.81)68(100.00)602(99.83)0.1270.721Yes1(0.19)0(0.00)1(0.17)Total53568603DiabetesNo490(91.59)62(91.18)552(91.54)0.0130.908Yes45(8.41)6(8.82)51(8.46)Total53568603Coronary heart diseaseNo513(95.89)68(100.00)581(96.35)2.9020.088Yes22(4.11)0(0.00)22(3.65)Total53568603Operation HistoryNo523(97.76)68(100.00)591(98.01)1.5560.212Yes12(2.24)0(0.00)12(1.99)Total53568603Disease of respiratory systemNo516(96.45)67(98.53)583(96.68)0.8150.367Yes19(3.55)1(1.47)20(3.32)Total53568603Other DiseaseNo517(96.64)67(98.53)584(96.85)0.7090.400Yes18(3.36)1(1.47)19(3.15)Total53568603Background diseaseWithout342(63.93)49(72.06)391(64.84)2.0030.3671141(26.36)15(22.06)156(25.87)≥ 252(9.72)4(5.88)56(9.29)Total53568603PathologyAdenocarcinoma293(54.77)36(52.94)329(54.56)4.1360.247Squamous carcinoma97(18.13)10(14.71)107(17.74)Small cell carcinoma118(22.06)21(30.88)139(23.05)Other carcinoma27(5.05)1(1.47)28(4.64)Total535(88.72)68(11.28)603(100.0)Staging05(0.93)0(0.00)5(0.83)5.3170.256119(3.55)2(2.94)21(3.48)218(3.36)2(2.94)20(3.32)3144(26.92)27(39.71)171(28.36)4346(65.23)37(54.41)383(64.01)Total53568603**P* < 0.05 **P < 0.01

Univariate analysis of COVID-19 and previous treatment patterns

In the exploration of the relationship between COVID-19 incidence and prior treatment modalities, as detailed in Table 4, the analysis revealed minimal variance in COVID-19 incidence rates between patients with a surgical history (2.94%) and those without (2.80%, χ^2^ = 0.004, P = 0.949). The investigation also showed that exposure to chemotherapy and radiotherapy could predispose patients to an immunocompromised state, potentially increasing their susceptibility to COVID-19. Specifically, 67.50% of the cohort (n = 407) received chemotherapy, 32.01% (n = 193) underwent radiotherapy, 40.80% (n = 246) received immunotherapy, and 17.58% (n = 106) were treated with TKIs, while 18.74% (n = 113) received anti-angiogenic treatment.

Table 4. Univariate analysis of COVID-19 and previous treatment patternsCategoryNameCOVID-19 (%)Totalχ^2^ P NoYesPrevious operationNo520(97.20)66(97.06)586(97.18)0.0040.949Yes15(2.80)2(2.94)17(2.82)Total53568603Prior chemotherapyNo182(34.02)14(20.59)196(32.50)4.9600.026Yes353(65.98)54(79.41)407(67.50)Total53568603Prior radiotherapyNo374(69.91)36(52.94)410(67.99)7.9790.005Yes161(30.09)32(47.06)193(32.01)Total53568603Prior immunotherapyNo319(59.63)38(55.88)357(59.20)0.3500.554Yes216(40.37)30(44.12)246(40.80)Total53568603Previous TKI treatmentNo437(81.68)60(88.24)497(82.42)1.7880.181Yes98(18.32)8(11.76)106(17.58)Total53568603Previous antivascular therapyNo434(81.12)56(82.35)490(81.26)0.0600.806Yes101(18.88)12(17.65)113(18.74)Total53568603P < 0.05 **P < 0.01

A significant disparity in COVID-19 incidence was observed between patients who had not received chemotherapy (20.59%) and those who had (34.02%, χ^2^ = 4.960, P = 0.026). Additionally, COVID-19 incidence was significantly higher in patients who had undergone radiotherapy (30.09%) compared to those who had not (69.91%, χ^2^ = 7.979, P = 0.005). These findings indicated a notable association between chemotherapy and radiotherapy exposure and an increased susceptibility to COVID-19. In contrast, immunotherapy, prior TKI therapy, and anti-angiogenic treatment did not increase the risk of COVID-19 infection.

