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Ovarian cancer is among the most common gynecological cancers and the eighth leading cause of cancer-related deaths among women worldwide. Surgery is among the most important options for cancer treatment. During surgery, a biopsy is generally required to screen for lesions; however, traditional case examinations are time consuming and laborious and require extensive experience and knowledge from pathologists. Therefore, this study proposes a simple, fast, and label-free ovarian cancer diagnosis method that combines second harmonic generation (SHG) imaging and deep learning. Unstained fresh human ovarian tissues were subjected to SHG imaging and accurately characterized using the Pyramid Vision Transformer V2 (PVTv2) model. The results showed that the SHG imaged collagen fibers could quantify ovarian cancer. In addition, the PVTv2 model could accurately differentiate the 3240 SHG images obtained from our imaging collection into benign, normal, and malignant images, with a final accuracy of 98.4%. These results demonstrate the great potential of SHG imaging techniques combined with deep learning models for diagnosing the diseased ovarian tissues.
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BACKGROUND: Neoadjuvant chemoradiotherapy(nCRT) for rectal cancer can lead to structural changes in collagen in the tumor microenvironment and increase the risk of postoperative anastomotic stenosis (AS). However, the quantitative relationship between AS and collagen has not been defined. This study is to quantitatively analyze the collagen features in rectal cancer and explore the relationship between the changes of collagen and postoperative anastomotic stenosis after nCRT. STUDY DESIGN: This study is a retrospective study. A total of 371 patients with rectal cancer were included. Collagen features in the resection margin of rectal cancer anastomosis was extracted by multi-photon imaging. LASSO-logistic regression was performed to select features related to AS and the collagen score (CS) was constructed. Area under the receiver operating curve (AUROC) and decision curve analysis was performed to evaluate the discrimination and clinical benefit of the nomogram. RESULTS: The probability of AS was 23% in the training cohort and 15.9% in the validation cohort. In the training cohort, the distance between tumor and resection margin, anastomotic leakage and CS were independent risk factors for postoperative AS in univariate and multivariate analyses. A nomogram was constructed based on the above results. The prediction nomogram showed good discrimination (AUROC, 0.864;95% CI, 0.776 to 0.952) and was validated in the validation cohort (AUROC, 0.918;95% CI, 0.851 to 0.985). CONCLUSIONS: CS is an independent risk factor for AS in rectal cancer after nCRT. The predictive model based on CS can predict the occurrence of postoperative AS.
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Lung cancer is the most commonly diagnosed cancer and the leading cause of cancer-related deaths in China. Rapid and precise evaluation of tumor tissue during lung cancer surgery can reduce operative time and improve negative-margin assessment, thus increasing disease-free and overall survival rates. This study aimed to explore the potential of label-free multiphoton microscopy (MPM) for imaging adenocarcinoma tissues, detecting histopathological features, and distinguishing between normal and cancerous lung tissues. We showed that second harmonic generation (SHG) signals exhibit significant specificity for collagen fibers, enabling the quantification of collagen features in lung adenocarcinomas. In addition, we developed a collagen score that could be used to distinguish between normal and tumor areas at the tumor boundary, showing good classification performance. Our findings demonstrate that MPM imaging technology combined with an image-based collagen feature extraction method can rapidly and accurately detect early-stage lung adenocarcinoma tissues.