Tomoyuki Otani

Artificial Intelligence-Based Histopathological Subtyping of High-Grade Serous Ovarian Cancer

Four subtypes of ovarian high-grade serous carcinoma (HGSC) have previously been identified, each with different prognoses and drug sensitivities. However, the accuracy of classification depended on the assessor's experience. This study aimed to develop a universal algorithm for HGSC-subtype classification using deep learning techniques. An artificial intelligence (AI)-based classification algorithm, which replicates the consensus diagnosis of pathologists, was formulated to analyze the morphological patterns and tumor-infiltrating lymphocyte counts for each tile extracted from whole slide images of ovarian HGSC available in The Cancer Genome Atlas (TCGA) data set. The accuracy of the algorithm was determined using the validation set from the Japanese Gynecologic Oncology Group 3022A1 (JGOG3022A1) and Kindai and Kyoto University (Kindai/Kyoto) cohorts. The algorithm classified the four HGSC-subtypes with mean accuracies of 0.933, 0.910, and 0.862 for the TCGA, JGOG3022A1, and Kindai/Kyoto cohorts, respectively. To compare mesenchymal transition (MT) with non-MT groups, overall survival analysis was performed in the TCGA data set. The AI-based prediction of HGSC-subtype classification in TCGA cases showed that the MT group had a worse prognosis than the non-MT group (P = 0.017). Furthermore, Cox proportional hazard regression analysis identified AI-based MT subtype classification prediction as a contributing factor along with residual disease after surgery, stage, and age. In conclusion, a robust AI-based HGSC-subtype classification algorithm was established using virtual slides of ovarian HGSC.

HER2-amplified endometrial carcinoma and AFP-producing endometrial carcinoma

Histopathological subtyping of high-grade serous ovarian cancer using whole slide imaging

We have established 4 histopathologic subtyping of high-grade serous ovarian cancer (HGSOC) and reported that the mesenchymal transition (MT) type has a worse prognosis than the other subtypes. In this study, we modified the histopathologic subtyping algorithm to achieve high interobserver agreement in whole slide imaging (WSI) and to characterize the tumor biology of MT type for treatment individualization. Four observers performed histopathological subtyping using WSI of HGSOC in The Cancer Genome Atlas data. As a validation set, cases from Kindai and Kyoto Universities were independently evaluated by the 4 observers to determine concordance rates. In addition, genes highly expressed in MT type were examined by gene ontology term analysis. Immunohistochemistry was also performed to validate the pathway analysis. After algorithm modification, the kappa coefficient, which indicates interobserver agreement, was greater than 0.5 (moderate agreement) for the 4 classifications and greater than 0.7 (substantial agreement) for the 2 classifications (MT vs. non-MT). Gene expression analysis showed that gene ontology terms related to angiogenesis and immune response were enriched in the genes highly expressed in the MT type. CD31 positive microvessel density was higher in the MT type compared to the non-MT type, and tumor groups with high infiltration of CD8/CD103 positive immune cells were observed in the MT type. We developed an algorithm for reproducible histopathologic subtyping classification of HGSOC using WSI. The results of this study may be useful for treatment individualization of HGSOC, including angiogenesis inhibitors and immunotherapy.

Differentiation of uterine fibroids and sarcomas by MRI and serum LDH levels: a multicenter study of the KAMOGAWA study

In the differential diagnosis between uterine fibroids and uterine sarcomas, real-world magnetic resonance imaging (MRI) diagnostic information is scarce; furthermore, high diagnostic sensitivity is important in clinical practice. We previously developed a diagnostic algorithm to detect uterine sarcoma with high sensitivity using simple MRI images and serum lactate dehydrogenase (LDH) levels. In this multicenter study, we investigated the preoperative diagnosis of sarcoma in the real world and further validated the usefulness of our diagnostic algorithm. Of 154 uterine sarcomas and 154 uterine fibroids treated at 15 centers between January 2006 and December 2020, 139 sarcomas (16 smooth muscle tumors of uncertain malignant potential) and 141 fibroids with diffusion-weighted imaging information were included in the analysis. The diagnostic algorithm was validated by 3 radiologists who were blinded to the clinical information and pathologic diagnoses and who read the MRIs. The sensitivity/specificity of preoperative diagnosis was 77.7%/92.9% for the preoperative report; 92.1%/72.3% for algorithm A; and 82.0%/85.8% for algorithm B (McNemar's test p<0.05). Comparison of overall survival rates among 3 groups (Group 1: negative A, Group 2: positive A and negative B; Group 3: positive B) using algorithms A and B showed p=0.012. On multivariate analysis, stage, and serum LDH level were independent prognostic factors. MRI is useful for preoperative diagnosis of uterine sarcoma, and the sarcoma diagnostic algorithm presented in this study is an option for diagnosing sarcoma with greater sensitivity. This information should be shared with patients.