Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2026
Clinical target volume radiomics from planning CT for pretreatment response prediction in rectal cancer undergoing chemoradiotherapy.
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Huang Yen-Cho, Huang Shih-Ming, Yeh Jih-Hsiang, Tu Shu-Ju
Аннотация
PURPOSE: Response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer remains heterogeneous. This exploratory study evaluated whether radiomics features extracted from the clinical target volume (CTV) on pretreatment non-contrast radiotherapy planning CT could provide additional information for pretreatment prediction of poor treatment response.
METHODS: This retrospective single-center study included 60 patients with rectal cancer treated with nCRT between 2008 and 2024, including 50 good responders and 10 poor responders. A total of 1148 radiomics features were extracted from the original pretreatment treatment-planning CTV and combined with clinical variables, including age, sex, cT stage, cN stage, log-transformed carcinoembryonic antigen, and tumor-to-anal verge distance. Models were internally evaluated using repeated nested cross-validation with fold-wise ComBat harmonization. Performance was assessed from pooled repeated out-of-fold predictions, including discrimination, apparent calibration, exploratory threshold-based operating characteristics, and decision curve analysis.
RESULTS: The best-performing combined clinical-radiomics model was Extra Trees, with an area under the receiver operating characteristic curve of 0.754 (95% confidence interval [CI], 0.713-0.789), compared with 0.507 for the clinical-only model. At a post hoc sensitivity-prioritized operating threshold, the combined Extra Trees model achieved sensitivity of 0.94 (95% CI, 0.89-0.98), specificity of 0.47 (95% CI, 0.42-0.51), and negative predictive value of 0.98 in pooled repeated out-of-fold analysis. Decision curve analysis suggested potential net benefit across low-to-moderate threshold probabilities in internal analysis.
CONCLUSION: Clinical target volume-based radiomics from routine pretreatment non-contrast planning CT may provide exploratory information for prediction of poor response to nCRT in rectal cancer. Because treatment-planning CTVs are routinely generated before radiotherapy, this workflow may be practical for radiotherapy-based research. The findings are hypothesis generating and require external validation before clinical use.
METHODS: This retrospective single-center study included 60 patients with rectal cancer treated with nCRT between 2008 and 2024, including 50 good responders and 10 poor responders. A total of 1148 radiomics features were extracted from the original pretreatment treatment-planning CTV and combined with clinical variables, including age, sex, cT stage, cN stage, log-transformed carcinoembryonic antigen, and tumor-to-anal verge distance. Models were internally evaluated using repeated nested cross-validation with fold-wise ComBat harmonization. Performance was assessed from pooled repeated out-of-fold predictions, including discrimination, apparent calibration, exploratory threshold-based operating characteristics, and decision curve analysis.
RESULTS: The best-performing combined clinical-radiomics model was Extra Trees, with an area under the receiver operating characteristic curve of 0.754 (95% confidence interval [CI], 0.713-0.789), compared with 0.507 for the clinical-only model. At a post hoc sensitivity-prioritized operating threshold, the combined Extra Trees model achieved sensitivity of 0.94 (95% CI, 0.89-0.98), specificity of 0.47 (95% CI, 0.42-0.51), and negative predictive value of 0.98 in pooled repeated out-of-fold analysis. Decision curve analysis suggested potential net benefit across low-to-moderate threshold probabilities in internal analysis.
CONCLUSION: Clinical target volume-based radiomics from routine pretreatment non-contrast planning CT may provide exploratory information for prediction of poor response to nCRT in rectal cancer. Because treatment-planning CTVs are routinely generated before radiotherapy, this workflow may be practical for radiotherapy-based research. The findings are hypothesis generating and require external validation before clinical use.