  Understanding the progression of cancer is crucial for defining treatments
for patients. The objective of this study is to automate the detection of
metastatic liver disease from free-style computed tomography (CT) radiology
reports. Our research demonstrates that transferring knowledge using three
approaches can improve model performance. First, we utilize generic language
models (LMs), pretrained in a self-supervised manner. Second, we use a
semi-supervised approach to train our model by automatically annotating a large
unlabeled dataset; this approach substantially enhances the model's
performance. Finally, we transfer knowledge from related tasks by designing a
multi-task transfer learning methodology. We leverage the recent advancement of
parameter-efficient LM adaptation strategies to improve performance and
training efficiency. Our dataset consists of CT reports collected at Memorial
Sloan Kettering Cancer Center (MSKCC) over the course of 12 years. 2,641
reports were manually annotated by domain experts; among them, 841 reports have
been annotated for the presence of liver metastases. Our best model achieved an
F1-score of 73.8%, a precision of 84%, and a recall of 65.8%.