Logistic regression analysis of risk factors

In model 1, COVID-19 infection status was the dependent variable, while sex, KPS score, smoking status, and basic disease were included as independent variables (Table 5). The regression equation for the model is expressed as follows: ln(P/(1 − P)) = − 3.678 + 0.342 × Sex − 0.278 × KPS score + 0.153 × Smoking status + 1.945 × Basic disease, where P denotes the probability of COVID-19 infection (P = 1), and (1 - P) represents the probability of not being infected (P = 0). The analysis identified basic disease as a significant determinant of COVID-19 infection, with a coefficient of 1.945, yielding an OR of 6.995 (95% CI: 4.644–10.537, P < 0.001). This suggests that individuals with underlying medical conditions were approximately seven times more likely to contract COVID-19 than those without comorbidities. In contrast, no significant associations were found for sex, KPS score, smoking status, or previous chemotherapy and radiotherapy (all P > 0.05).

Table 5. Binary logistic regressionCategoryregression coefficientSEWald χ^2^ P OROR (95% CI)Sex0.3420.4110.6900.4061.4070.629 ~ 3.151KPS score-0.2780.8710.1020.7500.7570.137 ~ 4.177Smoke0.1530.3650.1770.6741.1660.570 ~ 2.383Basic Disease1.9450.20986.605< 0.0016.9954.644 ~ 10.537Intercept-3.6780.99813.854< 0.0010.025Prior chemotherapy0.7290.3165.3340.0212.0741.117 ~ 3.851Prior radiotherapy0.7590.2628.3570.0042.1361.277 ~ 3.572Intercept-2.8860.30887.627< 0.001Dependent variable: COVID-19

In model 2, the dependent variable remained COVID-19 infection, with prior chemotherapy and radiotherapy as independent variables (Table 5). The model 2 formula is as follows: ln(P/1-P) = -2.886 + 0.729 * previous chemotherapy + 0.759 * previous radiotherapy. The regression analysis revealed that prior chemotherapy was associated with an OR of 2.074 (95% CI: 1.117–3.851, P = 0.021), indicating that patients with a history of chemotherapy had more than twice the likelihood of contracting COVID-19 compared to those without such a history. Similarly, prior radiotherapy was significantly associated with COVID-19 infection, with an OR of 2.136 (95% CI: 1.277–3.572, P = 0.004), indicating that radiotherapy exposure notably increased the odds of infection.

In the multiple logistic regression model, COVID-19 infection status was treated as the dependent variable, while pathology and staging were included as independent variables (Table 6). The analysis showed that neither pathology (P = 0.144, OR = 1.176, 95% CI: 0.946–1.463) nor staging (P = 0.050, OR = 1.491, 95% CI: 1.000–2.222) was significantly associated with COVID-19 infection (All P > 0.05).

Table 6. Multiple logistic regressionCategoryregression coefficientSEWald χ^2^ P OROR (95% CI)Pathology0.1620.1112.1310.1441.1760.946 ~ 1.463Staging0.3990.2043.8460.0501.4911.000 ~ 2.222Intercept-3.8120.81121.987< 0.001Dependent variable: COVID-19

Analysis of the influence of infection with novel coronavirus pneumonia on treatment

Table 7 summarizes the suspension of cancer treatments during the COVID-19 pandemic. Surgical interventions were largely unaffected, with 87.50% (n = 14 / 16) proceeding as planned. In contrast, chemotherapy faced significant disruptions, as 42.86% (n = 168 / 392) of treatments were suspended. Radiotherapy displayed a mixed response, with 52.38% (n = 99 / 189) continuing, while 47.62% (n = 90) were interrupted. Immunotherapy treatments showed greater continuity, with 60.59% (n = 143 / 236) ongoing, compared to 39.41% (n = 93) that were paused. TKI therapy exhibited notable resilience, with 80.20% (n = 81 / 101) of patients maintaining their schedules. Anti-vascular therapy experienced a near-equal distribution, as 50.93% (n = 55 / 108) continued, while 49.07% (n = 53) were halted.

