In the era of big data, text-based medical data, such as electronic health records (EHR) and electronic medical records (EMR), are growing rapidly. EHR and EMR are collected from patients to record their basic information, lab tests, vital signs, clinical notes, and reports. EHR and EMR contain the helpful information to assist oncologists in computer-aided diagnosis and decision making. However, it is time consuming for doctors to extract the valuable information they need and analyze the information from the EHR and EMR data. Recently, more and more research works have applied natural language processing (NLP) techniques, i.e., rule-based, machine learning-based, and deep learning-based techniques, on the EHR and EMR data for computer-aided diagnosis in oncology. The objective of this review is to narratively review the recent progress in the area of NLP applications for computer-aided diagnosis in oncology. Moreover, we intend to reduce the research gap between artificial intelligence (AI) experts and clinical specialists to design better NLP applications. We originally identified 295 articles from the three electronic databases: PubMed, Google Scholar, and ACL Anthology; then, we removed the duplicated papers and manually screened the irrelevant papers based on the content of the abstract; finally, we included a total of 23 articles after the screening process of the literature review. Furthermore, we provided an in-depth analysis and categorized these studies into seven cancer types: breast cancer, lung cancer, liver cancer, prostate cancer, pancreatic cancer, colorectal cancer, and brain tumors. Additionally, we identified the current limitations of NLP applications on supporting the clinical practices and we suggest some promising future research directions in this paper. 

/pdf/natural-language-processing-applications-for-computer-aided-1vvv47l9.pdf

Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology

Automatic clinical coding is an essential task in the process of extracting relevant information from unstructured documents contained in electronic health records (EHRs). However, most research in the development of computer-based methods for clinical coding focuses on texts written in English due to the limited availability of medical linguistic resources in languages other than English. With nearly 500 million native speakers, there is a worldwide interest in processing healthcare texts in Spanish. In this study, we systematically analyzed transformer-based models for automatic clinical coding in Spanish. Using a transfer-learning-based approach, the three existing transformer architectures that support the Spanish language, namely, multilingual BERT (mBERT), BETO and XLM-RoBERTa (XLM-R), were first pretrained on a corpus of real-world oncology clinical cases with the goal of adapting transformers to the particularities of Spanish medical texts. The resulting models were fine-tuned on three distinct clinical coding tasks, following a multilabel sentence classification strategy. For each analyzed transformer, the domain-specific version outperformed the original general domain model across those tasks. Moreover, the combination of the developed strategy with an ensemble approach leveraging the predictive capacities of the three distinct transformers yielded the best obtained results, with MAP scores of 0.662, 0.544 and 0.884 on CodiEsp-D, CodiEsp-P and Cantemist-Coding shared tasks, which remarkably improved the previous state-of-the-art performance by 11.6%, 10.3% and 4.4%, respectively. We publicly release the mBERT, BETO and XLMR transformers adapted to the Spanish clinical domain at https://github.com/guilopgar/ClinicalCodingTransformerES , providing the clinical natural language processing community with advanced deep learning methods for performing medical coding and other tasks in the Spanish clinical domain.

/pdf/transformers-for-clinical-coding-in-spanish-2makjw35au.pdf

Transformers for Clinical Coding in Spanish

Antiangiogenic therapy for patients with aggressive or refractory advanced non-small cell lung cancer in the second-line setting

