Breast cancer staging using Natural Language Processing

Abstract

Medical diagnostic reports archived as electronic forms are valuable resources for processing to understand retrospectively, the severity of the disease among patients and to verify the correctness of the diagnosis. In this work, Breast Cancer Pathology reports are processed using Natural Language Processing (NLP) and Information Extraction (IE) techniques in order to extract the parameters required for cancer staging namely Tumour (T), Lymph nodes (N) and Metastases (M). An automated system is developed to process the 'Impression' section of the report, classify T and N using pTNM classification protocol of American Joint Committee on Cancer (AJCC) and derive the stage S of cancer of patients. T and N are classified using numerical parameters and non-numeric medical conditions given in the natural language text. Metastases M which is not evident from Pathology reports is given a default value of M0 for staging. The dataset consisting of 150 de-identified reports were reviewed by the Pathologists to obtain the Gold standard for evaluation. The TNM classification and the cancer stage derived by the system were evaluated against the Gold standard and discrepancy reports were generated. The extraction process was then fine-tuned based on the recommendations of the domain experts.The automatic staging process had an average percentage of 73 for Precision, 82 for Recall, 59 for Specificity and 72 for Accuracy. The lack of high performance is due to presence of certain vital information in other sections of the report that are not processed. Processing these sections in future would improve the performance.

Natural Language Processing for Breast Imaging: A Systematic Review

“…The American Joint Committee on Cancer (AJCC) references were used to classify each category and then group into stages based on a staging protocol. This automatic staging process demonstrated a precision of 73%, 82% recall, 59% specificity, and 72% accuracy [ 2 ]. The low performance of this NLP model was attributed to the presence of certain vital information within the reports that the NLP was not built to consider.…”

“…NLP technologies offer promising solutions for assisting physicians, including radiologists, in performing various clinical tasks [ 1 , 2 , 3 , 4 ]. It has the ability to greatly impact decision support and utilization by guiding physicians towards optimal workups based on the vast amount of information contained within medical records, including specific clinical circumstances such as risk factors.…”

Transforming Thyroid Cancer Diagnosis and Staging Information from Unstructured Reports to the Observational Medical Outcome Partnership Common Data Model

“…Notes from radiology, pathology, and colonoscopy reports were most frequently used to extract clinical information for diagnostic reports. 2 In cancer research, NLP has been applied to obtain tumor characteristics, chemotherapy regimens, radiotherapy regimens, and operations, mainly in lung cancer, 3 breast cancer, 4 5 6 7 pulmonary cancer, 8 and colon cancer. 9 Free-text clinical notes on thyroid cancer have remained to be investigated with NLP.…”

The promise of natural language processing in healthcare

“…While we were not able to find any description of current NLP-enabled chatbots in healthcare settings, NLP tools have already been successfully implemented in analyzing clinical text, indicating that NLP-enabled chatbots could handle healthcare-related text. [11][12][13] NLP-enabled chatbots have already been successfully implemented by companies such as Lufthansa, BMW, and Panasonic to deliver customer and technical support. 14 While consumer support and healthcare provision have fundamental differences, important lessons for the development of NLP-enabled chatbots in healthcare can be gleaned from consumer industries.…”