Table 7. Status of suspension due to COVID-19 under different treatment modesCategoryTreatment suspensionCountsPercentage (%)Cumulative percentage (%)Previous surgery (n = 16)No1487.5087.50Yes212.50100.00Previous chemotherapy (n = 392)No22457.1457.14Yes16842.86100.00Previous radiotherapy (n = 189)No9952.3852.38Yes9047.62100.00Previous immunotherapy (n = 236)No14360.5960.59Yes9339.41100.00Previous TKI treatment (n = 101)No8180.2080.20Yes2019.80100.00Previous antivascular therapy (n = 108)No5550.9350.93Yes5349.07100.00

The difference of restart time interval between different treatment modes

Table 8 presents the Kruskal-Wallis test results comparing restart intervals across six treatment types, revealing significant differences (H = 13.314, P = 0.021). TKI treatment had the shortest restart interval, with a median of 1 day, though the sample size limited its applicability. Immunotherapy resumed after a median delay of 12 days, with a range of 5 to 22 days. Chemotherapy restarted faster, with a median delay of 9 days and a range of 4 to 16.3 days. Surgical treatments showed variability, with resumption times ranging from 1 to 20.5 days, and a median of 1 day. Anti-angiogenic therapies had the longest median delay of 35 days, with a range of 22 to 48 days.

Table 8. Differences in restart intervals between the 6 previous treatment modesPrior treatmentRestart intervalPrevious TKI treatment (n = 1)1.0(1.0,1.0)Previous immunotherapy (n = 39)12.0(5.0,22.0)Previous chemotherapy (n = 86)9.0(4.0,16.3)Previous surgery (n = 4)1.0(1.0,20.5)Previous antivascular therapy (n = 2)35.0(22.0,48.0)Previous radiotherapy (n = 16)3.5(1.0,23.5)Kruskal-Wallis test statistic H value13.31 P 0.021**P < 0.05 **P < 0.01

In conclusion, the analysis highlighted significant variability in resumption times, with anti-angiogenic therapy experiencing the longest delays, while TKI therapy and surgery resumed more promptly.

Variation in efficacy evaluation and toxicity evaluation after 2 cycles with or without treatment suspension

Difference in efficacy evaluation after 2 cycles with or without suspension of treatment

Table 9 examines the impact of COVID-19-related treatment suspensions on efficacy after two cycles. In the uninterrupted treatment group, 0.45% of cases achieved CR, while no CR was observed in the suspended treatment group, though this difference was not statistically significant. PD occurred in 8.11% of the uninterrupted group and 6.70% of the suspended group. PR rates were similar, at 4.95% for the uninterrupted group and 4.47% for the suspended group. SD was the most common outcome in both groups, with 86.49% in the non-suspended group and 88.83% in the suspended group. These results indicate that COVID-19-related treatment suspensions did not significantly affect treatment efficacy upon resumption (χ^2^ = 1.179, P = 0.758), with the majority of patients showing stable disease in both groups.

Table 9. Differences in efficacy evaluation after 2 cycles with or without suspension of treatmentCategoryNameWhether treatment was suspended due to COVID-19 (%)Totalχ^2^ P NoYesEvaluation of curative effect after 2 cyclesCR1(0.45)0(0.00)1(0.25)1.1790.758PD18(8.11)12(6.70)30(7.48)PR11(4.95)8(4.47)19(4.74)SD192(86.49)159(88.83)351(87.53)Total222179401**P* < 0.05 **P < 0.01

Differences in evaluation of toxicity with or without suspension of treatment

Fatigue occurred at similar rates across both cohorts, with 6.98% in the ongoing treatment group and 6.06% in the suspended treatment group, indicating that treatment interruption did not significantly affect fatigue prevalence (Table 10). Electrolyte disturbances were observed exclusively in the suspended treatment group, at a rate of 9.09%. Dermatitis was rare, affecting 3.03% of the suspended group and was absent in the uninterrupted group. Secondary pneumonia rates were comparable between the groups, with 13.95% in the continuous treatment group and 12.12% in the suspended group. Hepatic or renal impairment was reported by 4.65% of the non-suspended group and 3.03% of the suspended group. Gastrointestinal side effects were more prevalent in the non-suspended group (16.28%) compared to the suspended group (3.03%), suggesting a stronger association with continuous treatment. Bone marrow suppression emerged as the most common toxicity, with a slightly higher incidence in the suspended group (63.64%) compared to the non-suspended group (58.14%). In conclusion, the interruption of cancer treatments due to the COVID-19 pandemic did not significantly alter the overall toxicity profile of these treatments (χ^2^ = 1.179, P = 0.758).

