Shuangmu Zhuo

Ovarian cancer identification technology based on deep learning and second harmonic generation imaging

Abstract 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.

Label‐free imaging diagnosis and collagen‐optical evaluation of endometrioid adenocarcinoma with multiphoton microscopy

AbstractThe assessment of tumor grade and pathological stage plays a pivotal role in determining the treatment strategy and predicting the prognosis of endometrial cancer. In this study, we employed multiphoton microscopy (MPM) to establish distinctive optical pathological signatures specific to endometrioid adenocarcinoma (EAC), while also assessing the diagnostic sensitivity, specificity, and accuracy of MPM for this particular malignancy. The MPM technique exhibits robust capability in discriminating between benign hyperplasia and various grades of cancer tissue, with statistically significant differences observed in nucleocytoplasmic ratio and second harmonic generation/two‐photon excited fluorescence intensity. Moreover, by utilizing semi‐automated image analysis, we identified notable disparities in six collagen signatures between benign and malignant endometrial stroma. Our study demonstrates that MPM can differentiate between benign endometrial hyperplasia and EAC without labels, while also quantitatively assessing changes in the tumor microenvironment by analyzing collagen signatures in the endometrial stromal tissue.

Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree‐based pipeline optimization tool

AbstractOvarian cancer is currently one of the most common cancers of the female reproductive organs, and its mortality rate is the highest among all types of gynecologic cancers. Rapid and accurate classification of ovarian cancer plays an important role in the determination of treatment plans and prognoses. Nevertheless, the most commonly used classification method is based on histopathological specimen examination, which is time‐consuming and labor‐intensive. Thus, in this study, we utilize radiomics feature extraction methods and the automated machine learning tree‐based pipeline optimization tool (TOPT) for analysis of 3D, second harmonic generation images of benign, malignant and normal human ovarian tissues, to develop a high‐efficiency computer‐aided diagnostic model. Area under the receiver operating characteristic curve values of 0.98, 0.96 and 0.94 were obtained, respectively, for the classification of the three tissue types. Furthermore, this approach can be readily applied to other related tissues and diseases, and has great potential for improving the efficiency of medical diagnostic processes.