Qian Chen

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

Intratumoral and peritumoral radiomics based on super-resolution T2-weighted imaging for prediction of normal-sized lymph node metastasis in cervical cancer

Background Preoperative identification of normal-sized lymph node metastases (LNM) remains clinically significant yet challenging in cervical cancer. Purpose To investigate the value of super-resolution T2WI-derived intratumoral and peritumoral radiomics for normal-sized LNM prediction in cervical cancer. Material and Methods A total of 257 patients from three sites of our hospital were divided into a development cohort (site 1, n = 97), a validation cohort (site 1, n = 42), and two internal test cohorts (site 2, n = 62; site 3, n = 56). Super-resolution reconstruction based on generative adversarial network was applied to all images. The volume of interest delineation encompassed primary tumor boundaries with outward expansions (1–5 mm increments) in super-resolution T2-weighted (T2W) imaging. Radiomics features were independently extracted from intratumoral and five peritumoral regions. The clinical, radiomics and combined models were built using multilayer perceptron. Model performance was evaluated through receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Results The IntraPeri3 mm radiomics model achieved superior discriminative performance compared to other radiomics models. The combined model integrated clinical variables (tumor size and squamous cell carcinoma antigen), intratumoral and peritumoral 3 mm radiomics features yielded optimal performance (AUC = 0.838 in the development cohort, 0.808 in the validation cohort, and 0.769 and 0.766 in the internal test cohorts). DCA confirmed the combined model's enhanced clinical utility across probability thresholds. Conclusion Super-resolution T2W-based radiomics aids in predicting normal-sized LNM in cervical cancer, especially the combined model incorporating clinical information, intratumoral and peritumoral 3 mm radiomics features demonstrates optimal diagnostic performance.

Upregulation of SOX9 promotes the self‐renewal and tumorigenicity of cervical cancer through activating the Wnt/β‐catenin signaling pathway

Abstract Sry‐box9 (SOX9) maintains stem cell properties and plays crucial roles in many cancers. However, whether SOX9 is correlated with cervical cancer cell stemness and its detailed mechanism remains obscure. We studied the relationship between SOX9 and prognosis of cervical cancer through public database, and SOX9 was related to poor prognosis of cervical cancer. Elevated SOX9 expression enhanced the self‐renewal properties and promotes tumorigenicity in cervical cancer. Overexpression of SOX9 could promote the expression of stem cell‐related factors in cervical cancer cells and xenografts. Meanwhile, overexpression of SOX9 could also enhance the expressions of FZD10, β‐catenin, and c‐Myc in cervical cancer cells and xenografts, while inhibiting the expression of DDK1. The activation of Wnt pathway by chir‐99 021 raised the tumor spheroid ability of SOX9 knockdown HeLa cells. In addition, SOX9 could transcriptional inhibit DKK1 and activate FZD10 and MYC by binding to their promoters to affect the Wnt/β‐catenin pathway. These results demonstrated SOX9 regulated the self‐renewal and tumorigenicity of cervical cancer through Wnt/β‐catenin pathway by directly transcriptional activation of FZD10, MYC and transcriptional inhibition of DKK1.