Table 10. Differences in toxicity evaluation of treatment suspensionCategoryNameSuspension of treatment due to COVID-19 (%)Totalχ^2^ p NoYesToxicity assessmentFeeble3(6.98)2(6.06)5(6.58)8.6150.196Electrolyte disturbance0(0.00)3(9.09)3(3.95)Dermatitis0(0.00)1(3.03)1(1.32)Secondary pneumonia6(13.95)4(12.12)10(13.16)Hepatic and renal impairment2(4.65)1(3.03)3(3.95)Gastrointestinal reaction7(16.28)1(3.03)8(10.53)Myelosuppression25(58.14)21(63.64)46(60.53)Total433376**P* < 0.05 **P < 0.01

The difference of efficacy evaluation and toxicity evaluation after 2 cycles of different restart treatment modes

The difference of efficacy evaluation after 2 cycles of different restart treatment modes

Upon resumption of treatments, an analysis revealed that CR was exceedingly rare across all treatment types, occurring only in immunotherapy and chemotherapy, with rates of 0.66% and 0.39%, respectively (Table 11). In contrast, PD rates varied widely, with the highest observed in TKI therapy (19.05%) and anti-angiogenic therapy (14.06%), and the lowest in radiotherapy (7.37%). No partial response (PR) was noted in TKI therapy and surgery, while immunotherapy and chemotherapy showed PR rates of 5.92% and 5.10%. The most frequent outcome was stable disease (SD), which was observed across all treatment types, with 100% SD in surgery and over 86% in both immunotherapy and chemotherapy. These findings suggest that, although complete or partial responses were infrequent, the majority of patients achieved disease stability after treatment resumption.

Table 11. Differences in efficacy evaluation after 2 cycles of different restart treatment modesCategoryNameRestart Healing mode (%)Totalχ^2^ P TKI treatmentImmunotherapyChemotherapyOperationAnti-angiogenic therapyRadiotherapyEvaluation of post-cycle efficacyCR0(0.00)1(0.66)1(0.39)0(0.00)0(0.00)0(0.00)2(0.33)13.4200.570PD8(19.05)11(7.24)21(8.24)0(0.00)9(14.06)7(7.37)56(9.15)PR0(0.00)9(5.92)13(5.10)0(0.00)1(1.56)5(5.26)28(4.58)SD34(80.95)131(86.18)220(86.27)4(100.00)54(84.38)83(87.37)526(85.95)Total4215225546495612**P* < 0.05 **P < 0.01

The difference of toxicity evaluation in different reactivation therapy modes

A comparative analysis of the toxicity profiles following treatment resumption in cancer patients after COVID-19 infection revealed minor variations across different cancer treatments (Table 12). Fatigue was absent in TKI therapy and surgery, while anti-angiogenic therapy exhibited the highest incidence at 12.50%. Dermatitis was observed in immunotherapy and radiotherapy, with incidences of 5.00% and 4.76%, respectively. Hepatic or renal impairment was more frequently reported in immunotherapy and anti-angiogenic therapy, with rates of 10.00% and 12.50%, while no such impairments were noted in TKI therapy, surgery, or radiotherapy. Gastrointestinal reactions were more pronounced in chemotherapy and immunotherapy, with incidences of 11.54% and 10.00%, respectively. Bone marrow suppression was the most prevalent toxicity across all treatment modalities, particularly in chemotherapy, immunotherapy, and radiotherapy, with reported rates of 65.38%, 60.00%, and 52.38%, respectively. In conclusion, bone marrow suppression emerged as the most common adverse effect across nearly all treatment types, most notably in chemotherapy, immunotherapy, and radiotherapy.

