Molecular Histopathology for Establishing Diagnostic Method and Clinical Therapy for Ovarian Carcinoma
Bibliographic record
Abstract
Ovarian carcinoma (OC) is considered the deadliest gynecological malignancy. It is typically diagnosed in the advanced stages of the disease, with metastatic sites widely disseminated within the abdominal cavity. OC treatment is challenging due to the high rate of disease recurrence, which is further complicated by acquired chemoresistance caused by the reversion of the pathological variant. Therefore, more effective treatments are still being sought. Histologically, OC is classified into serous, mucinous, endometrioid, clear cell, and transitional cell carcinomas and malignant Brenner tumor. Recent clinicopathological and molecular biological studies demonstrated that these subtypes differ in histogenesis and anti-tumor agent sensitivity. In Japan, the incidence rates of the histological types of OC, namely, serous carcinoma, mucinous carcinoma, endometrioid carcinoma, and clear cell adenocarcinoma, are 39%, 12%, 16%, and 23%, respectively. Serous carcinoma is classified as high or low grade, with the former accounting for the overwhelming majority. In this study, the molecular pathological classification of OC has been described based on the characteristics of the two types of OC, types 1 and 2. Compared with Europe and the United States, Japan has a higher prevalence of type 1 OC and a lower prevalence of type 2 OC. The prevalence of each type of OC varies by race. It has been elucidated that the prevalence rate of each type of ovarian cancer in Asian countries is similar to that in Japan. Thus, OC is a heterogeneous disease. Furthermore, OC has been attributed to molecular biological mechanisms that vary among tissue subtypes. Therefore, it is necessary to conduct treatment based on accurate diagnoses of each tissue type and establish an optimal treatment strategy, and now is the transition period.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".