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Ovarian cancer is one of the three most common types of gynecological cancer globally, with high-grade serous ovarian cancer being the most common and aggressive histological type. Guided treatment of high-grade serous ovarian cancer typically involves platinum-based combination chemotherapy, necessitating the assessment of whether the patient is platinum resistant. This study proposes a deep learning-based method to determine whether a patient is platinum resistant using multimodal positron emission tomography/computed tomography images. In total, 289 patients with high-grade serous ovarian cancer were included in this study. An end-to-end Squeeze-Excitation–Spatial Pyramid Pooling–Dense Convolutional Network model was built by adding a Squeeze-Excitation Block and Spatial Pyramid Pooling Layer to a Dense Convolutional Network. Multimodal data from positron emission tomography/computed tomography images of regions of interest were used to predict platinum resistance in patients. Through five-fold cross-validation, the Squeeze-Excitation–Spatial Pyramid Pooling–Dense Convolutional Network achieved a high accuracy rate and area under the curve of 92.6% and 0.93, respectively, for predicting platinum resistance in patients. The importance of incorporating the Squeeze-Excitation Block and Spatial Pyramid Pooling Layer into the deep learning model and considering multimodal data was substantiated by performing ablation studies and experiments with single-modality data. The classification results indicate that our proposed deep learning framework performs better in predicting platinum resistance in patients, which can help gynecologists make more appropriate treatment decisions.

Presents corrections to the paper, (Corrections to “An Attention-Based Deep Learning Network for Predicting Platinum Resistance in Ovarian Cancer”).

In the recent past, the Classifiers are based on genetic signatures in which many microarray studies are analyzed to predict medical results for cancer patients. However, the Signatures from different studies have been benefitted with low-intensity ratio during the classification of individual datasets has been considered as a significant point of research in the present scenario. Hence to overcome the above-discussed issue, this paper provides a Deep Learning Framework that combines an algorithm of necessary processing of Linear Discriminant Analysis (LDA) and Auto Encoder (AE) Neural Network to classify different features within the profile of gene expression. Hence, an advanced ensemble classification has been developed based on the Deep Learning (DL) algorithm to assess the clinical outcome of breast cancer. Furthermore, numerous independent breast cancer datasets and representations of the signature gene, including the primary method, have been evaluated for the optimization parameters. Finally, the experiment results show that the suggested deep learning frameworks achieve 98.27% accuracy than many other techniques such as genomic data and pathological images with multiple kernel learning (GPMKL), Multi-Layer Perception (MLP), Deep Learning Diagnosis (DLD), and Spatiotemporal Wavelet Kinetics (SWK).