Table 12. Differences in toxicity evaluation of different reactivation therapy modesCategoryNameRestart Healing mode (%)Totalχ^2^ P TKI treatmentImmunotherapyChemotherapyoperationAntivascular therapyRadiotherapyToxicity assessmentLacking in strength0(0.00)1(5.00)3(5.77)0(0.00)1(12.50)1(4.76)6(5.77)25.7780.686Electrolyte disturbance0(0.00)0(0.00)1(1.92)0(0.00)0(0.00)3(14.29)4(3.85)Dermatitis0(0.00)1(5.00)0(0.00)0(0.00)0(0.00)1(4.76)2(1.92)Secondary pneumonia1(50.00)2(10.00)6(11.54)1(100.00)2(25.00)4(19.05)16(15.38)Liver and kidney function is impaired0(0.00)2(10.00)2(3.85)0(0.00)1(12.50)0(0.00)5(4.81)Gastrointestinal reaction0(0.00)2(10.00)6(11.54)0(0.00)0(0.00)1(4.76)9(8.65)Myelosuppression1(50.00)12(60.00)34(65.38)0(0.00)4(50.00)11(52.38)62(59.62)Total220521821104**P* < 0.05 **P < 0.01

Discussion

Cancer, complications and COVID-19

Cancer patients are at increased risk for severe SARS-CoV-2 infection due to their immunocompromised status, which is a consequence of both the malignancy itself and the treatments they receive [16]. This heightened vulnerability is further exacerbated by frequent healthcare visits during active cancer treatment, as well as factors such as compromised immune function and complications arising from anti-cancer therapies [17]. A study assessing the seroprevalence of SARS-CoV-2 IgG antibodies in cancer patients prior to vaccination revealed that 29% of patients had detectable antibodies, including 20% without a documented history of COVID-19 infection [18]. The seroconversion rate for patients with a confirmed history of COVID-19 was 79.5%, which was notably lower than that observed in the general population. These findings suggest that cancer patients may exhibit a diminished immune response to SARS-CoV-2, thereby increasing their susceptibility to severe COVID-19 outcomes. According to the investigation, the COVID-19 positivity rate was likely influenced by cancer stage, type, and overall health status, including comorbidities such as age, race, ethnicity, smoking habits, and body mass index [19–21].

This study included 603 cancer patients, of whom 68 (11.28%) were infected with COVID-19, 398 (66%) were vaccinated, and 205 (34%) were unvaccinated. Regarding vaccine safety, this study found no severe adverse reactions to the vaccine. Studies have shown that the inactivated SARS-CoV-2 vaccine is safe and effective in cancer patients, with injection site pain and fever being the most common side effects [22, 23]. The overall seroconversion rate was 86.9%, with lower rates observed in older patients, those with hematological malignancies, and those undergoing chemotherapy [22]. In addition to age and comorbidities, smoking status was also identified as a potential risk factor for severe COVID-19 in cancer patients [19–21]. In this study, 58.37% of patients were non-smokers, and 41.63% were smokers. Unfortunately, the regression analysis did not demonstrate a significant association between age, sex, and smoking history with COVID-19 infection in this cancer cohort.

Although this study did not find a significant association between cancer type, staging, or KPS with COVID-19 incidence in the cohort, other studies have reported such correlations. For instance, a case report highlighted that patients with advanced cancer, particularly those with Stage IV thoracic esophageal cancer, were more susceptible to severe COVID-19 infections, which could lead to treatment interruptions [24]. Notably, the most common cancer type in this cohort was adenocarcinoma (54.56%), and patients with more advanced cancer, especially Stage IV (64.01%), were found to be more vulnerable to COVID-19, as reported in previous studies [12, 25].

