Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer

BACKGROUND Colorectal cancer significantly impacts global health,with unplanned reoperations post-surgery being key determinants of patient outcomes.Existing predictive models for these reoperations lack precision in integrating complex clinical data.AIM To develop and validate a machine learning model for predicting unplanned reoperation risk in colorectal cancer patients.METHODS Data of patients treated for colorectal cancer(n=2044)at the First Affiliated Hospital of Wenzhou Medical University and Wenzhou Central Hospital from March 2020 to March 2022 were retrospectively collected.Patients were divided into an experimental group(n=60)and a control group(n=1984)according to unplanned reoperation occurrence.Patients were also divided into a training group and a validation group(7:3 ratio).We used three different machine learning methods to screen characteristic variables.A nomogram was created based on multifactor logistic regression,and the model performance was assessed using receiver operating characteristic curve,calibration curve,Hosmer-Lemeshow test,and decision curve analysis.The risk scores of the two groups were calculated and compared to validate the model.RESULTS More patients in the experimental group were≥60 years old,male,and had a history of hypertension,laparotomy,and hypoproteinemia,compared to the control group.Multiple logistic regression analysis confirmed the following as independent risk factors for unplanned reoperation(P<0.05):Prognostic Nutritional Index value,history of laparotomy,hypertension,or stroke,hypoproteinemia,age,tumor-node-metastasis staging,surgical time,gender,and American Society of Anesthesiologists classification.Receiver operating characteristic curve analysis showed that the model had good discrimination and clinical utility.CONCLUSION This study used a machine learning approach to build a model that accurately predicts the risk of postoperative unplanned reoperation in patients with colorectal cancer,which can improve treatment decisions and prognosis.

Establishment and validation of a computer-assisted colonic polyp localization system based on deep learning

BACKGROUND Artificial intelligence in colonoscopy is an emerging field,and its application may help colonoscopists improve inspection quality and reduce the rate of missed polyps and adenomas.Several deep learning-based computer-assisted detection(CADe)techniques were established from small single-center datasets,and unrepresentative learning materials might confine their application and generalization in wide practice.Although CADes have been reported to identify polyps in colonoscopic images and videos in real time,their diagnostic performance deserves to be further validated in clinical practice.AIM To train and test a CADe based on multicenter high-quality images of polyps and preliminarily validate it in clinical colonoscopies.METHODS With high-quality screening and labeling from 55 qualified colonoscopists,a dataset consisting of over 71000 images from 20 centers was used to train and test a deep learning-based CADe.In addition,the real-time diagnostic performance of CADe was tested frame by frame in 47 unaltered full-ranged videos that contained 86 histologically confirmed polyps.Finally,we conducted a selfcontrolled observational study to validate the diagnostic performance of CADe in real-world colonoscopy with the main outcome measure of polyps per colonoscopy in Changhai Hospital.RESULTS The CADe was able to identify polyps in the test dataset with 95.0%sensitivity and 99.1%specificity.For colonoscopy videos,all 86 polyps were detected with 92.2%sensitivity and 93.6%specificity in frame-by-frame analysis.In the prospective validation,the sensitivity of CAD in identifying polyps was 98.4%(185/188).Folds,reflections of light and fecal fluid were the main causes of false positives in both the test dataset and clinical colonoscopies.Colonoscopists can detect more polyps(0.90 vs 0.82,P<0.001)and adenomas(0.32 vs 0.30,P=0.045)with the aid of CADe,particularly polyps<5 mm and flat polyps(0.65 vs 0.57,P<0.001;0.74 vs 0.67,P=0.001,respectively).However,high efficacy is not realized in colonoscopies with inadequate bowel preparation and withdrawal time(P=0.32;P=0.16,respectively).CONCLUSION CADe is feasible in the clinical setting and might help endoscopists detect more polyps and adenomas,and further confirmation is warranted.

Reliability and validity of sub-axial injury classification in clinical application

Objective To evaluate the clinical reliability and validity of the sub-axial injury classification (SLIC) system proposed by the Spine Trauma Study Group (STSG) in 2007. Methods Thirty cases of cervical injury were randomly chosen

Technology-Driven and Evidence-Based Genomic Analysis for Integrated Pediatric and Prenatal Genetics Evaluation

The first decade since the completion of the Human Genome Project has been marked with rapid development of genomic technologies and their immediate clinical applications. Genomic analysis using oligonucleotide array comparative genomic hybridization （aCGH） or single nucleotide polymorphism （SNP） chips has been applied to pediatric patients with developmental and intellectual disabilities （DD/ ID）, multiple congenital anomalies （MCA） and autistic spectrum disorders （ASD）. Evaluation of analytical and clinical validities of aCGH showed 〉 99% sensitivity and specificity and increased analytical resolution by higher density probe coverage. Reviews of case series, multi-center comparison and large patient-control studies demonstrated a diagnostic yield of 12%--20%; approximately 60% of these abnormalities were recurrent genomic disorders. This pediatric experience has been extended toward prenatal diagnosis. A series of reports indicated approximately 10% of pregnancies with ultrasound-detected structural anomalies and normal cytogenetic findings had genomic abnormalities, and 30% of these abnormalities were syndromic genomic disorders. Evidence-based practice guidelines and standards for implementing genomic analysis and web-delivered knowledge resources for interpreting genomic findings have been established. The progress from this technology-driven and evidence-based genomic analysis provides not only opportunities to dissect disease-causing mechanisms and develop rational therapeutic interventions but also important lessons for integrating genomic sequencing into pediatric and prenatal genetic evaluation.