Incremental Value of Radiomics in 5-Year Overall Survival Prediction for Stage II–III Rectal Cancer

Abstract

Although rectal cancer comprises up to one-third of colorectal cancer cases and several prognosis nomograms have been established for colon cancer, statistical tools for predicting long-term survival in rectal cancer are lacking. In addition, previous prognostic studies did not include much imaging findings, qualitatively or quantitatively. Therefore, we include multiparametric MRI information from both radiologists’ readings and quantitative radiomics signatures to construct a prognostic model that allows 5-year overall survival (OS) prediction for advance-staged rectal cancer patients. The result suggested that the model combined with quantitative imaging findings might outperform that of conventional TNM staging or other clinical prognostic factors. It was noteworthy that the identified radiomics signature consisted of three from dynamic contrast-enhanced (DCE)-MRI, four from anatomical MRI, and one from functional diffusion-weighted imaging (DWI). This highlighted the importance of multiparametric MRI to address the issue of long-term survival estimation in rectal cancer. Additionally, the constructed radiomics signature demonstrated value to the conventional prognostic factors in predicting 5-year OS for stage II–III rectal cancer. The presented nomogram also provides a practical example of individualized prognosis estimation and may potentially impact treatment strategies.

Radiomic biomarkers of locoregional recurrence: prognostic insights from oral cavity squamous cell carcinoma preoperative CT scans

“…Moreover, imaging provides unique 3D information about neoplasm. These radiomics features can be leveraged to develop predictive models for survival and treatment failure (33)(34)(35)(36)(37)(38)(39)(40)(41)(42). The rationale behind this approach is that these images capture crucial information about the neoplasm phenotype and microenvironment (43).…”

Identification of CT-based non-invasive radiomic biomarkers for overall survival prediction in oral cavity squamous cell carcinoma

“…Moreover, imaging provides unique 3D information about the tumor. Studies have shown the clinical utility of CT and PET in predicting treatment outcomes for rectal and lung cancer patients 27 , 28 . The rationale behind this approach is that these images capture crucial information about the tumor phenotype and microenvironment 29 .…”

Identification of CT-based non-invasive Radiographic Biomarkers for Overall Survival Stratification in Oral Cavity Squamous Cell Carcinoma

“…Recent studies have highlighted the potential of medical images in defining genetic mutations related to cancer and utilizing radiomics-based imaging biomarkers for outcome predictions [22][23][24] . Outcome prediction typically involves the use of classical statistical methodologies or Artificial Intelligence (AI) models trained on historical data encompassing genomic, epigenomic, and other omics data.…”

“…The rationale behind this approach is that these images capture crucial information about the tumor phenotype and microenvironment 21 . Recent studies have highlighted the potential of medical images in defining genetic mutations related to cancer and utilizing radiomics-based imaging biomarkers for outcome predictions 22 – 24 Outcome prediction typically involves the use of classical statistical methodologies or Artificial Intelligence (AI) models trained on historical data encompassing genomic, epigenomic, and other omics data. These models aim to uncover patterns or predictors associated with the specific outcome of interest, enabling personalized predictions for individual patients.…”

“…The pervasive use of CT, PET, and MRI biomarkers in cancer research can be attributed to their non-invasive nature and their ability to portray the multifaceted nature of heterogeneous tumors 28 . Studies have shown the clinical utility of CT and PET in predicting treatment outcomes for rectal and lung cancer patients 24 , 29 . Recent investigations also hint at the potential role of PET imaging in identifying cancer-associated genetic mutations 30 and the prospective use of radiomics-based imaging biomarkers for outcome predictions in head and neck cancer 31 .…”