The binary logistic regression analysis revealed that cancer patients with underlying conditions were more likely to contract COVID-19. The investigation indicated that cancer and cardiovascular diseases share common risk factors, such as hypertension, diabetes, and obesity [26]. Studies have shown that patients with these conditions are more likely to develop COVID-19 and experience more severe illness [16, 27, 28]. In a Chinese study of 138 hospitalized COVID-19 patients, 31.1% had hypertension and 14.1% had cardiovascular disease. Patients with these comorbidities were more likely to require ICU care [29]. A meta-analysis of 1,527 patients found that hypertension was the most common comorbidity, followed by cardiovascular disease and diabetes. Having these conditions increased the risk of ICU admission by two to three times [30]. These conditions significantly exacerbated COVID-19 severity and mortality, as seen in previous studies [31–34]. In this cohort, 21.89% of patients had hypertension, and 8.46% had diabetes. Consistent with previous studies [27], patients with cancer and cardiovascular comorbidities were more likely to develop myocarditis after contracting COVID-19 (data not shown in the table). Study highlights that diabetic individuals are more susceptible to severe COVID-19 due to altered metabolic programming in the kidney [35]. Diabetic-like kidney organoids showed higher viral loads after SARS-CoV-2 infection, and diabetic patient cells exhibited enhanced glycolysis and altered mitochondrial respiration, increasing viral susceptibility [35].

Cancer treatment patterns and COVID-19

A study demonstrated that cancer patients receiving palliative treatments and radiotherapy were at a higher risk of contracting COVID-19, with these factors also contributing to increased mortality [6]. The results of my study align with these findings, revealing that chemotherapy induces immunosuppression, thereby elevating the risk of serious infections and complications in COVID-19. Logistic regression analysis highlighted a significant association between prior chemotherapy, radiotherapy, and the incidence of COVID-19. Chemotherapy patients are particularly vulnerable to respiratory syndromes induced by chemotherapeutic agents and are more susceptible to viral infections due to the immunosuppressive effects of treatment. A review and meta-analysis assessing the impact of COVID-19 on cancer patients reported a higher likelihood of severe outcomes and increased mortality in this population [36, 37]. Interestingly, during the COVID-19 epidemic control period, the infection rate in patients with Hodgkin lymphoma receiving chemotherapy decreased to 2.1% of their treatment cycles, accounting for 24.1% of total cases, suggesting a reduction in the overall infection rate [38]. While recent chemotherapy treatments were not directly associated with worse COVID-19 outcomes, specific factors, such as active hematologic or lung malignancies and baseline neutropenia, were found to exacerbate outcomes [39].

Similarly, radiation therapy targeting the chest region may impair lung function, thereby worsening the impact of respiratory infections like COVID-19. No statistically significant associations were observed between surgery, immunotherapy, TKI therapy, and anti-angiogenic therapy with COVID-19 incidence. The low incidence of surgery among the cohort may reflect the limited extent of invasive procedures, which could be beneficial in the context of COVID-19, as surgical histories may complicate respiratory illnesses [40]. Furthermore, studies have indicated that patients with adenocarcinoma who received immunotherapy within four weeks prior to COVID-19 diagnosis had a reduced risk of death [41]. These findings suggest that the interactions between antineoplastic therapy, cancer type, and COVID-19 outcomes are complex and warrant further investigation under specific clinical conditions [41].

Adjustment and efficacy of cancer treatment modalities

Studies on the impact of chemotherapy and immunotherapy have confirmed that recent chemotherapy treatments are a strong risk factor for inadequate antibody responses, while immunotherapy may have a more limited effect on vaccine efficacy [42–44]. Regarding treatment modalities, radiotherapy has been adapted during the pandemic to replace or delay higher-infection-risk treatments [36]. For instance, short courses of radiotherapy have been used to delay surgery in certain cancer cases. However, with increasing demand, there may be a need to ration radiotherapy [36]. The risk associated with biological and monoclonal-antibody therapies is less clear, and some might be beneficial against COVID-19’s inflammatory storm. Clinical guidelines have been published to inform the systemic treatment of cancer during the pandemic, taking into account the balance of risks and benefist [36]. However, it’s important to note that recent treatment, including immunotherapy, does not significantly impact the outcome of COVID-19 in cancer patients [45]. The variation in treatment suspension rates across different modalities suggests that the decision to continue or halt treatment was not uniform but rather dependent on the type of treatment and possibly the patient’s overall health status and COVID-19 severity. Particularly, patients undergoing treatments that could potentially compromise their immune system, such as chemotherapy, might have been more prone to treatment suspensions due to heightened risks associated with COVID-19 infection.

