Ke Cao

Purpose: While changes in weight are often observed in patients with colorectal cancer (CRC), they provide limited information regarding how or if body composition changes might occur. We used artificial intelligence (AI) to analyse computed tomography (CT) images from patients’ lumbar region to investigate how body composition differs across clinical disease stages of CRC. Methods: A retrospective analysis was performed on patients diagnosed with CRC and treated at Western Health, Melbourne (2013–2023). Three‐dimensional measures of skeletal muscle (SM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT) volumes and radiodensities were automatically segmented from staging CT scans (L1–L5) using a pretrained AI model. Disease stage was determined using the Australian Clinicopathological Staging (ACPS) system. Body composition values were adjusted for physiological variation (age, sex and height) and compared across ACPS stages. Results: A total of 1138 individuals were evaluated (68.2 ± 13.0 years; 59.8% male), with 24% classified as Stage A, 30.5% as Stage B, 17.5% as Stage C, and 28% as Stage D. No significant differences were observed between Stages B and C, and these groups were therefore combined (Stage B/C) for further analysis. VAT volume was significantly lower in Stage B/C versus Stage A ( p = 0.004) and declined further in Stage D ( p = 0.003). In contrast, SM and SAT volumes were significantly lower only in Stage D compared with earlier stages. Radiodensity measures showed that SAT and VAT radiodensity increased progressively from Stage A to Stage D, whereas SM radiodensity increased only in Stage D. Conclusion: CRC patients were shown to exhibit distinct body composition changes at diagnosis depending on disease stage. Adipose tissue loss and density changes occurred earlier than muscle changes. Longitudinal studies are warranted to understand these trajectories but suggest that nutritional and supportive interventions could be targeted based on CRC stage at diagnosis.

Background: Perineal wound complications following abdominoperineal resection (APR) significantly impacts patient morbidity. Despite various closure techniques, no method has proven superior. Body composition is a key factor influencing postoperative outcomes. AI‐assisted CT scan analysis is an accurate and efficient approach to assessing body composition. This study aimed to evaluate whether body composition characteristics can predict perineal wound complications following APR. Methods: A retrospective cohort study of APR patients from 2012 to 2024 was conducted, comparing primary closure and inferior gluteal artery myocutaneous (IGAM) flap closure outcomes. Preoperative CT scans were analyzed using a validated AI model to measure lumbosacral skeletal muscle (SM), intramuscular adipose tissue (IMAT), visceral adipose tissue, and subcutaneous adipose tissue. Results: Greater IMAT volume correlated with increased wound dehiscence in males undergoing IGAM closure (40% vs. 4.8% and p = 0.027). Lower SM‐to‐IMAT volume ratio was associated with higher wound infection rates (60% vs. 19% and p = 0.04). Closure technique did not significantly impact wound infection or dehiscence rates. Conclusion: This study is the first to use AI derived 3D body composition analysis to assess perineal wound complications after APR. IMAT volume significantly influences wound healing in male patients having IGAM reconstruction.

Introduction: This study aimed to evaluate the accuracy of our own artificial intelligence (AI)‐generated model to assess automated segmentation and quantification of body composition‐derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.Methods: A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012–2019 at a single Australian tertiary institution, Western Health in Melbourne. A two‐dimensional U‐Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U‐Net‐based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross‐sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods.Results: The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944–0.999, P < 0.001).Conclusions: Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.

(1) Background: The objective of this review was to synthesize available data on the use of machine learning to evaluate its accuracy (as determined by pooled sensitivity and specificity) in detecting keratoconus (KC), and measure reporting completeness of machine learning models in KC based on TRIPOD (the transparent reporting of multivariable prediction models for individual prognosis or diagnosis) statement. (2) Methods: Two independent reviewers searched the electronic databases for all potential articles on machine learning and KC published prior to 2021. The TRIPOD 29-item checklist was used to evaluate the adherence to reporting guidelines of the studies, and the adherence rate to each item was computed. We conducted a meta-analysis to determine the pooled sensitivity and specificity of machine learning models for detecting KC. (3) Results: Thirty-five studies were included in this review. Thirty studies evaluated machine learning models for detecting KC eyes from controls and 14 studies evaluated machine learning models for detecting early KC eyes from controls. The pooled sensitivity for detecting KC was 0.970 (95% CI 0.949–0.982), with a pooled specificity of 0.985 (95% CI 0.971–0.993), whereas the pooled sensitivity of detecting early KC was 0.882 (95% CI 0.822–0.923), with a pooled specificity of 0.947 (95% CI 0.914–0.967). Between 3% and 48% of TRIPOD items were adhered to in studies, and the average (median) adherence rate for a single TRIPOD item was 23% across all studies. (4) Conclusions: Application of machine learning model has the potential to make the diagnosis and monitoring of KC more efficient, resulting in reduced vision loss to the patients. This review provides current information on the machine learning models that have been developed for detecting KC and early KC. Presently, the machine learning models performed poorly in identifying early KC from control eyes and many of these research studies did not follow established reporting standards, thus resulting in the failure of these clinical translation of these machine learning models. We present possible approaches for future studies for improvement in studies related to both KC and early KC models to more efficiently and widely utilize machine learning models for diagnostic process.