Oncological Outcomes and Response Rate After Total Neoadjuvant Therapy for Locally Advanced Rectal Cancer: A Network Meta-Analysis Comparing Induction vs. Consolidation Chemotherapy vs. Standard Chemoradiation.

TNT is now considered the preferred option for stage II-III locally advanced rectal cancer (LARC). However, the prognostic benefit and optimal sequence of TNT remains unclear. This network meta-analysis (NMA) compared short- and long-term outcomes amongst patients with LARC receiving total neoadjuvant therapy (TNT) as induction (iTNT) or consolidation chemotherapy (cTNT) with those receiving neoadjuvant chemoradiation (nCRT) alone. A systematic literature search was performed between 2012 and 2023. A Bayesian NMA was conducted using a Markov Chain Monte Carlo method with a random-effects model and vague prior distribution to calculate odds ratios (OR) with 95% credible intervals (CrI). The surface under the cumulative ranking (SUCRA) curves were used to rank treatment(s) for each outcome. In total, 11 cohorts involving 8360 patients with LARC were included. There was no significant difference in disease-free survival (DFS) and overall survival (OS) amongst the 3 treatments. Compared with nCRT, both cTNT (OR 2.36; 95% CrI, 1.57-3.66) and iTNT (OR 1.99; 95% CrI, 1.44-2.95) significantly improved complete response (CR) rate. Notably, cTNT ranked as the best treatment for CR (SUCRA 0.90) and iTNT as the best treatment for 3-year DFS and OS (SUCRA 0.72 and 0.87, respectively). Both iTNT and cTNT strategies significantly improved CR rates compared with nCRT. cTNT was ranked highest for CR rates, while iTNT was ranked highest for 3-year survival outcomes. However, no other significant differences in DFS, OS, sphincter-saving surgery, R0 resection and postoperative complications were found amongst the treatment groups.

Deep learning to predict lymph node status on pre-operative staging CT in patients with colon cancer.

INTRODUCTION: Lymph node (LN) metastases are an important determinant of survival in patients with colon cancer, but remain difficult to accurately diagnose on preoperative imaging. This study aimed to develop and evaluate a deep learning model to predict LN status on preoperative staging CT. METHODS: In this ambispective diagnostic study, a deep learning model using a ResNet-50 framework was developed to predict LN status based on preoperative staging CT. Patients with a preoperative staging abdominopelvic CT who underwent surgical resection for colon cancer were enrolled. Data were retrospectively collected from February 2007 to October 2019 and randomly separated into training, validation, and testing cohort 1. To prospectively test the deep learning model, data for testing cohort 2 was collected from October 2019 to July 2021. Diagnostic performance measures were assessed by the AUROC. RESULTS: A total of 1,201 patients (median [range] age, 72 [28-98 years]; 653 [54.4%] male) fulfilled the eligibility criteria and were included in the training (n = 401), validation (n = 100), testing cohort 1 (n = 500) and testing cohort 2 (n = 200). The deep learning model achieved an AUROC of 0.619 (95% CI 0.507-0.731) in the validation cohort. In testing cohort 1 and testing cohort 2, the AUROC was 0.542 (95% CI 0.489-0.595) and 0.486 (95% CI 0.403-0.568), respectively. CONCLUSION: A deep learning model based on a ResNet-50 framework does not predict LN status on preoperative staging CT in patients with colon cancer.

The Impact of Obesity on Inflammatory Bowel Disease.

Obesity is prevalent in the inflammatory bowel disease (IBD) population, particularly in newly developed countries where both IBD and obesity in the general population are on the rise. The role of obesity in the pathogenesis of IBD was entertained but results from available studies are conflicting. It does, however, appear to negatively influence disease course whilst impacting on our medical and surgical therapies. The pro-inflammatory profile of the visceral adipose tissue might play a role in the pathogenesis and course of Crohn's Disease (CD). Interestingly, isolating the mesentery from the surgical anastomosis using a KONO-S technique significantly decreases anastomotic recurrence rate. Anti-obesity therapy is not widely used in IBD but was suggested as an adjunctive therapy in those patients. In this review, we aimed to highlight the epidemiology of obesity in IBD and to describe its influence on disease course and outcomes.

