# PET/CT-Based prognostic model enhances early survival prediction in angioimmunoblastic t-cell lymphoma

## TL;DR

A new model using PET/CT scans and blood markers improves early survival predictions for patients with a rare type of T-cell lymphoma.

## Contribution

A novel prognostic model combining PET/CT parameters and clinical indicators for early survival prediction in AITL.

## Key findings

- SUVmax, β2MG, platelet, and albumin were identified as independent risk factors for survival.
- The model achieved a C-index of 0.78 and high AUC values for 6-month and 1-year survival prediction.
- The model outperformed existing systems like PIAI, IPI, and PIT in predictive accuracy.

## Abstract

To develop and validate a new prognostic model using baseline PET parameters and clinical indicators for predicting early overall survival (OS) in Angioimmunoblastic T-cell lymphoma (AITL) patients.

We conducted a retrospective cohort study from December 2009 to December 2023 (n=124) at a single center. The model’s predictors included baseline clinical characteristics, pathological indicators, laboratory metrics, and PET/CT parameters. Independent prognostic factors were identified using Cox regression and presented as nomograms. The C-index assessed predictive accuracy, while calibration plots and decision curve analysis evaluated prediction accuracy and discrimination ability. The model’s accuracy was compared with existing prognostic systems using C-index, NRI, ROC, and Kaplan-Meier survival curves.

SUVmax, β2MG, platelet, and albumin were identified as independent risk factors. The C-index for OS was 0.78 (95% CI: 0.70-0.85); for 1000 bootstrap samples, it was 0.76 (95% CI: 0.61-0.93). Calibration curves showed excellent agreement between predictions and actual observations. The AUC for 6-month and 1-year OS were 0.91(95% CI: 0.82-1.00) and 0.85 (95% CI: 0.77–0.94), respectively. The model outperformed PIAI, IPI, and PIT in predictive capacity.

The new prediction model reliably estimates outcomes for AITL patients, demonstrating high discrimination and calibration.