Md Rahaman

PET/CT radiomics for non-invasive prediction of immunotherapy efficacy in cervical cancer

Purpose The prediction of immunotherapy efficacy in cervical cancer patients remains a critical clinical challenge. This study aims to develop and validate a deep learning-based automatic tumor segmentation method on PET/CT images, extract texture features from the tumor regions in cervical cancer patients, and investigate their correlation with PD-L1 expression. Furthermore, a predictive model for immunotherapy efficacy will be constructed. Methods We retrospectively collected data from 283 pathologically confirmed cervical cancer patients who underwent 18 F-FDG PET/CT examinations, divided into three subsets. Subset-I (n = 97) was used to develop a deep learning-based segmentation model using Attention-UNet and region-growing methods on co-registered PET/CT images. Subset-II (n = 101) was used to explore correlations between radiomic features and PD-L1 expression. Subset-III (n = 85) was used to construct and validate a radiomic model for predicting immunotherapy response. Results Using Subset-I, a segmentation model was developed. The segmentation model achieved optimal performance at the 94th epoch with an IoU of 0.746 in the validation set. Manual evaluation confirmed accurate tumor localization. Sixteen features demonstrated excellent reproducibility (ICC > 0.75). Using Subset-II, PD-L1–correlated features were extracted and identified. In Subset-II, 183 features showed significant correlations with PD-L1 expression (P < 0.05).Using these features in Subset-III, a predictive model for immunotherapy efficacy was constructed and evaluated. In Subset-III, the SVM-based radiomic model achieved the best predictive performance with an AUC of 0.935. Conclusion We validated, respectively in Subset-I, Subset-II, and Subset-III, that deep learning models incorporating medical prior knowledge can accurately and automatically segment cervical cancer lesions, that texture features extracted from 18 F-FDG PET/CT are significantly associated with PD-L1 expression, and that predictive models based on these features can effectively predict the efficacy of PD-L1 immunotherapy. This approach offers a non-invasive, efficient, and cost-effective tool for guiding individualized immunotherapy in cervical cancer patients and may help reduce patient burden, accelerate treatment planning.

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset.