## 1 Introduction
investigated the effects of simultaneous exposure to cisplatin and the electromagnetic environment of clinical magnetic resonance imaging (MRI) on the in vitro chemotherapy sensitivity of ovarian cancer cell lines [19]. In their study, cells treated with cisplatin were exposed to three MRI acquisition sequences produced by a 3T clinical device--T2-weighted 2D turbo spin echo, 3D TSE,and gradient recalled echo (GRE)--to examine the effects of radiofrequency (RF) fields and time-varying magnetic fields using methods previously employed by [PERSON] and colleagues [20]. The analysis concluded that simultaneous exposure to cisplatin and MRI did not significantly affect the in vitro activity (cisplatin effect) on ovarian cancer cell lines. [PERSON] et al. [21] analyzed histopathology slides, RNA-Seq, and proteomic data from 587 patients with primary serous ovarian adenocarcinoma who participated in The Cancer Genome Atlas (TCGA) and Genomic Data Commons studies [22]. They developed a systematic algorithm to integrate histopathology and functional omics findings and predict patient responses to platinum-based chemotherapy. To reduce the risk of overfitting, the architectures AlexNet [23], GoogLeNet [24], and VGGNet [25] pretrained with weights from the ImageNet dataset were used to create classification models with histopathological images containing tumor cells. [PERSON] and colleagues achieved approximately 95% accuracy in identifying cancerous regions and classified tumor grades with about 80% accuracy. [PERSON] et al. [26] aimed to develop a deep learning model to predict the platinum sensitivity of epithelial ovarian cancer patients based on contrast-enhanced MRI. Their team collected images from 93 patients who underwent contrast-enhanced MRI and received cisplatin/carboplatin chemotherapy at Sun Yat-sen Memorial Hospital. In total, 62 images were used as the training group and 31 images were set aside for validation. Two different deep learning models were utilized: the first, a primary tumor model, used tumor images obtained from volume of interest (VOI) segmentation of the entire image; the second used the entire abdominal images fed into the architecture of a pretrained 3D ResNet network. After extracting features from the relevant VOIs, Principal Component Analysis (PCA) was applied to reduce data dimensions. A support vector machine was then used on the PCA-generated data to classify the likelihood of cisplatin sensitivity, demonstrating that the full abdominal model performed better than the primary tumor model [26]. Studies have shown that convolutional neural networks (CNNs) can accurately identify cancerous regions in ovarian tissues with high AUC values and effectively classify tumor grades [27]. [PERSON] et al. [28] conducted a study to analyze the feature extraction performance of deep structured algorithms in diagnosing lung nodules on computer tomography (CT) images and compared it to computer-aided diagnostic (CADx) systems. From 1018 cases of diagnostic and lung cancer screening thoracic CT scans containing marked and annotated lesions, they generated three images for each segmented grayscale ROI fragment: the original ROI containing areas surrounding nodules, the nodule-only image, and a degraded image. These were combined into a Red-Green-Blue (RGB) channel resulting in 134,668 samples. They extracted features by designing CNN, deep belief network (DBN), and stacked denoising auto-encoder (SDAE) algorithms and created a new three-channel image generation method for nodule diagnosis. They also compared AlexNet-based transfer learning in this experiment by training the last 1-3 layers. [PERSON] et al. [28] concluded that compared to traditional CADx systems, deep learning algorithms did not exhibit biases observed in misclassified data. They analyzed that the deep learning-based computerized lung nodule diagnostic scheme could distinguish malignant from benign lung nodules with a relatively higher AUC of 0.899 \(\pm\) 0.018.