# Deep learning [18F]-FDG-PET/CT‑based algorithm for tumor burden estimation in metastatic melanoma patients under immunotherapy

## TL;DR

A deep learning model called PARS was tested for detecting and estimating tumor burden in metastatic melanoma patients using PET/CT scans, showing moderate accuracy but significant variability.

## Contribution

The study introduces and evaluates a novel deep learning algorithm for tumor burden estimation in metastatic melanoma using [18F]-FDG-PET/CT imaging.

## Key findings

- PARS detected 68.9% of expert-identified lesions but had a precision of only 46.8%.
- Lesion volume estimates showed good agreement (ICC = 0.77) but were generally underestimated.
- Tumor burden estimation was highly variable and poorly correlated with expert assessments (ICC = 0.28).

## Abstract

•This study evaluates a deep learning model (PARS) for lesion detection and tumor burden estimation in metastatic melanoma.•PARS detected 68.9% of expert-identified lesions but with a modest precision of 46.8%.•Lung lesions showed highest precision (74.0%), while bone lesions had the lowest (32.9%).•Lesion volume estimates had good agreement (ICC = 0.77) but were generally underestimated.•Tumor burden estimation was highly variable and poorly correlated with expert assessments (ICC = 0.28).

This study evaluates a deep learning model (PARS) for lesion detection and tumor burden estimation in metastatic melanoma.

PARS detected 68.9% of expert-identified lesions but with a modest precision of 46.8%.

Lung lesions showed highest precision (74.0%), while bone lesions had the lowest (32.9%).

Lesion volume estimates had good agreement (ICC = 0.77) but were generally underestimated.

Tumor burden estimation was highly variable and poorly correlated with expert assessments (ICC = 0.28).

Artificial intelligence is increasingly used in radiation oncology, yet its application for tumor burden (TB) estimation remains limited. This study evaluated the performance of a [18F]-fluorodeoxyglucose positron emission tomography/computerized tomography ([18F]-FDG-PET/CT)-based deep learning model, PET-Assisted Reporting System (“PARS”, Siemens Healthineers), for lesion detection, segmentation, and TB estimation in patients with metastatic melanoma undergoing immunotherapy.

This retrospective study included 165 stage IV melanoma patients who underwent [18F]-FDG-PET/CT imaging prior to immunotherapy. Gross tumor volumes were segmented using PARS and compared with manual delineations performed by radiation oncologists. Performance was assessed through lesion detection metrics (precision and recall), individual lesion volume agreement, and overall TB estimation accuracy.

PARS demonstrated an overall recall (sensitivity) of 68.9 %, though with modest precision (46.8 %). Performance was location-dependent, with highest precision observed for lung lesions (74.0 %) and lowest for bone lesions (32.9 %). For lesions detected by both methods, PARS tended to underestimate lesion volumes by an average (median) of 0.9 cc (median relative percentage difference (MRPD) =  −34.3 %), with a good agreement (intraclass correlations coefficient (ICC) = 0.77). The global TB in the whole cohort was overestimated by 28.3 %, but patient-level TB was on average (median) underestimated by 1.1 cc (MRPD =   −18.4 %) with high variability with a median absolute relative percentage difference (MARPD) = 68.6 %) and poor agreement (intraclass correlation coefficient (ICC) = 0.28).

PARS shows potential for treatment decision support with moderate accuracy in lesion detection and lesion volume estimation, but demonstrates significant variability in TB estimation, highlighting the need for further model refinements before clinical adoption.