Efficacy of HALP combined with T2-weighted MRI radiomics in predicting pathological complete response and overall survival in rectal cancer after neoadjuvant chemoradiotherapy

This study aimed to develop and validate an integrated model combining pretreatment hemoglobin, albumin, lymphocyte, platelet (HALP) score and T2-weighted magnetic resonance imaging radiomics for predicting pathological complete response (pCR) and overall survival in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy. A total of 137 consecutive LARC patients from January 2019 to January 2022 were retrospectively included. Pretreatment 3.0T T2-weighted high-resolution magnetic resonance imaging scans were performed. Four stable features were selected using least absolute shrinkage and selection operator regression. The HALP score was calculated as (hemoglobin × albumin × lymphocytes)/platelets, with an optimal cutoff of 48.4 determined by X-tile analysis. Three predictive models were constructed: a radiomics-only model (RAD), a HALP-only model (HALP), and a combined model (RAD + HALP) using a random forest classifier. Model performance was assessed using the area under the curve (AUC), sensitivity, specificity, calibration curves, and decision curve analysis. The pCR rate was 13.1% (18/137). For pCR prediction, the combined model achieved an AUC of 0.808 (95% confidence interval: 0.73–0.88), compared to 0.788 for the RAD model. The HALP score alone did not show predictive capability ( P = .59). For overall survival prediction, the combined model significantly outperformed the individual models, with an AUC of 0.957 (sensitivity: 0.945, specificity: 1.0; DeLong test, P = .002), the AUC was corrected to 0.852 (sensitivity: 0.788, specificity: 0.889; P < .001). Multivariate Cox analysis identified HALP as an independent prognostic factor (hazard ratio: 5.83, 95% confidence interval: 2.23–15.2). The combined RAD + HALP model demonstrated excellent and internally validated performance in predicting both short-term treatment response and long-term survival in LARC patients undergoing neoadjuvant chemoradiotherapy patients. Prospective multicenter studies are warranted prior to clinical application.

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Journal
Medicine
Published
2026-09-11
DOI
https://doi.org/10.1097/md.0000000000050554
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
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article
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article

Efficacy of HALP combined with T2-weighted MRI radiomics in predicting pathological complete response and overall survival in rectal cancer after neoadjuvant chemoradiotherapy

Xiaoke Di, Caiqiang Zhu, Xiaolin Ge, Xiaolei Wan et al.
Medicine
Inflammatory Biomarkers in Disease Prognosis
article

Efficacy of HALP combined with T2-weighted MRI radiomics in predicting pathological complete response and overall survival in rectal cancer after neoadjuvant chemoradiotherapy

Xiaoke Di, Caiqiang Zhu, Xiaolin Ge, Xiaolei Wan, Dan Mei, Liang Liang, Zhengqi Li, Ning Xue, Xiaojiao Chen, Xinchen Sun, Sheng Zhang
article en

Abstract

This study aimed to develop and validate an integrated model combining pretreatment hemoglobin, albumin, lymphocyte, platelet (HALP) score and T2-weighted magnetic resonance imaging radiomics for predicting pathological complete response (pCR) and overall survival in patients with locally advanced rectal cancer (LARC) undergoing neoadjuvant chemoradiotherapy. A total of 137 consecutive LARC patients from January 2019 to January 2022 were retrospectively included. Pretreatment 3.0T T2-weighted high-resolution magnetic resonance imaging scans were performed. Four stable features were selected using least absolute shrinkage and selection operator regression. The HALP score was calculated as (hemoglobin × albumin × lymphocytes)/platelets, with an optimal cutoff of 48.4 determined by X-tile analysis. Three predictive models were constructed: a radiomics-only model (RAD), a HALP-only model (HALP), and a combined model (RAD + HALP) using a random forest classifier. Model performance was assessed using the area under the curve (AUC), sensitivity, specificity, calibration curves, and decision curve analysis. The pCR rate was 13.1% (18/137). For pCR prediction, the combined model achieved an AUC of 0.808 (95% confidence interval: 0.73–0.88), compared to 0.788 for the RAD model. The HALP score alone did not show predictive capability ( P = .59). For overall survival prediction, the combined model significantly outperformed the individual models, with an AUC of 0.957 (sensitivity: 0.945, specificity: 1.0; DeLong test, P = .002), the AUC was corrected to 0.852 (sensitivity: 0.788, specificity: 0.889; P < .001). Multivariate Cox analysis identified HALP as an independent prognostic factor (hazard ratio: 5.83, 95% confidence interval: 2.23–15.2). The combined RAD + HALP model demonstrated excellent and internally validated performance in predicting both short-term treatment response and long-term survival in LARC patients undergoing neoadjuvant chemoradiotherapy patients. Prospective multicenter studies are warranted prior to clinical application.

MedicineVol. 105(37)
Jiangsu University (CN), Affiliated Hospital of Jiangsu University (CN), Nanjing Medical University (CN)
Openalex Percentile: Top 14%
Inflammatory Biomarkers in Disease Prognosis
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