RadioPathomics Artificial Intelligence Model to Predict nCRT Response in Locally Advanced Rectal Cancer
Condition: Rectal Cancer
MeSH terms: Rectal Neoplasms
Study type: Observational

Summary: 
      In this study, investigators utilize a radiopathomics integrated Artificial Intelligence (AI)
      supportive system to predict tumor response to neoadjuvant chemoradiotherapy (nCRT) before
      its administration for patients with locally advanced rectal cancer (LARC). By the system,
      whether the participants achieve the pathologic complete response (pCR) will be identified
      based on the radiopathomics features extracted from the pre-nCRT Magnetic Resonance Imaging
      (MRI) and biopsy images. The predictive power to discriminate the pCR individuals from
      non-pCR patients, will be validated in this multicenter, prospective clinical study.
    

Detailed description: 
      This is a multicenter, prospective, observational clinical study for validation of a
      radiopathomics artificial intelligence (AI) system. Patients who have been pathologically
      diagnosed as rectal adenocarcinoma and defined as clinical II-III staging without distant
      metastasis by enhanced Magnetic Resonance Imaging (MRI) will be enrolled from the Sixth
      Affiliated Hospital of Sun Yat-sen University, the Third Affiliated Hospital of Kunming
      Medical College and Sir Run Run Shaw Hospital Affiliated by Zhejiang University School of
      Medicine. All participants should follow a very standard treatment protocol, including of
      concurrent neoadjuvant chemoradiotherapy (nCRT), total mesorectum excision (TME) surgery and
      adjuvant chemotherapy. The MRI and biopsy examination should be completed before the nCRT and
      the images will be subjected to the manual delineation of the tumor regions of interest (ROI)
      by experienced radiologists and pathologists. Subsequently, the outlined MRI and biopsy
      slides images will be employed to the radiopathomics AI system to generate the predicted
      response ("predicted pathologic complete response (pCR)" vs. "predicted non-pCR") of
      individual patient, whereas the actual response ("pathologic confirmed as pCR" vs.
      "pathologic confirmed as non-pCR") will be diagnosed at surgery excised specimen. Through
      comparisons of the predicted responses and true pathologic responses, investigators calculate
      the prediction accuracy, specificity, sensitivity as well as the Area Under Curve (AUC) of
      Receiver Operating Characteristic (ROC) curves. This study is aimed to validate the high
      accuracy and robustness of the radiopathomics AI system for identifying pCR candidates from
      non-pCR individuals before nCRT which will facilitate further precision therapy for patients
      with locally advanced rectal cancer.
    

Observational model: Other
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        The population in the study are the patients with LARC, who are intended to receive or
        undergoing standard, neoadjuvant concurrent chemoradiotherapy with tumor pathologic
        response unknown.
      
Keywords: Radiopathomics features; Artificial intelligence; Locally advanced rectal cancer; Pathologic complete response; Neoadjuvant chemoradiotherapy