Implications for high-precision dose radiation therapy planning or limited surgical resection after percutaneous computed tomography-guided lung nodule biopsy using a tract sealant

Purpose: Precision radiation therapy such as stereotactic body radiation therapy and limited resection are being used more frequently to treat intrathoracic malignancies. Effective local control requires precise radiation target delineation or complete resection. Lung biopsy tracts (LBT) on computed tomography (CT) scans after the use of tract sealants can mimic malignant tract seeding (MTS) and it is unclear whether these LBTs should be included in the calculated tumor volume or resected. This study evaluates the incidence, appearance, evolution, and malignant seeding of LBTs. Methods and materials: A total of 406 lung biopsies were performed in oncology patients using a tract sealant over 19 months. Of these patients, 326 had follow-up CT scans and were included in the study group. Four thoracic radiologists retrospectively analyzed the imaging, and a pathologist examined 10 resected LBTs. Results: A total of 234 of 326 biopsies (72%, including primary lung cancer [n = 98]; metastases [n = 81]; benign [n = 50]; and nondiagnostic [n = 5]) showed an LBT on CT. LBTs were identified on imaging 0 to 3 months after biopsy. LBTs were typically straight or serpiginous with a thickness of 2 to 5 mm. Most LBTs were unchanged (92%) or decreased (6.3%) over time. An increase in LBT thickness/nodularity that was suspicious for MTS occurred in 4 of 234 biopsies (1.7%). MTS only occurred after biopsy of metastases from extrathoracic malignancies, and none occurred in patients with lung cancer. Conclusions: LBTs are common on CT after lung biopsy using a tract sealant. MTS is uncommon and only occurred in patients with extrathoracic malignancies. No MTS was found in patients with primary lung cancer. Accordingly, potential alteration in planned therapy should be considered only in patients with LBTs and extrathoracic malignancies being considered for stereotactic body radiation therapy or wedge resection

Preoperative Chemo-Radiation-Induced Ulceration in Patients with Esophageal Cancer: A Confounding Factor in Tumor Response Assessment in Integrated Computed Tomographic-Positron Emission Tomographic Imaging

HypothesisPositron emission tomography can be useful in predicting response of esophageal cancer after preoperative chemo-radiation therapy (CRT). We evaluated the use of integrated computed tomography (CT)-PET among patients with esophageal cancer being considered for resection after CRT.MethodsThree reviewers blinded to clinical and pathologic staging retrospectively reviewed the CT-PET scans of patients with esophageal cancer after preoperative CRT who underwent esophagectomy. [18F]-fluoro-2-deoxy-D-glucose uptake for residual malignancy was determined by visual analysis and semi-quantitatively when standardized uptake value (SUV) was ≥4.ResultsForty-two patients underwent esophageal resection. Using visual analysis, CT-PET had a sensitivity of 47% and specificity of 58% in detecting residual malignancy. Using semi-quantitative analysis, 19 patients had a SUV ≥4 in the region of the primary esophageal tumor and were interpreted as having residual malignancy (sensitivity 43%, specificity 50%). Of these 19, six had complete pathologic response to CRT. These false-positive results, due to therapy-induced ulceration detected at endoscopy, limit the use of CT-PET alone in detecting residual malignancy. Similarly, sensitivity (25%) and specificity (73%) of endoscopy/biopsy in detecting residual malignancy were poor. However, the accuracy of CT-PET in detecting residual malignancy was improved when combined with endoscopic findings. In the absence of ulceration at endoscopy, 8 of 8 patients with SUV ≥4 after chemo-radiation had residual malignancy at surgery.ConclusionsCRT-induced ulceration results in false-positive results on CT-PET and precludes accurate detection of residual esophageal tumor. However, CT-PET in combination with endoscopy is useful in identifying patients with a high risk of residual tumor post-CRT

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19

Objectives: Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population. Methods: We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction. Results: These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts. Conclusions: Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population