  A Pathology report is arguably one of the most important documents in
medicine containing interpretive information about the visual findings from the
patient's biopsy sample. Each pathology report has a retention period of up to
20 years after the treatment of a patient. Cancer registries process and encode
high volumes of free-text pathology reports for surveillance of cancer and
tumor diseases all across the world. In spite of their extremely valuable
information they hold, pathology reports are not used in any systematic way to
facilitate computational pathology. Therefore, in this study, we investigate
automated machine-learning techniques to identify/predict the primary diagnosis
(based on ICD-O code) from pathology reports. We performed experiments by
extracting the TF-IDF features from the reports and classifying them using
three different methods---SVM, XGBoost, and Logistic Regression. We constructed
a new dataset with 1,949 pathology reports arranged into 37 ICD-O categories,
collected from four different primary sites, namely lung, kidney, thymus, and
testis. The reports were manually transcribed into text format after collecting
them as PDF files from NCI Genomic Data Commons public dataset. We subsequently
pre-processed the reports by removing irrelevant textual artifacts produced by
OCR software. The highest classification accuracy we achieved was 92\% using
XGBoost classifier on TF-IDF feature vectors, the linear SVM scored 87\%
accuracy. Furthermore, the study shows that TF-IDF vectors are suitable for
highlighting the important keywords within a report which can be helpful for
the cancer research and diagnostic workflow. The results are encouraging in
demonstrating the potential of machine learning methods for classification and
encoding of pathology reports.
