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BACKGROUND: We aimed to investigate the value of a machine learning (ML) algorithm in the preoperative prediction of lymph node metastasis in patients with rectal cancer. METHODS: Based on the histopathological results, 126 rectal cancer patients were divided into two groups: lymph node metastasis-positive and metastasis-negative groups. We collected clinical and laboratory data, three-dimensional endorectal ultrasound (3D-ERUS) findings, and parameters of the tumor for between-group comparisons. We constructed a clinical prediction model based on the ML algorithm, which demonstrated the best diagnostic performance. Finally, we analyzed the diagnostic results and processes of the ML model. RESULTS: Between the two groups, there were significant differences in serum carcinoembryonic antigen (CEA) levels, tumor length, tumor breadth, circumferential extent of the tumor, resistance index (RI), and ultrasound T-stage (P < 0.05). The extreme gradient boosting (XGBoost) model had the best comprehensive diagnostic performance for predicting lymph node metastasis in patients with rectal cancer. Compared with experienced radiologists, the XGBoost model showed significantly higher diagnostic value in predicting lymph node metastasis; the area under curve (AUC) value of the receiver operating characteristic (ROC) curve of the XGBoost model and experienced radiologists was 0.82 and 0.60, respectively. CONCLUSIONS: Preoperative predictive utility in lymph node metastasis was demonstrated by the XGBoost model based on the 3D-ERUS finding and related clinical information. This could be useful in guiding clinical decisions on the selection of different treatment strategies.

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Objectives: Evidence suggests that about 80% of all salivary gland tumors involve the parotid glands, with approximately 20% of parotid gland tumors (PGTs) being malignant. Discriminating benign and malignant parotid gland lesions preoperatively is vital for selecting the appropriate treatment strategy. This study explored the diagnostic performance of deep learning system for discriminating benign and malignant PGTs in ultrasonography images and compared it with radiologists. Methods. A total of 251 consecutive patients with surgical resection and proven parotid gland malignant or benign tumors who underwent preoperative ultrasound examinations were enrolled in this study between January 2014 and November 2020. Next, we compared the diagnostic accuracy of deep learning methods (ViT-B\16, EfficientNetB3, DenseNet121, and ResNet50) and radiologists in parotid gland tumor. In addition, the area under the curve (AUC), specificity, sensitivity, positive predictive value, and negative predictive value were calculated. Results: Among the 251 patients, 176/251 were the training set, whereas 75/251 were the validation set. Results showed that 74/251 patients had malignant tumor. Deep learning models achieved good performance in differentiating benign from malignant tumors, with the diagnostic accuracy and AUCs of ViT-B\16, EfficientNetB3, DenseNet121, and ResNet50 model being 81% and 0.81, 80% and 0.82, 77% and 0.81, and 79% and 0.80, respectively. On the other hand, the diagnostic accuracy and AUCs of radiologists were 77%-81% and 0.68-0.75, respectively. It was evident that the diagnostic accuracy of deep learning methods was higher than that of inexperienced radiologists, but there was no significant difference between deep learning methods and experienced radiologists. Conclusions: This study shows that the deep learning system can be used for diagnosing parotid tumors. The findings also suggest that the deep learning system may improve the diagnosis performance of inexperienced radiologists.

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Integration of human papillomavirus (HPV) DNA into the human genome is considered as a key event in cervical carcinogenesis. Here, we perform comprehensive characterization of large-range virus-human integration events in 16 HPV16-positive cervical tumors using the Nanopore long-read sequencing technology. Four distinct integration types characterized by the integrated HPV DNA segments are identified with Type B being particularly notable as lacking E6/E7 genes. We further demonstrate that multiple clonal integration events are involved in the use of shared breakpoints, the induction of inter-chromosomal translocations and the formation of extrachromosomal circular virus-human hybrid structures. Combined with the corresponding RNA-seq data, we highlight LINC00290, LINC02500 and LENG9 as potential driver genes in cervical cancer. Finally, we reveal the spatial relationship of HPV integration and its various structural variations as well as their functional consequences in cervical cancer. These findings provide insight into HPV integration and its oncogenic progression in cervical cancer.

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AIM: To investigate whether mitochondrial DNA (mtDNA) background (haplogroup) is associated with cervical cancer in patients in southern China. METHODS: A case-control study of 150 patients with cervical cancer and 217 geographically matched controls was conducted in Wenzhou, a southern Chinese city in the Zhejiang province. DNA from peripheral blood was extracted and sequenced. Sequences were aligned to the mtDNA revised Cambridge Reference Sequence (GenBank number NC_012920) to determine mtDNA single nucleotide polymorphisms (SNPs) and haplogroups. RESULTS: We found that both M and N haplogroups and their diagnostic SNPs (A10398G and C10400T) are not associated with the risk of cervical cancer. However, individuals with haplogroup D4b1/D4b1*, an M subhaplogroup, exhibited an increased risk of cervical cancer (odds ratio [OR] = 1.034; 95% confidence interval [CI] 1.004, 1.066; P = 0.011/OR =1.027; 95% CI 1.001, 1.055; P = 0.027). Individuals with SNPs C10181T/A10136G (OR =1.034; 95% CI 1.004, 1.066; P = 0.011/OR =1.027; 95% CI 1.001, 1.055; P = 0.027) were more susceptible to cervical cancer than individuals without. Furthermore, we determined that mtDNA background is not associated with the progression of cervical cancer. CONCLUSIONS: Our results indicate that mtDNA haplogroups play a role in cervical cancer initiation.

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