Development and validation of a radiopathomics model to predict pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a multicentre observational study

Abstract

Background Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides. MethodsIn this multicentre observational study, eligible participants who had undergone neoadjuvant chemoradiotherapy followed by radical surgery were recruited, with their pretreatment pelvic MRI (T2-weighted imaging, contrast-enhanced T1-weighted imaging, and diffusion-weighted imaging) and whole slide images of H&E-stained biopsy sections collected for annotation and feature extraction. The RAdioPathomics Integrated preDiction System (RAPIDS) was constructed by machine learning on the basis of three feature sets associated with pathological complete response: radiomics MRI features, pathomics nucleus features, and pathomics microenvironment features from a retrospective training cohort. The accuracy of RAPIDS for the prediction of pathological complete response in locally advanced rectal cancer was verified in two retrospective external validation cohorts and further validated in a multicentre, prospective observational study (ClinicalTrials.gov, NCT04271657). Model performances were evaluated using area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Machine learning models predict overall survival and progression free survival of non-surgical esophageal cancer patients with chemoradiotherapy based on CT image radiomics signatures

“…Li et al [27] used deep learning to predict the treatment response to CCRT for ESCC patients and the M stage was also involved in the progression of model development. Due to the heterogeneity of tumor, the treatment outcomes of patients might be different even with the same clinical features [11,35,36].…”

“…Even though there are different treatment strategies, the recurrence or metastasis are still the main factors that affects the prognosis and patient's survival [7]. Currently, the methods to predict prognosis of ESCC patients are mostly based on clinic risk factors, pathology and image, such as the patients characteristics like age, gender, treatment response, the tumor characteristics like location, size, differential, TNM stage et al, the pathology characteristics like lymphovascular invasion, the hematology test results like leukocyte, platelet [8][9][10][11][12].…”

Review of Radiomics- and Dosiomics-based Predicting Models for Rectal Cancer

“…Whether a patient has achieved pCR is often determined by postoperative pathological examination, and it is not known whether it is remission before surgery. Many recent studies have found that radiomics can help clinicians predict whether patients will be pCR after NCRT before surgery to avoid excessive treatment and the burden and pain caused by surgery and improve treatment accuracy (61)(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73). CT-based radiomics has shown promise in LARC.…”

Deep-learning-based 3D super-resolution MRI radiomics model: superior predictive performance in preoperative T-staging of rectal cancer

“…Previous studies have suggested its potential to improve patient management and clinical decision-making by uncovering disease characteristics that may be invisible to human eyes [18,19]. In RC, radiomics has attained impressive performance in different oncological scenarios, including evaluating tumor biological behaviors [20], assessing treatment response [21,22], and predicting prognosis [23]. Despite these advances, radiomics features tend to be affected by anisotropic resolution and low voxel statistics in current medical imaging [24].…”