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Development and Cross-Institutional Validation of a Comprehensive Machine Learning Model Predicting Response to

Sha Li1,2, Zhengxian Li3, Shuai Li2

  • 1Institute of Medical Technology, Peking University Health Science Center, Peking University, Beijing, People's Republic of China.

Cancer Management and Research
|August 11, 2025
PubMed
Summary
This summary is machine-generated.

Predicting pathological complete response (pCR) in locally advanced rectal cancer (LARC) is vital. A new multi-omics model integrating clinical data, radiomics, and dosiomics accurately identifies patients likely to achieve pCR after neoadjuvant chemoradiotherapy (nCRT).

Keywords:
dosiomicsnCRTpredict therapy responseradiomicsrectal cancer

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Area of Science:

  • Oncology
  • Radiology
  • Medical Informatics

Background:

  • Accurate prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) for locally advanced rectal cancer (LARC) is crucial for treatment optimization and avoiding unnecessary surgery.
  • Current predictive methods may not fully leverage the potential of multi-modal data.

Purpose of the Study:

  • To develop and validate a comprehensive multi-omics model for predicting pCR in LARC patients before surgery.
  • To evaluate the individual and combined contributions of clinical data, radiomics, and dosiomics in predicting treatment response.

Main Methods:

  • Collected clinical data, CT, MRI-T1WI, MRI-T2WI, and radiotherapy dose from 183 LARC patients.
  • Developed non-imaging, radiomics-based (CT, T1WI, T2WI), and dosiomics-based models using backward stepwise selection, logistic regression, and cross-validation.
  • Integrated individual models into a final comprehensive model (F_model) and validated it on multi-center datasets.

Main Results:

  • The clinical model (C_model) achieved an AUC of 0.85.
  • Radiomics models showed AUCs ranging from 0.64 to 0.67.
  • The dosiomics model (D_model) achieved an AUC of 0.75.
  • The final multi-omics model (F_model) demonstrated high performance with mean AUCs of 0.90 (training), 0.88 (validation), 0.77 (internal test), and 0.74 (external test).

Conclusions:

  • Clinical characteristics and dosiomics are key factors in assessing nCRT efficacy for LARC.
  • While radiomics models (CT, T1WI, T2WI) showed comparable performance, they each offer unique predictive value.
  • The comprehensive multi-omics model provides a robust approach for predicting pCR, aiding in personalized treatment strategies.