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A case of giant panda ovarian cancer diagnosis and histopathology.

ABSTRACT: BACKGROUND:Ovarian cancer is diagnosed clinically by detecting ovarian cancer-related factors and markers. Here, we report a case of giant panda ovarian tumor metastasis with a combination of clinical and histopathological diagnosis. CASE PRESENTATION:Histopathological studies revealed severe lesions and tumor cells in the ovaries, lungs, spleen, kidneys and perianal tissue. Immunohistochemistry staining showed that the ovarian cancer markers B7-H4, CA125, and HE4 were highly expressed in the lungs, kidneys, spleen, ovaries and perianal tissue. Tumor marker tests detected significantly high levels of AFP in serum. CONCLUSION:Clinical biomarkers combined with histopathology can provide a more accurate diagnosis of ovarian cancer metastasis and identification of ovarian cancer types than either method alone. The giant panda's death may be due to granulosa cell tumor and theca cell tumor metastasis causing multiple organ dysfunction or even failure.

SUBMITTER: Gao Q

PROVIDER: S-EPMC6186091 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

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A case of giant panda ovarian cancer diagnosis and histopathology.

Gao Qi Q Wang Chengdong C Li Desheng D Zhang Hemin H Deng Linhua L Li Caiwu C Chen Zhengli Z

BMC veterinary research 20181012 1

<h4>Background</h4>Ovarian cancer is diagnosed clinically by detecting ovarian cancer-related factors and markers. Here, we report a case of giant panda ovarian tumor metastasis with a combination of clinical and histopathological diagnosis.<h4>Case presentation</h4>Histopathological studies revealed severe lesions and tumor cells in the ovaries, lungs, spleen, kidneys and perianal tissue. Immunohistochemistry staining showed that the ovarian cancer markers B7-H4, CA125, and HE4 were highly expr ...[more]

PMID: 30314476

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Project description:BackgroundOvarian cancer remains the leading gynecological cause of cancer mortality. Predicting the sensitivity of ovarian cancer to chemotherapy at the time of pathological diagnosis is a goal of precision medicine research that we have addressed in this study using a novel deep-learning neural network framework to analyze the histopathological images.MethodsWe have developed a method based on the Inception V3 deep learning algorithm that complements other methods for predicting response to standard platinum-based therapy of the disease. For the study, we used histopathological H&E images (pre-treatment) of high-grade serous carcinoma from The Cancer Genome Atlas (TCGA) Genomic Data Commons portal to train the Inception V3 convolutional neural network system to predict whether cancers had independently been labeled as sensitive or resistant to subsequent platinum-based chemotherapy. The trained model was then tested using data from patients left out of the training process. We used receiver operating characteristic (ROC) and confusion matrix analyses to evaluate model performance and Kaplan-Meier survival analysis to correlate the predicted probability of resistance with patient outcome. Finally, occlusion sensitivity analysis was piloted as a start toward correlating histopathological features with a response.ResultsThe study dataset consisted of 248 patients with stage 2 to 4 serous ovarian cancer. For a held-out test set of forty patients, the trained deep learning network model distinguished sensitive from resistant cancers with an area under the curve (AUC) of 0.846 ± 0.009 (SE). The probability of resistance calculated from the deep-learning network was also significantly correlated with patient survival and progression-free survival. In confusion matrix analysis, the network classifier achieved an overall predictive accuracy of 85% with a sensitivity of 73% and specificity of 90% for this cohort based on the Youden-J cut-off. Stage, grade, and patient age were not statistically significant for this cohort size. Occlusion sensitivity analysis suggested histopathological features learned by the network that may be associated with sensitivity or resistance to the chemotherapy, but multiple marker studies will be necessary to follow up on those preliminary results.ConclusionsThis type of analysis has the potential, if further developed, to improve the prediction of response to therapy of high-grade serous ovarian cancer and perhaps be useful as a factor in deciding between platinum-based and other therapies. More broadly, it may increase our understanding of the histopathological variables that predict response and may be adaptable to other cancer types and imaging modalities.

Project description:Epithelial ovarian cancer (EOC) has a 5-year relative survival of 50%, partly because markers of early-stage disease are not available in current clinical diagnostics. The aim of the present study was to investigate whether EOC is associated with transcriptional profiles in blood collected up to 7 years before diagnosis. For this, we used RNA-stabilized whole blood, which contains circulating immune cells, from a sample of EOC cases from the population-based Norwegian Women and Cancer (NOWAC) postgenome cohort. We explored case-control differences in gene expression in all EOC (66 case-control pairs), as well as associations between gene expression and metastatic EOC (56 pairs), serous EOC (45 pairs, 44 of which were metastatic), and interval from blood sample collection to diagnosis (≤3 or >3 years; 34 and 31 pairs, respectively). Lastly, we assessed differential expression of genes associated with EOC in published functional genomics studies that used blood samples collected from newly diagnosed women. After adjustment for multiple testing, this nested case-control study revealed no significant case-control differences in gene expression in all EOC (false discovery rate q>0.96). With the exception of a few probes, the log2 fold change values obtained in gene-wise linear models were below ±0.2. P-values were lowest in analyses of metastatic EOC (80% of which were serous EOC). No common transcriptional profile was indicated by interval to diagnosis; when comparing the 100 genes with the lowest p-values in gene-wise tests in samples collected ≤3 and >3 years before EOC diagnosis, no overlap in these genes was observed. Among 86 genes linked to ovarian cancer in previous publications, our data contained expression values for 42, and of these, tests of LIME1, GPR162, STAB1, and SKAP1, resulted in unadjusted p<0.05. Although limited by sample size, our findings indicated less variation in blood gene expression between women with similar tumor characteristics.

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A case of giant panda ovarian cancer diagnosis and histopathology.

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A case of giant panda ovarian cancer diagnosis and histopathology.

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