Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology.
BackgroundThere is currently conflicting evidence surrounding the effects of obesity on postoperative outcomes. Previous studies have found obesity to be associated with adverse events, but others have found no association. The aim of this study was to determine whether increasing body mass index (BMI) is an independent risk factor for development of major postoperative complications.MethodsThis was a multicentre prospective cohort study across the UK and Republic of Ireland. Consecutive patients undergoing elective or emergency gastrointestinal surgery over a 4‐month interval (October–December 2014) were eligible for inclusion. The primary outcome was the 30‐day major complication rate (Clavien–Dindo grade III–V). BMI was grouped according to the World Health Organization classification. Multilevel logistic regression models were used to adjust for patient, operative and hospital‐level effects, creating odds ratios (ORs) and 95 per cent confidence intervals (c.i.).ResultsOf 7965 patients, 2545 (32·0 per cent) were of normal weight, 2673 (33·6 per cent) were overweight and 2747 (34·5 per cent) were obese. Overall, 4925 (61·8 per cent) underwent elective and 3038 (38·1 per cent) emergency operations. The 30‐day major complication rate was 11·4 per cent (908 of 7965). In adjusted models, a significant interaction was found between BMI and diagnosis, with an association seen between BMI and major complications for patients with malignancy (overweight: OR 1·59, 95 per cent c.i. 1·12 to 2·29, P = 0·008; obese: OR 1·91, 1·31 to 2·83, P = 0·002; compared with normal weight) but not benign disease (overweight: OR 0·89, 0·71 to 1·12, P = 0·329; obese: OR 0·84, 0·66 to 1·06, P = 0·147).ConclusionOverweight and obese patients undergoing surgery for gastrointestinal malignancy are at increased risk of major postoperative complications compared with those of normal weight.
Background Coronavirus infection (COVID) presents with flu-like symptoms and can cause serious complications. Here, we discuss the presentation and outcomes of COVID in an ambulatory setting along with distribution of positive cases amongst healthcare workers (HCWs). Method Patients who visited the COVID clinic between 03/11/2020 and 06/14/2020 were tested based on the CDC guidelines at the time using PCR-detection methods. Medical records were reviewed and captured on a RedCap database. Statistical analysis was performed using both univariate and bivariate analysis using Fischer’s exact test with 2-sided P values. Results Of the 2471 evaluated patients, 846 (34.2%) tested positive for COVID. Mean age of positivity was 43.4 years (SD ± 15.4), 60.1% were female and 49% were Black. 58.7% of people tested had a known exposure, and amongst those with exposure, 57.3% tested positive. Ninety-four patients were hospitalized (11.1%), of which 22 patients (23.4%) required ICU admission and 10 patients died. The overall death rate of patients presenting to clinic was 0.4%, or 1.2% amongst positive patients. Median length of hospital stay was 6 days (range 1-51). Symptoms significantly associated with COVID included: anosmia, fever, change in taste, anorexia, myalgias, cough, chills, and fatigue. Increased risk of COVID occurred with diabetes, whereas individuals with lung disease or malignancy were not associated with increased risk of COVID. Amongst COVID positive HCWs, the majority were registered nurses (23.4%), most working in general medicine (39.8%) followed by critical care units (14.3%). Discussion/Conclusion Blacks and females had the highest infection rates. There was a broad range in presentation from those who are very ill and require hospitalization and those who remain ambulatory. The above data could assist health care professionals perform a targeted review of systems and co-morbidities, allowing for appropriate patient triage.
Background: Patient selection for critical care admission must balance patient safety with optimal resource allocation. This study aimed to determine the relationship between critical care admission, and postoperative mortality after abdominal surgery. Methods: This prespecified secondary analysis of a multicentre, prospective, observational study included consecutive patients enrolled in the DISCOVER study from UK and Republic of Ireland undergoing major gastrointestinal and liver surgery between October and December 2014. The primary outcome was 30-day mortality. Multivariate logistic regression was used to explore associations between critical care admission (planned and unplanned) and mortality, and intercentre variation in critical care admission after emergency laparotomy. Results: Of 4529 patients included, 37.8% (n¼1713) underwent planned critical care admissions from theatre. Some 3.1% (n¼86/2816) admitted to ward-level care subsequently underwent unplanned critical care admission. Overall 30-day mortality was 2.9% (n¼133/4519), and the risk-adjusted association between 30-day mortality and critical care admission was higher in unplanned [odds ratio (OR): 8.65, 95% confidence interval (CI): 3.51e19.97) than planned admissions (OR: 2.32, 95% CI: 1.43e3.85). Some 26.7% of patients (n¼1210/4529) underwent emergency laparotomies. After adjustment, 49.3% (95% CI: 46.8e51.9%, P<0.001) were predicted to have planned critical care admissions, with 7% (n¼10/145) of centres outside the 95% CI. Conclusions: After risk adjustment, no 30-day survival benefit was identified for either planned or unplanned postoperative admissions to critical care within this cohort. This likely represents appropriate admission of the highest-risk patients. Planned admissions in selected, intermediate-risk patients may present a strategy to mitigate the risk of unplanned admission. Substantial inter-centre variation exists in planned critical care admissions after emergency laparotomies.
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