Yu Shi

Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews

Background Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading to better patient outcomes. Objective We performed an umbrella review to summarize and critically evaluate the evidence for the AI-based imaging diagnosis of cancers. Methods PubMed, Embase, Web of Science, Cochrane, and IEEE databases were searched for relevant systematic reviews from inception to June 19, 2024. Two independent investigators abstracted data and assessed the quality of evidence, using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Systematic Reviews and Research Syntheses. We further assessed the quality of evidence in each meta-analysis by applying the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria. Diagnostic performance data were synthesized narratively. Results In a comprehensive analysis of 158 included studies evaluating the performance of AI algorithms in noninvasive imaging diagnosis across 8 major human system cancers, the accuracy of the classifiers for central nervous system cancers varied widely (ranging from 48% to 100%). Similarities were observed in the diagnostic performance for cancers of the head and neck, respiratory system, digestive system, urinary system, female-related systems, skin, and other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 reviews meta-analyzed sensitivity and specificity for esophageal cancer, showing ranges of 90%-95% and 80%-93.8%, respectively. In the case of breast cancer detection, 8 reviews calculated the pooled sensitivity and specificity within the ranges of 75.4%-92% and 83%-90.6%, respectively. Four meta-analyses reported the ranges of sensitivity and specificity in ovarian cancer, and both were 75%-94%. Notably, in lung cancer, the pooled specificity was relatively low, primarily distributed between 65% and 80%. Furthermore, 80.4% (127/158) of the included studies were of high quality according to the JBI Critical Appraisal Checklist, with the remaining studies classified as medium quality. The GRADE assessment indicated that the overall quality of the evidence was moderate to low. Conclusions Although AI shows great potential for achieving accelerated, accurate, and more objective diagnoses of multiple cancers, there are still hurdles to overcome before its implementation in clinical settings. The present findings highlight that a concerted effort from the research community, clinicians, and policymakers is required to overcome existing hurdles and translate this potential into improved patient outcomes and health care delivery. Trial Registration PROSPERO CRD42022364278; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364278

Choosing the right timing for interval debulking surgery and perioperative chemotherapy may improve the prognosis of advanced epithelial ovarian cancer: a retrospective study

Abstract Background Primary debulking surgery (PDS) is the main treatment for patients with advanced ovarian cancer, and neoadjuvant chemotherapy (NACT) is for bulky stage III-IV patients who are poor surgical candidates and/or for whom there is a low likelihood of optimal cytoreduction. NACT can increase the rate of complete cytoreduction, but this advantage has not translated to an improvement in survival. Therefore, we aimed to identify factors associated with the survival of patients who received NACT followed by interval debulking surgery (IDS). Methods A retrospective study was conducted in FIGO stage IIIC-IV epithelial ovarian cancer patients who underwent PDS or IDS in our center between January 1st, 2013, and December 31st, 2018. Results A total of 273 cases were included, of whom 20 were lost to follow-up. Progression-free survival (PFS) and overall survival (OS) of the IDS and PDS groups were found to be similar, although the proportion of patients in stage IV and serum carbohydrate antigen 125 (CA125) levels before treatment in the IDS group were significantly higher than that in the PDS group. Body mass index (BMI), CA125 level before IDS, residual disease after surgery, and the interval between preoperative and postoperative chemotherapy were all found to be independent prognostic factors for PFS; FIGO stage, residual disease after surgery, and CA125 level before IDS were independent prognostic factors for OS. We found that PFS and OS were both significantly longer in patients with normal CA125 levels before IDS and when the interval between preoperative and postoperative chemotherapy was < 35.5 days (IDS-3 group) than for patients in the PDS group. Conclusions The results suggested the importance of timely IDS and postoperative chemotherapy and potentially allowed the identification of patients who would benefit the most from NACT. Normal CA125 levels before IDS and an interval between preoperative and postoperative chemotherapy no longer than 5 weeks were associated with improved prognosis in advanced ovarian cancer patients.