In this review of rectal MRI techniques and rectal anatomy, the authors describe current concepts in rectal cancer management; demonstrate the role of rectal MRI in local staging, restaging, and recurrence; and discuss future directions in imaging research. Rectal cancer is prone to local recurrence and systemic metastasis. However, owing to improvements in TNM staging and treatment, including a more widespread use of rectal MRI and increased radiologist awareness of the key rectal cancer TNM staging features, the mortality rate of rectal cancer has been declining over the past few decades in adults over 50 years of age. Currently, rectal MRI plays a key role in the pre- and posttreatment evaluation of rectal cancer, assisting the multidisciplinary team in tailoring the most appropriate treatment option. The benefits achieved with rectal MRI are strictly dependent on obtaining good-quality images, which is important for the characterization of the main anatomic structures and their relationship with the tumor. In primary staging, rectal MRI helps the radiologist (a) describe the tumor location and morphology, (b) provide its T and N categories, (c) detect the presence of extramural vascular invasion, and (d) identify its relationship with surrounding structu...

The radiomics measures by using cross-validation showed better classification performance compared with the qualitative assessment for diagnosing pathologic complete response in patients with locally advanced rectal cancer after neoadjuvant therapy. Purpose To investigate the value of T2-weighted–based radiomics compared with qualitative assessment at T2-weighted imaging and diffusion-weighted (DW) imaging for diagnosis of clinical complete response in patients with rectal cancer after neoadjuvant chemotherapy–radiation therapy (CRT). Materials and Methods This retrospective study included 114 patients with rectal cancer who underwent magnetic resonance (MR) imaging after CRT between March 2012 and February 2016. Median age among women (47 of 114, 41%) was 55.9 years (interquartile range, 45.4–66.7 years) and median age among men (67 of 114, 59%) was 55 years (interquartile range, 48–67 years). Surgical histopathologic analysis was the reference standard for pathologic complete response (pCR). For qualitative assessment, two radiologists reached a consensus. For radiomics, one radiologist segmented the volume of interest on high-spatial-resolution T2-weighted images. A random forest classifier was trained to separate the patients by their outcomes af...

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Radiomics is a promising and fast-developing field within oncology that involves the mining of quantitative highdimensional data from medical images. Radiomics has the potential to transform cancer management, whereby radiomics data can be used to aid early tumor characterization, prognosis, risk stratification, treatment planning, treatment response assessment, and surveillance, etc. Nevertheless, certain challenges have delayed the clinical adoption and acceptability of radiomics in routine clinical practice. The objectives of this report are: (a) to provide a perspective on the translational potential and potential impact of radiomics in oncology; (b) to explore frequent challenges and mistakes in its derivation, encompassing study design, technical requirements, standardization, model reproducibility, transparency, data sharing, privacy concerns, quality control, as well as the complexity of multistep processes resulting in less radiologist-friendly interfaces; (c) to discuss strategies to overcome these challenges and mistakes; and (d) to propose measures to increase the clinical use and acceptability of radiomics, taking into account the different perspectives of patients, health care workers, and health care systems. ©RSNA, 2024.

The integration of artificial intelligence (AI) in medical imaging has revolutionized diagnostic capabilities, yet the black-box nature of deep learning models poses significant challenges for clinical adoption. Current explainable AI (XAI) approaches, including SHAP, LIME, and Grad-CAM, predominantly focus on post hoc explanations that may inadvertently undermine clinical decision-making by providing misleading confidence in AI outputs. This paper presents a systematic review and meta-analysis of 67 studies (covering 23 radiology, 19 pathology, and 25 ophthalmology applications) evaluating XAI fidelity, stability, and performance trade-offs across medical imaging modalities. Our meta-analysis of 847 initially identified studies reveals that LIME achieves superior fidelity (0.81, 95% CI: 0.78–0.84) compared to SHAP (0.38, 95% CI: 0.35–0.41) and Grad-CAM (0.54, 95% CI: 0.51–0.57) across all modalities. Post hoc explanations demonstrated poor stability under noise perturbation, with SHAP showing 53% degradation in ophthalmology applications (ρ = 0.42 at 10% noise) compared to 11% in radiology (ρ = 0.89). We demonstrate a consistent 5–7% AUC performance penalty for interpretable models but identify modality-specific stability patterns suggesting that tailored XAI approaches are necessary. Based on these empirical findings, we propose a comprehensive three-pillar accountability framework that prioritizes transparency in model development, interpretability in architecture design, and a cautious deployment of post hoc explanations with explicit uncertainty quantification. This approach offers a pathway toward genuinely accountable AI systems that enhance rather than compromise clinical decision-making quality and patient safety.