These disruptions in care also led to a greater need for post-surgical radiation therapy, indicating worsened clinical outcomes [46]. Previous studies have suggested that COVID-19 may impact cancer patients differently based on disease stage and performance status, with certain cancer types showing varying susceptibility to infection and outcomes [47]. Specifically, the multivariate Cox regression analysis revealed that lung cancer (HR 1.76, CI95 1.23–2.53, p < 0.05), stage III (HR 3.63, CI95 2.21–5.98, p < 0.05), stage IV (HR 11.06, CI95 7.04–17.36, p < 0.05), and age at diagnosis (HR 1.04, CI95 1.02–1.05, p < 0.05) were independent predictors of poorer OS [47]. Especially due to treatment delays caused by the COVID-19 lockdown, there was a significant increase in tumor size, more advanced N-staging, and a higher incidence of extreme advanced disease in breast cancer patients [46, 48].

Unanswered Questions in COVID-19 and cancer research

Studies have shown that COVID-19 mRNA vaccines are safe for cancer patients and can induce seroconversion, although response rates are lower in those with hematologic malignancies or undergoing chemotherapy [49, 50]. The majority of solid tumor patients are able to seroconvert after two vaccine doses, and a third dose significantly improves immunization rates [50–52]. Given these findings, it is recommended that all cancer patients receive the full vaccination regimen, with a potential third booster dose for those actively undergoing chemotherapy or suffering from hematologic cancers [49]. Adverse events from vaccination are generally mild and include symptoms like pain at the injection site, fatigue, and headaches, though there are concerns regarding immune-related adverse events (irAEs) in patients undergoing immune checkpoint inhibitors and potential radiation reactions post-vaccination [53, 54].

Several key questions remain unanswered in clinical COVID-19 and cancer research. The effects of SARS-CoV-2 on lung cancer pathogenesis, particularly whether it directly infects cancer cells and influences tumor progression, remain unclear. Additionally, the long-term consequences of COVID-19 vaccines on immune function in cancer patients, especially those receiving immunosuppressive therapies, need further exploration. The impact of emerging COVID-19 variants, such as Delta and Omicron, on cancer patients’ immune responses and outcomes post-vaccination is also under-researched [55, 56]. Furthermore, the interaction between cancer therapies, including immune checkpoint inhibitors, and vaccine efficacy requires a deeper understanding.

Future research should focus on longitudinal, multi-center studies to evaluate the durability of vaccine protection and its interaction with different treatment regimens. The effectiveness of vaccines against emerging variants should be closely monitored, and the use of heterologous booster vaccines warrants further investigation. Additionally, more research is needed to understand the direct effects of SARS-CoV-2 infection on tumor progression and the combined impact of infection and vaccination on cancer treatment outcomes. Addressing these gaps is crucial to optimizing the management of cancer patients during the ongoing pandemic and future health crises.

Conclusion

COVID-19 incidence was low in the patient cohort, with no significant associations found between gender, age, or common comorbidities such as hypertension, diabetes, or cardiovascular diseases. However, patients undergoing chemotherapy and radiotherapy exhibited a significantly higher incidence of COVID-19. Binary logistic regression analysis further revealed that chemotherapy and radiotherapy were risk factors for COVID-19 infection, and patients with underlying diseases were more prone to infection. A considerable proportion of the cohort experienced treatment interruptions due to the pandemic, particularly those receiving chemotherapy, radiotherapy, and anti-angiogenic therapy. Treatment resumption did not show significant differences in efficacy or toxicity profiles between patients with and without treatment suspension, with Stable Disease being the most common outcome across all modalities. These findings emphasize the increased susceptibility of patients receiving chemotherapy and radiotherapy to COVID-19 and underscore the importance of tailored management strategies for these high-risk groups.