Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi‐Scale 3D U‐Nets architecture, which uses several U‐net blocks to capture long‐distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state‐of‐the‐art methods.

Small cell lung cancer (SCLC) is one of the most common types of malignant tumors, characterized by rapid growth and early metastasis spread. Early and accurate diagnosis of SCLC is vital for improved survival. Accurate cancer segmentation helps doctors understand the location and size of cancer and make better diagnostic decisions. However, manual segmentation of lung cancers from large amounts of medical images is a time-consuming and challenging task. In this paper, we propose a hybrid segmentation network (referred to as HSN) based on convolutional neural network (CNN) to automatically segment SCLC from computed tomography (CT) images. The design philosophy of our model is to combine a lightweight 3D CNN to learn long-range 3D contextual information and a 2D CNN to learn fine-grained semantic information, which is essential for accurate cancer segmentation. We propose a hybrid features fusion module to effectively fuse the 2D and 3D features and to jointly train these two CNNs. We utilize a generalized Dice loss function to tackle the severe class imbalance problem in data. A dataset consists of 134 CT scans was constructed to evaluate our model. Our model achieved high performances with a mean Dice score of 0.888, a mean sensitivity score of 0.872 and a mean precision of 0.909, outperforming the other state-of-the-art 2D and 3D CNN methods by a large margin. INDEX TERMS Small cell lung cancer, CT, deep convolutional neural network, hybrid features fusion. I. INTRODUCTION Lung cancer is one of the most common types of malignant tumors in the world. It is the second most common cancer among both men and women in the United States [1], the first most common cancer among men and the second most common cancer among women in China [2]. Moreover, lung cancer is the leading cause of cancer death among both men and women in both countries. Small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) are the two main types of lung cancers. SCLC accounts for 15-20% of all lung cancer cases. Compared with NSCLC, SCLC is characterized by high malignancy, rapid progress, early metastasis spread, and poor prognosis, and has a severe impact on the physical and mental health of patients [3]. SCLC can be staged into two categories: limited stage and extensive stage. Being at a specific stage indicates how much cancer has spread through The associate editor coordinating the review of this manuscript and approving it for publication was Shovan Barma.

Background. Adjuvant therapy for cervical cancer (CC) patients with intermediate-risk factors remains controversial. The objectives of the present study are to assess the prognoses of early-stage CC patients with pathological intermediate-risk factors and to provide a reference for adjuvant therapy choice. Materials and Methods. This retrospective study included 481 patients with stage IB-IIA CC. Cox proportional hazards regression analysis, machine learning (ML) algorithms, Kaplan-Meier analysis and the area under the receiver operating characteristic curve (AUC) were used to develop and validate prediction models for disease-free survival (DFS) and overall survival (OS). Results. A total of 35 (7.3%) patients experienced recurrence, and 20 (4.2%) patients died. Two prediction models were built for DFS and OS using clinical information, including age, lymphovascular space invasion, stromal invasion, tumor size, and adjuvant treatment. Patients were divided into high-risk or low-risk groups according to the risk score cutoff value. Kaplan-Meier analysis showed significant differences in DFS (p=0.001) and OS (p=0.011) between the two risk groups. In the traditional Sedlis criteria groups, there were no significant differences in DFS or OS (p >0.05). In the ML-based validation, the best AUCs of DFS at 2 and 5 years were 0.69/0.69, and the best AUCs of OS at 2 and 5 years were 0.88/0.63. Conclusion.Two prognostic assessment models were successfully established, and risk grouping stratified the prognostic risk of CC patients with pathological intermediaterisk factors. Evaluation of long-term survival will be needed to corroborate these findings. The Oncologist 2021;9999:• • Implications for Practice: The Sedlis criteria are intermediate-risk factors used to guide postoperative adjuvant treatment in patients with cervical cancer. However, for patients meeting the Sedlis criteria, the choice of adjuvant therapy remains controversial. Our study developed two prognostic models based on pathological intermediate-risk factors. According to the risk score obtained by the prediction model, patients can be further divided into groups with high or low risk of recurrence and death. The prognostic models developed in this study can be used in clinical practice to stratify prognostic risk and provide more individualized adjuvant therapy choices to early-stage cervical cancer patients.

Purpose. The objectives of our study were to assess the association of radiological imaging and gene expression with patient outcomes in non-small cell lung cancer (NSCLC) and construct a nomogram by combining selected radiomic, genomic, and clinical risk factors to improve the performance of the risk model. Methods. A total of 116 cases of NSCLC with CT images, gene expression, and clinical factors were studied, wherein 87 patients were used as the training cohort, and 29 patients were used as an independent testing cohort. Handcrafted radiomic features and deep-learning genomic features were extracted and selected from CT images and gene expression analysis, respectively. Two risk scores were calculated through Cox regression models for each patient based on radiomic features and genomic features to predict overall survival (OS). Finally, a fusion survival model was constructed by incorporating these two risk scores and clinical factors. Results. The fusion model that combined CT images, gene expression data, and clinical factors effectively stratified patients into low- and high-risk groups. The C-indexes for OS prediction were 0.85 and 0.736 in the training and testing cohorts, respectively, which was better than that based on unimodal data. Conclusions. Combining radiomics and genomics can effectively improve OS prediction for NSCLC patients.