An Overview of Mathematical Models for RNA Sequence-based Glioblastoma Subclassification

Molecular marker-based glioblastoma (GBM) subclassification is emerging as a key factor in personalized GBM treatment planning. Multiple genetic alterations, including methylation status and mutations, have been proposed in GBM subclassification. RNA-Sequence (RNA-Seq)-based molecular profiling of GBM is widely implemented and readily quantifiable. Machine learning (ML) algorithms have been reported as an applicable method that can consistently subgroup GBM. In this study, we systematically studied the applicability of the commonly used ML algorithms based on The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) dataset and cross-validated in the Chinese Glioma Genome Atlas (CGGA) dataset. ML algorithms studied include Binomial and multinomial Logistic Regression, Linear discriminant analysis, Decision trees, K-Nearest Neighbors, Gaussian Naive Bayes, Support Vector Machines, Gradient Boosting, Voting Ensemble, Multi-Layer Perceptron. RNA-Seq data of 44 biomarkers were passed through the algorithms for performance evaluation. We found ML algorithms Support Vector Machines, Multi-Layer Perceptron s, and Voting Ensemble are best equipped in assigning GBM to correct molecular subgroups of GBM without histological studies.

Towards the personalized radiotherapy for elderly and/or frail glioblastoma multiforme patients – radiosensitivity profiling using biomarkers and image analyses

This a commentary to the article titled "Integrating cullin2-RING E3 ligase as a potential biomarker for glioblastoma multiforme prognosis and radiosensitivity profiling" recently published in the journal Radiotherapy and Oncology (https://doi.org/10.1016/j.radonc.2020.09.005).

Cascaded U-net for Kidney and Tumor Segmentation from CT volumes

Contrast-enhanced Computed Tomography (CT) imaging is most useful tool in diagnosing and locating the kidney lesions. An automated kidney and tumor segmentation are very helpful because it can provide the precise information about the location and size of lesions which can be used in quantitative analysis of the tumor. Semantic segmentation of kidney is very challenging as it requires large dataset for training and its morphological heterogeneity makes it a difficult problem. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) has publicly released a 210 cross sectional CT images with kidney tumors along with corresponding semantic segmentation masks. In this work we proposed a novel two stage 2D segmentation method to automatically segment kidney and tumor using the combination of Unet++ and squeeze and excite approach. The proposed network is trained in keras framework. Our method achieves a dice score of 0.98 and 0.965 with kidney and tumor respectively on training data and the results demonstrates the accuracy of our proposed method. Proposed method was able to segment kidney and tumor from abdominal CT images which can provide the exact location and size of the tumor. This information can also be used to analyze treatment response.

Application of artificial intelligence in cancer patient care during COVID-19 pandemic

The SARS-CoV-2 has caused tremendous stress in cancer patient care. Millions of confirmed cases and hundreds of thousands of deaths have been reported since the virus first reported in just 6 months ago in January 2020. While patients with chronic diseases such as hypertension, diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD) are particularly vulnerable to COVID19, cancer patients will probably suffer the most from visits due to weak immune system, frequent hospital visits, complicated treatment regimens, and ever-growing costs of seeking in-person healthcare. In this perspective, we propose a simple workflow based on artificial intelligence (AI) that integrates currently available platforms and resources. The workflow hereby proposed is easy to develop and will promote the entry of the first clinical application of AI in modern medicine.

AI in oncology: when science fiction meets reality

Artificial intelligence has long been a hot topic in the science fiction books and movies. But now it seemingly has stepped into our real life in a variety of forms. In this brief editorial, we discuss the various aspects of AI utilizations in our day-to-day life and foresee its applications in oncology.

Mining Cancer-related Information in Electronic Healthcare Records with Natural Language Processing

Cancer is in the midst of leading causes of death. In 2018, around 1,735,350 new cases of cancer were estimated and 609,640 people will die from cancer in the United States. A wealth of cancer-relevant information is conserved in a variety of types of healthcare records, for example, the electronic health records (EHRs). However, part of the critical information is organized in the free narrative text which hampers machine to interpret the information underlying the text. The development of artificial intelligence provides a variety of solutions to this plight. For example, the technology of natural language processing (NLP) has emerged bridging the gap between free text and structured representation of cancer information. Recently, several researchers have published their work on unearthing cancer-related information in EHRs based on the NLP technology. Apart from the traditional NLP methods, the development of deep learning helps EHRs mining go further.