The skeletal region is one of the common sites of metastatic spread of cancer in the breast and prostate. CT is routinely used to measure the size of lesions in the bones. However, they can be difficult to spot due to the wide variations in their sizes, shapes, and appearances. Precise localization of such lesions would enable reliable tracking of interval changes (growth, shrinkage, or unchanged status). To that end, an automated technique to detect bone lesions is highly desirable. In this pilot work, we developed a pipeline to detect bone lesions (lytic, blastic, and mixed) in CT volumes via a proxy segmentation task. First, we used the bone lesions that were prospectively marked by radiologists in a few 2D slices of CT volumes and converted them into weak 3D segmentation masks. Then, we trained a 3D full-resolution nnUNet model using these weak 3D annotations to segment the lesions and thereby detected them. Our automated method detected bone lesions in CT with a precision of 96.7% and recall of 47.3% despite the use of incomplete and partial training data. To the best of our knowledge, we are the first to attempt the direct detection of bone lesions in CT via a proxy segmentation task.
Percutaneous liver ablation is a minimally invasive procedure to treat liver tumors. Postablation images are highly significant as they distinguish normal post-procedure changes from abnormalities, preventing unnecessary retreatment and confirming procedural quality. However, the cancer surveillance imaging reports after the procedure can be numerous and challenging to read. Moreover, annotated data is limited in this setting. In this study we used the cutting-edge large language model Llama 2 to automatically extract critical findings from real-world diagnostic imaging reports without the need of training a new information extraction model. This could potentially automate part of the outcome research and registry construction process, as well as decrease the number of studies needed to review for research purposes. A dataset of 87 full-text reports from 13 patients who underwent percutaneous thermal ablation for pancreatic liver metastases were used to benchmark the capability of Llama 2 for cancer progression finding extraction and classification. We asked Llama 2 to determine whether there is cancer progression within the given report and then classify progression findings into Local Tumor Progression (LTP), Intrahepatic Progression (IHP) and Extrahepatic Progression (EHP). Llama 2 achieved decent performance for detecting progression at study level. The precision is 0.91 and recall is 0.96, with specificity 0.84. However, the classification of progression into LTP, IHP and EHP still needs to be improved.
Universal lesion detection and tagging (ULDT) in CT studies is critical for tumor burden assessment and tracking the progression of lesion status (growth/shrinkage) over time. However, a lack of fully annotated data hinders the development of effective ULDT approaches. Prior work used the DeepLesion dataset (4,427 patients, 10,594 studies, 32,120 CT slices, 32,735 lesions, 8 body part labels) for algorithmic development, but this dataset is not completely annotated and contains class imbalances. To address these issues, in this work, we developed a self-training pipeline for ULDT. A VFNet model was trained on a limited 11.5% subset of DeepLesion (bounding boxes + tags) to detect and classify lesions in CT studies. Then, it identified and incorporated novel lesion candidates from a larger unseen data subset into its training set, and self-trained itself over multiple rounds. Multiple self-training experiments were conducted with different threshold policies to select predicted lesions with higher quality and cover the class imbalances. We discovered that direct self-training improved the sensitivities of over-represented lesion classes at the expense of under-represented classes. However, upsampling the lesions mined during self-training along with a variable threshold policy yielded a 6.5% increase in sensitivity at 4 FP in contrast to self-training without class balancing (72% vs 78.5%) and a 11.7% increase compared to the same self-training policy without upsampling (66.8% vs 78.5%). Furthermore, we show that our results either improved or maintained the sensitivity at 4FP for all 8 lesion classes.
Clear cell renal cell carcinoma (ccRCC) is a common cancer and could result in poor prognosis. Understanding individual tumor immune microenvironment (TIME) in ccRCC patients may predict prognosis and response to therapy. In this work, we explore the concept of using radiomic features extracted from computer tomography (CT) imaging to correlate the TIME measurements from multiplex immunohistochemistry (mIHC) analysis. Since CT imaging has long been the standard for evaluation of RCCs, it has the potential to provide noninvasive approximations of the tissue-based mIHC biomarkers. We selected two biomarkers that were grounded by clinical research: PD-L1 expression and CD8+PD-1+ T cell to CD8+ T cell ratio of the tumor epithelium. Then we extracted these two markers from a preliminary set of 52 patients using automated mIHC analysis. We used Random Forest, AdaBoost and ElasticNet to classify each sample as either expressing high or low levels of these markers. We found the radiomic features can correlate tumor epithelium PD-L1 >5%, PD-L1 >10%, and CD8+PD1+/CD8+ >37% with AUROC 0.75, 0.85 and 0.71, respectively.
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