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Background <p>Gallbladder carcinoma (GBC) is an aggressive malignancy with poor prognosis, often diagnosed at advanced stages. While chemotherapy forms the cornerstone of treatment for locally advanced and metastatic disease, reliable predictors of response remain elusive. This exploratory pilot study aimed to develop a computed tomography (CT)-based prediction model incorporating clinical and biochemical parameters to identify patients likely to respond to chemotherapy.</p> Methods <p>This prospective study enrolled consecutive treatment-naïve patients with cytologically/histologically proven locally advanced or metastatic GBC scheduled for chemotherapy between July 2023 and March 2025. All patients underwent biphasic contrast-enhanced CT within 14 days prior to chemotherapy initiation. Demographic variables, biochemical parameters, and tumor markers (CA 19 − 9, CEA) were systematically recorded. CT-based response assessment was performed using RECIST 1.1 criteria after two cycles of chemotherapy. Three exploratory prediction models were developed using logistic regression with appropriate variable selection methods and penalized regression to address small sample size.</p> Results <p>Of 25 patients (18 females, mean age 50.0 ± 11.6), 11 (44%) achieved partial response while 14 (56%) were non-responders (stable or progressive disease). Univariate analysis identified calculus presence (<i>p</i> = 0.002), largest liver metastasis size (<i>p</i> = 0.001), ascites (<i>p</i> = 0.008), age (<i>p</i> = 0.128), ALT (<i>p</i> = 0.241), and CEA levels (<i>p</i> = 0.123) as potential predictors. After correction for optimism using bootstrap validation, the CT-only model achieved area under the receiver operating characteristic curve (AUC) of 0.82 (95% CI: 0.64–0.92) with 80.0% accuracy, 92.3% sensitivity, and 62.5% specificity (Nagelkerke R²=0.272). The clinical-only model yielded optimism-corrected AUC of 0.74 (95% CI: 0.51–0.88) with 68.0% accuracy, 71.4% sensitivity, and 63.6% specificity (Nagelkerke R²=0.424). The combined model showed better performance with optimism-corrected AUC of 0.85 (95% CI: 0.67–0.95), 84.0% accuracy, 85.7% sensitivity, 81.8% specificity, and highest explained variance (Nagelkerke R²=0.662). Gallstone presence emerged as a potential predictor associated with treatment response across models, while larger liver lesions and elevated CEA showed trends that may be associated with poor response.</p> Conclusions <p>This preliminary hypothesis-generating study suggests that integration of baseline CT imaging features with clinical parameters may potentially allow prediction of chemotherapy response in GBC patients. The identified factors require external validation in larger multi-institutional cohorts before clinical implementation can be considered.</p>

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CT-based multimodal prediction model for chemotherapy response in gallbladder cancer: integration of imaging and clinical biomarkers—a prospective pilot study

  • 期刊论文

  • 发表日期：

    2025年10月6日

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  • Kritika Sharma,
  • Pankaj Gupta,
  • Ajay Gulati,
  • Niharika Dutta,
  • Divya Khosla,
  • Gaurav Prakash,
  • Thakur Yadav,
  • Lileswar Kaman,
  • Santosh Irrinki,
  • Harjeet Singh,
  • Madhurima Sharma,
  • Parikshaa Gupta,
  • Aravind Sekhar,
  • Rakesh Kapoor,
  • Rajesh Gupta,
  • Usha Dutta

摘要

Background

Gallbladder carcinoma (GBC) is an aggressive malignancy with poor prognosis, often diagnosed at advanced stages. While chemotherapy forms the cornerstone of treatment for locally advanced and metastatic disease, reliable predictors of response remain elusive. This exploratory pilot study aimed to develop a computed tomography (CT)-based prediction model incorporating clinical and biochemical parameters to identify patients likely to respond to chemotherapy.

Methods

This prospective study enrolled consecutive treatment-naïve patients with cytologically/histologically proven locally advanced or metastatic GBC scheduled for chemotherapy between July 2023 and March 2025. All patients underwent biphasic contrast-enhanced CT within 14 days prior to chemotherapy initiation. Demographic variables, biochemical parameters, and tumor markers (CA 19 − 9, CEA) were systematically recorded. CT-based response assessment was performed using RECIST 1.1 criteria after two cycles of chemotherapy. Three exploratory prediction models were developed using logistic regression with appropriate variable selection methods and penalized regression to address small sample size.

Results

Of 25 patients (18 females, mean age 50.0 ± 11.6), 11 (44%) achieved partial response while 14 (56%) were non-responders (stable or progressive disease). Univariate analysis identified calculus presence (p = 0.002), largest liver metastasis size (p = 0.001), ascites (p = 0.008), age (p = 0.128), ALT (p = 0.241), and CEA levels (p = 0.123) as potential predictors. After correction for optimism using bootstrap validation, the CT-only model achieved area under the receiver operating characteristic curve (AUC) of 0.82 (95% CI: 0.64–0.92) with 80.0% accuracy, 92.3% sensitivity, and 62.5% specificity (Nagelkerke R²=0.272). The clinical-only model yielded optimism-corrected AUC of 0.74 (95% CI: 0.51–0.88) with 68.0% accuracy, 71.4% sensitivity, and 63.6% specificity (Nagelkerke R²=0.424). The combined model showed better performance with optimism-corrected AUC of 0.85 (95% CI: 0.67–0.95), 84.0% accuracy, 85.7% sensitivity, 81.8% specificity, and highest explained variance (Nagelkerke R²=0.662). Gallstone presence emerged as a potential predictor associated with treatment response across models, while larger liver lesions and elevated CEA showed trends that may be associated with poor response.

Conclusions

This preliminary hypothesis-generating study suggests that integration of baseline CT imaging features with clinical parameters may potentially allow prediction of chemotherapy response in GBC patients. The identified factors require external validation in larger multi-institutional cohorts before clinical implementation can be considered.