Radiomics approach with deep learning for predicting T4 obstructive colorectal cancer using CT image

Pan, Lin; He, Tian; Huang, Zihan; Chen, Shuai; Zhang, Junrong; Zheng, Shuang; Chen, Xianqiang

doi:10.1007/s00261-023-03838-9

Cited by 3 publications

(1 citation statement)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, labeling a lesion with a rectangle is more efficient and time-saving for physicians responsible for labeling. Pan et al segmented tumors for invasion evaluation by two clinicians with the ROI and the dilated region of interest (dROI) to input the data into a deep learning model and achieved an AUC value of 0.947 in the T4 and non-T4 (NT4) classification [ 30 ]. Huang et al performed tumor segmentation via manually delineating the ROI along the outline of the visible tumor in the largest three continuous cross-sectional images using 3D Slicer software (version 4.3), and the radiomics signature demonstrated a discriminative performance for high-grade and low-grade CRC, with an AUC of 0.812 in the training dataset and 0.735 in the validation dataset [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach

Sahoo,

Gupta,

Lai

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly. In addition, the sensitivity of CT in detecting colorectal cancer is greatly dependent on the size of the tumor. Missed incidental colon cancers using CT are an emerging problem for clinicians and radiologists; consequently, the automatic localization of lesions in the CT images of unprepared bowels is needed. Therefore, this study used artificial intelligence (AI) to localize colorectal cancer in CT images. We enrolled 190 colorectal cancer patients to obtain 1558 tumor slices annotated by radiologists and colorectal surgeons. The tumor sites were double-confirmed via colonoscopy or other related examinations, including physical examination or image study, and the final tumor sites were obtained from the operation records if available. The localization and training models used were RetinaNet, YOLOv3, and YOLOv8. We achieved an F1 score of 0.97 (±0.002), a mAP of 0.984 when performing slice-wise testing, 0.83 (±0.29) sensitivity, 0.97 (±0.01) specificity, and 0.96 (±0.01) accuracy when performing patient-wise testing using our derived model YOLOv8 with hyperparameter tuning.

show abstract

Section: Discussionmentioning

confidence: 99%