Lesion selection and external evaluation of clinical-imaging survival models after transarterial chemoembolization for hepatocellular carcinoma
- 1University of Texas at Austin
Abstract
Background: Pretreatment computed tomography (CT) describes tumor characteristics in patients receiving transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC), but whether measuring more tumors improves mortality predictions in a new cohort remains unclear. We assessed the added value of different lesion-selection scopes beyond shared clinical predictors. Methods: We compared a model using five clinical variables with models adding CT measurements from the largest, two largest, or all retained annotated tumors. All models used the same regularized discrete-time survival estimator. Evaluation used repeated nested cross-validation in 228 WAW-TACE patients and frozen external application in 69 HCC-TACE-Seg patients, with 15 deaths by day 365. We assessed first-year probability error, discrimination, and calibration, followed by local recalibration using small external samples. Results: Internally, adding CT-derived lesion features did not establish an improvement in prediction accuracy. Externally, the all-lesion model had the highest discrimination point estimates, but its advantage over the clinical-only model was uncertain. Its mortality probabilities were less accurate than those of the clinical-only model, with calibration estimates consistent with overly extreme predicted risks. Local recalibration reduced its median held-out prediction error at day 365, but the median adjusted error remained higher than that of the adjusted clinical model. Simple tumor-burden predictors also did not improve external probability accuracy over the clinical baseline. Conclusions: In these cohorts, measurements from more annotated tumors did not yield more reliable first-year mortality probabilities. Comparisons of clinical-imaging models should evaluate external probability accuracy and calibration alongside patient ranking.
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BibTeX
@misc{xu2026lesion,
title = {{Lesion selection and external evaluation of clinical-imaging survival models after transarterial chemoembolization for hepatocellular carcinoma}},
author = {Z. Xu and A. Melek and E. Castillo and H. Wang},
howpublished = {medRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.28.26366759},
url = {https://www.medrxiv.org/content/10.64898/2026.10.28.26366759v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Lesion selection and external evaluation of clinical-imaging survival models after transarterial chemoembolization for hepatocellular carcinoma
AU - Z. Xu
AU - A. Melek
AU - E. Castillo
AU - H. Wang
PY - 2026
DA - 2026/10/28
PB - medRxiv
DO - 10.64898/2026.10.28.26366759
UR - https://www.medrxiv.org/content/10.64898/2026.10.28.26366759v1
AB - Background: Pretreatment computed tomography (CT) describes tumor characteristics in patients receiving transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC), but whether measuring more tumors improves mortality predictions in a new cohort remains unclear. We assessed the added value of different lesion-selection scopes beyond shared clinical predictors. Methods: We compared a model using five clinical variables with models adding CT measurements from the largest, two largest, or all retained annotated tumors. All models used the same regularized discrete-time survival estimator. Evaluation used repeated nested cross-validation in 228 WAW-TACE patients and frozen external application in 69 HCC-TACE-Seg patients, with 15 deaths by day 365. We assessed first-year probability error, discrimination, and calibration, followed by local recalibration using small external samples. Results: Internally, adding CT-derived lesion features did not establish an improvement in prediction accuracy. Externally, the all-lesion model had the highest discrimination point estimates, but its advantage over the clinical-only model was uncertain. Its mortality probabilities were less accurate than those of the clinical-only model, with calibration estimates consistent with overly extreme predicted risks. Local recalibration reduced its median held-out prediction error at day 365, but the median adjusted error remained higher than that of the adjusted clinical model. Simple tumor-burden predictors also did not improve external probability accuracy over the clinical baseline. Conclusions: In these cohorts, measurements from more annotated tumors did not yield more reliable first-year mortality probabilities. Comparisons of clinical-imaging models should evaluate external probability accuracy and calibration alongside patient ranking.
ER -