Exosomal microRNAs (miRNAs) have great potentials as a novel biomarker to predict lung cancer. We applied a miRNA microarray to identify aberrantly expressed serum exosomal miRNAs as candidate biomarkers for patients with lung adenocarcinoma (LUAD). Compared with the normal control, 31 exosomal miRNAs were found to be upregulated and 29 exosomal miRNAs were downregulated in the serum of LUAD respectively. Then, 10 dysregulated exosomal miRNAs expression levels in serum were further validated via qRT-polymerase chain reaction. Notably, exosomal miR-7977 was highest expressed and miR-98-3p was lowest expressed in the patients with LUAD, and exosomal miR-7977 showed significant correlation with the N stage and TNM stage with patients with LUAD (P < .05). Receiver operating characteristic curve showed that the abundant level of exosomal miR-7977 may predict LUAD with an area of under the curve (AUC) of 0.787. In comparison with exosomal miR-7977, exosomal miR-98-3p had a smaller area (0.719). The combination of exosomal miR-7977 and miR-98-3p improved the AUC to 0.816. Furthermore, in vitro experiments revealed that inhibition of miR-7977 enhanced the proliferation, invasion, and inhibited apoptosis in A549 cells, the opposite results were performed by miR-7977 mimics. In conclusion, exosomal miR-7977 was identified as a novel biomarker for patients with LUAD and may play as a tumor suppressor in lung cancer.

Serum exosomal miR-7977 as a novel biomarker for lung adenocarcinoma.

This is a review on the use of artificial intelligence for digital breast pathology. A systematic search on PubMed was conducted, identifying 17,324 research papers related to breast cancer pathology. Following a semimanual screening, 664 papers were retrieved and pursued. The papers are grouped into six major tasks performed by pathologists—namely, molecular and hormonal analysis, grading, mitotic figure counting, ki‐67 indexing, tumour‐infiltrating lymphocyte assessment, and lymph node metastases identification. Under each task, open‐source datasets for research to build artificial intelligence (AI) tools are also listed. Many AI tools showed promise and demonstrated feasibility in the automation of routine pathology investigations. We expect continued growth of AI in this field as new algorithms mature.

Artificial intelligence in breast cancer histopathology

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

The vascular endothelial growth factor (VEGF) and receptor is a therapeutic target because of the importance of this pathway in carcinogenesis. This pathway regulates and promotes angiogenesis as well as increases endothelial cell proliferation, permeability, and cancer survival. Ramucirumab is a new fully human monoclonal antibody that targets the VEGF receptor-2, an important key receptor implicated in angiogenesis. Ramucirumab has been approved for the treatment of second-line advanced or metastatic non-small cell lung cancer (NSCLC) in combination with the chemotherapy agent docetaxel. This was based on the result of the randomized trial REVEL of 1,253 patients with metastatic NSCLC previously treated with a platinum-based combination therapy. The authors observed a significant improvement in overall survival (OS) with an acceptable toxicities profile. In this study, patients were randomized to receive ramucirumab plus docetaxel or placebo with docetaxel. The combination of docetaxel and ramucirumab showed an improved OS (hazard ratio [HR]: 0.86; 95% CI: 0.75, 0.98). Median OS was 10.5 months in the ramucirumab arm versus 9.1 months in the placebo arm. Regarding side effects, the toxicity described on the ramucirumab arm were principally diarrhea, fatigue, and neutropenia. The most common (5%) adverse reactions of grade 3 and 4 in the ramucirumab arm were fatigue, neutropenia, febrile neutropenia, leukopenia, and hypertension. Adding ramucirumab to docetaxel improves QoL of patients, and does not impair symptoms or functioning. There are currently several trials in progress evaluating the effects of ramucirumab in combination with other drugs in patients with advanced NSCLC.

/pdf/spotlight-on-ramucirumab-in-the-treatment-of-nonsmall-cell-2s9u0m00ap.pdf

Spotlight on ramucirumab in the treatment of nonsmall cell lung cancer: design, development, and clinical activity.

15Background: Accurate genotyping is mandatory for the management of mCRC pts. Tissue-based testing is still the SoC; however, is not available for all pts. and may be exhausted by serial testing, ...

Clinical utility of comprehensive circulating tumor DNA (ctDNA) testing compared to standard-of-care (SoC) tissue testing in first-line (1L) metastatic colorectal cancer (mCRC) patients (pts).

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Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

Spotlight on ramucirumab in the treatment of nonsmall cell lung cancer: design, development, and clinical activity.

Clinical utility of comprehensive circulating tumor DNA (ctDNA) testing compared to standard-of-care (SoC) tissue testing in first-line (1L) metastatic colorectal cancer (mCRC) patients (pts).