A prospective study of diagnostic accuracy of multidisciplinary team and radiology reporting of preoperative colorectal cancer local staging.

INTRODUCTION: The aim of this study was to correlate and assess diagnostic accuracy of preoperative staging at multidisciplinary team meeting (MDT) against the original radiology reports and pathological staging in colorectal cancer patients. METHODS: A prospective observational study was conducted at two institutions. Patients with histologically proven colorectal cancer and available preoperative imaging were included. Preoperative tumor and nodal staging (cT and cN) as determined by the MDT and the radiology report (computed tomography [CT] and/or magnetic resonance imaging [MRI]) were recorded. Kappa statistics were used to assess agreement between MDT and the radiology report for cN staging in colon cancer, cT and cN in rectal cancer, and tumor regression grade (TRG) in patients with rectal cancer who received neoadjuvant therapy. Pathological report after surgery served as the reference standard for local staging, and AUROC curves were constructed to compare diagnostic accuracy of the MDT and radiology report. RESULTS: A total of 481 patients were included. Agreement between MDT and radiology report for cN stage was good in colon cancer (k = .756, Confidence Interval (CI) 95% .686-.826). Agreement for cT and cN and in rectal cancer was very good (kw = .825, CI 95% .758-.892) and good (kw = .792, CI 95% .709-.875), respectively. In the rectal cancer group that received neoadjuvant therapy, agreement on TRG was very good (kw = .919, CI 95% .846-.993). AUROC curves using pathological staging indicated no difference in diagnostic accuracy between MDT and radiology reports for either colon or rectal cancer. CONCLUSION: Preoperative colorectal cancer local staging was consistent between specialist MDT review and original radiology reports, with no significant differences in diagnostic accuracy identified.

Outcomes of open vs laparoscopic vs robotic vs transanal total mesorectal excision (TME) for rectal cancer: a network meta-analysis.

BACKGROUND: Total mesorectal excision (TME) for rectal cancer can be achieved using open (OpTME), laparoscopic (LapTME), robotic (RoTME), or transanal techniques (TaTME). However, the optimal approach for access remains controversial. The aim of this network meta-analysis was to assess operative and oncological outcomes of all four surgical techniques. METHODS: Ovid MEDLINE, EMBASE, and PubMed databases were searched systematically from inception to September 2020, for randomised controlled trials (RCTs) comparing any two TME surgical techniques. A network meta-analysis using a Bayesian random-effects framework and mixed treatment comparison was performed. Primary outcomes were the rate of clear circumferential resection margin (CRM), defined as > 1 mm from the closest tumour to the cut edge of the tissue, and completeness of mesorectal excision. Secondary outcomes included radial and distal resection margin distance, postoperative complications, locoregional recurrence, disease-free survival, and overall survival. Surface under cumulative ranking (SUCRA) was used to rank the relative effectiveness of each intervention for each outcome. The higher the SUCRA value, the higher the likelihood that the intervention is in the top rank or one of the top ranks. RESULTS: Thirty-two RCTs with a total of 6151 patients were included. Compared with OpTME, there was no difference in the rates of clear CRM: LapTME RR = 0.99 (95% (Credible interval) CrI 0.97-1.0); RoTME RR = 1.0 (95% CrI 0.96-1.1); TaTME RR = 1.0 (95% CrI 0.96-1.1). There was no difference in the rates of complete mesorectal excision: LapTME RR = 0.98 (95% CrI 0.98-1.1); RoTME RR = 1.1 (95% CrI 0.98-1.4); TaTME RR = 1.0 (95% CrI 0.91-1.2). RoTME was associated with improved distal resection margin distance compared to other techniques (SUCRA 99%). LapTME had a higher rate of conversion to open surgery when compared with RoTME: RoTME RR = 0.23 (95% CrI 0.034-0.70). Length of stay was shortest in RoTME compared to other surgical approaches: OpTME mean difference in days (MD) 3.3 (95% CrI 0.12-6.0); LapTME MD 1.7 (95% CrI - 1.1-4.4); TaTME MD 1.3 (95% CrI - 5.2-7.4). There were no differences in 5-year overall survival (LapTME HR 1.1, 95% CrI 0.74, 1.4; TaTME HR 1.7, 95% CrI 0.79, 3.4), disease-free survival rates (LapTME HR 1.1, 95% CrI 0.76, 1.4; TaTME HR 1.1, 95% CrI 0.52, 2.4), or anastomotic leakage (LapTME RR = 0.92 (95% CrI 0.63, 1.1); RoTME RR = 1.0 (95% CrI 0.48, 1.8); TaTME RR = 0.53 (95% CrI 0.19, 1.2). The overall quality of evidence as per Grading of Recommendations Assessment, Development and Evaluation (GRADE) assessments across all outcomes including primary and secondary outcomes was deemed low. CONCLUSIONS: In selected patients eligible for a RCT, RoTME achieved improved distal resection margin distance and a shorter length of hospital stay. No other differences were observed in oncological or recovery parameters between (OpTME), laparoscopic (LapTME), robotic (RoTME), or trans-anal TME (TaTME). However, the overall quality of evidence across all outcomes was deemed low.

Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: A systematic review and meta-analysis.

PURPOSE: Tracing muscle groups manually on CT to calculate body composition parameters and diagnose sarcopenia is costly and time consuming. Artificial Intelligence (AI) provides an opportunity to automate this process. In this systematic review, we aimed to assess the performance of CT-based AI segmentation models used for body composition analysis. METHOD: We systematically searched PubMed (MEDLINE), Embase, Web of Science and Scopus for studies published from January 1, 2011, to May 27, 2021. Studies using AI models for assessment of body composition and sarcopenia on CT scans were included. Excluded were studies that used muscle strength, physical performance data, DXA and MRI. Meta-analysis was conducted on the reported dice similarity coefficient (DSC) and Jaccard similarity coefficient (JSC) of AI models. RESULTS: 284 studies were identified, of which 24 could be included in the systematic review. Among them, 15 were included in the meta-analysis, all of which used deep learning. Deep learning models for skeletal muscle (SM) segmentation performed with a pooled DSC of 0.941 (95 %CI 0.923-0.959) and a pooled JSC of 0.967 (95 %CI 0.949-0.986). Additionally, a pooled DSC of 0.967 (95 %CI 0.958-0.978), 0.963 (95 %CI 0.957-0.969) and 0.970 (95 %CI 0.944-0.996) was observed for segmentation of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and bone, respectively. SM studies suffered from significant publication bias, and heterogeneity among the included studies was considerable. CONCLUSIONS: CT-based deep learning models can facilitate the automated segmentation of body composition and aid in sarcopenia diagnosis. More rigorous guidelines and comparative studies are required to assess the efficacy of AI segmentation models before incorporating these into clinical practice.

Assessing effectiveness and patient perceptions of a novel electronic medical record for the management of inflammatory bowel disease.

BACKGROUND AND AIM: There is an increasing prevalence of chronic disease worldwide, resulting in multiple management challenges. Inflammatory bowel disease (IBD) is an exemplar chronic disease requiring coordinated longitudinal care. We propose that Crohn's Colitis Care (CCCare), a novel IBD-specific, structured electronic medical record is effective at improving data capture and is acceptable to patients. METHODS: A comparison was made between IBD-data completeness in usual records and CCCare. CCCare's acceptability to patients was assessed in two independent IBD patient cohorts and included:• Overall ratings of acceptability.• Factors associated with pre-exposure acceptability ratings.• Whether exposure and security concerns influenced acceptability ratings.• Direct patient feedback through CCCare's patient portal. RESULTS: In all cases reviewed, there was data gain using structured CCCare fields compared with IBD documentation in usual medical records. The overall acceptability in the combined cohort (n = 310) was very high. More than three-quarters of patients rated acceptability as >7 of 10. Self-reported information technology (IT) literacy positively associated with acceptability. Exposure had a small positive affect on acceptability, whereas security concerns had little impact on acceptability. Patient portal feedback revealed that most patients are very likely to recommend CCCare to others (8.56 ± 2.2 [out of 10]). CONCLUSION: CCCare is effective in supporting more complete IBD-specific data capture compared with usual medical records. It is highly acceptable to patients, especially those with reasonable IT literacy. Patient concerns about privacy and security of electronic medical records (EMRs) did not significantly affect acceptability.

Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: Artificial intelligence (AI) is increasingly being used in medical imaging analysis. We aimed to evaluate the diagnostic accuracy of AI models used for detection of lymph node metastasis on pre-operative staging imaging for colorectal cancer. METHODS: A systematic review was conducted according to PRISMA guidelines using a literature search of PubMed (MEDLINE), EMBASE, IEEE Xplore and the Cochrane Library for studies published from January 2010 to October 2020. Studies reporting on the accuracy of radiomics models and/or deep learning for the detection of lymph node metastasis in colorectal cancer by CT/MRI were included. Conference abstracts and studies reporting accuracy of image segmentation rather than nodal classification were excluded. The quality of the studies was assessed using a modified questionnaire of the QUADAS-2 criteria. Characteristics and diagnostic measures from each study were extracted. Pooling of area under the receiver operating characteristic curve (AUROC) was calculated in a meta-analysis. RESULTS: Seventeen eligible studies were identified for inclusion in the systematic review, of which 12 used radiomics models and five used deep learning models. High risk of bias was found in two studies and there was significant heterogeneity among radiomics papers (73.0%). In rectal cancer, there was a per-patient AUROC of 0.808 (0.739-0.876) and 0.917 (0.882-0.952) for radiomics and deep learning models, respectively. Both models performed better than the radiologists who had an AUROC of 0.688 (0.603 to 0.772). Similarly in colorectal cancer, radiomics models with a per-patient AUROC of 0.727 (0.633-0.821) outperformed the radiologist who had an AUROC of 0.676 (0.627-0.725). CONCLUSION: AI models have the potential to predict lymph node metastasis more accurately in rectal and colorectal cancer, however, radiomics studies are heterogeneous and deep learning studies are scarce. TRIAL REGISTRATION: PROSPERO CRD42020218004 .

Artificial intelligence for the diagnosis of lymph node metastases in patients with abdominopelvic malignancy: A systematic review and meta-analysis.

PURPOSE: Accurate clinical diagnosis of lymph node metastases is of paramount importance in the treatment of patients with abdominopelvic malignancy. This review assesses the diagnostic performance of deep learning algorithms and radiomics models for lymph node metastases in abdominopelvic malignancies. METHODOLOGY: Embase (PubMed, MEDLINE), Science Direct and IEEE Xplore databases were searched to identify eligible studies published between January 2009 and March 2019. Studies that reported on the accuracy of deep learning algorithms or radiomics models for abdominopelvic malignancy by CT or MRI were selected. Study characteristics and diagnostic measures were extracted. Estimates were pooled using random-effects meta-analysis. Evaluation of risk of bias was performed using the QUADAS-2 tool. RESULTS: In total, 498 potentially eligible studies were identified, of which 21 were included and 17 offered enough information for a quantitative analysis. Studies were heterogeneous and substantial risk of bias was found in 18 studies. Almost all studies employed radiomics models (n = 20). The single published deep-learning model out-performed radiomics models with a higher AUROC (0.912 vs 0.895), but both radiomics and deep-learning models outperformed the radiologist's interpretation in isolation (0.774). Pooled results for radiomics nomograms amongst tumour subtypes demonstrated the highest AUC 0.895 (95 %CI, 0.810-0.980) for urological malignancy, and the lowest AUC 0.798 (95 %CI, 0.744-0.852) for colorectal malignancy. CONCLUSION: Radiomics models improve the diagnostic accuracy of lymph node staging for abdominopelvic malignancies in comparison with radiologist's assessment. Deep learning models may further improve on this, but data remain limited.