Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to late diagnosis and extensive heterogeneity across subtypes. Current diagnostic methods are limited in their ability to reveal underlying genomic variations essential for precision oncology. This study introduces a novel hybrid deep learning pipeline that integrates quantitative nuclear morphometry with deep convolutional image features to perform ovarian cancer subtype classification and gene mutation inference directly from Hematoxylin and Eosin (H&E) histopathological images. Using $\sim45,000$ image patches sourced from The Cancer Genome Atlas (TCGA) and public datasets, a fusion model combining a ResNet-50 Convolutional Neural Network (CNN) encoder and a Vision Transformer (ViT) was developed. This model successfully captured both local morphological texture and global tissue context. The pipeline achieved a robust overall subtype classification accuracy of $84.2\%$ (Macro AUC of $0.87 \pm 0.03$). Crucially, the model demonstrated the capacity for gene mutation inference with moderate-to-high accuracy: $AUC_{TP53} = 0.82 \pm 0.02$, $AUC_{BRCA1} = 0.76 \pm 0.04$, and $AUC_{ARID1A} = 0.73 \pm 0.05$. Feature importance analysis established direct quantitative links, revealing that nuclear solidity and eccentricity were the dominant predictors for TP53 mutation. These findings validate that quantifiable histological phenotypes encode measurable genomic signals, paving the way for cost-effective, precision histopathology in ovarian cancer triage and diagnosis.

翻译：卵巢癌作为致死率最高的妇科恶性肿瘤之一，其高死亡率主要源于诊断延迟和显著的亚型异质性。现有诊断方法在揭示精准肿瘤学所必需的潜在基因组变异方面存在局限。本研究提出了一种创新的混合深度学习流程，通过整合定量核形态测量学与深度卷积图像特征，直接利用苏木精-伊红（H&E）染色组织病理学图像实现卵巢癌亚型分类与基因突变推断。基于来自癌症基因组图谱（TCGA）及公共数据集的约45,000个图像区块，构建了融合ResNet-50卷积神经网络编码器与视觉Transformer（ViT）的模型。该模型成功捕获了局部形态纹理特征与全局组织学背景信息。该流程实现了84.2%的稳健总体亚型分类准确率（宏观AUC为0.87±0.03）。尤为关键的是，模型展现出中高精度的基因突变推断能力：TP53基因AUC=0.82±0.02，BRCA1基因AUC=0.76±0.04，ARID1A基因AUC=0.73±0.05。特征重要性分析建立了直接定量关联，揭示核致密度与偏心率是TP53突变的主导预测因子。这些发现证实了可量化的组织学表型编码了可测量的基因组信号，为卵巢癌分诊与诊断中经济高效的精准组织病理学技术开辟了新途径。