Efficacy of percutaneous image-guided biopsy for diagnosis of intrahepatic cholangiocarcinoma.

PURPOSE: To evaluate the efficacy of percutaneous biopsy for diagnosing intrahepatic cholangiocarcinoma (IHCCA). METHODS: Retrospective review of biopsy and pathology databases from 2006 to 2019 yielded 112 patients (54F/58 M; mean age, 62.9 years; 27 cirrhotic) with IHCCA who underwent percutaneous biopsy. Data regarding the lesion, biopsy procedure technique, and diagnostic yield were collected. If biopsy was non-diagnostic or discordant with imaging, details of repeat biopsy or resection/explant were gathered. A control group of 100 consecutive patients (56F/44 M; mean age, 63 years, 5 cirrhotic) with focal liver lesions > 1 cm was similarly assessed. RESULTS: Mean IHCCA lesion size was 6.1 ± 3.6 cm, with dominant lesion sampled in 78% (vs. satellite in 22%). 95% (n = 106) were US guided and 96% were core biopsies (n = 108), typically 18G (n = 102, 91%), median 2 passes. 18 patients (16%) had discordant/ambiguous pathology results requiring repeat biopsy, with two patients requiring 3-4 total attempts. A 4.4% minor complication rate was seen. Mean time from initial biopsy to final diagnosis was 60 ± 120 days. Control group had mean lesion size of 2.9 ± 2.5 cm and showed a non-diagnostic rate of 3.3%, both significantly lower than that seen with CCA, with average time to diagnosis of 21 ± 28.8 days (p = 0.002, p = 0.001). CONCLUSION: IHCCA is associated with lower diagnostic yield at initial percutaneous biopsy, despite larger target lesion size. If a suspicious lesion yields a biopsy result discordant with imaging, the radiologist should recommend prompt repeat biopsy to prevent delay in diagnosis.

Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection.

Purpose To determine the feasibility of using a deep learning approach to detect cartilage lesions (including cartilage softening, fibrillation, fissuring, focal defects, diffuse thinning due to cartilage degeneration, and acute cartilage injury) within the knee joint on MR images. Materials and Methods A fully automated deep learning-based cartilage lesion detection system was developed by using segmentation and classification convolutional neural networks (CNNs). Fat-suppressed T2-weighted fast spin-echo MRI data sets of the knee of 175 patients with knee pain were retrospectively analyzed by using the deep learning method. The reference standard for training the CNN classification was the interpretation provided by a fellowship-trained musculoskeletal radiologist of the presence or absence of a cartilage lesion within 17 395 small image patches placed on the articular surfaces of the femur and tibia. Receiver operating curve (ROC) analysis and the κ statistic were used to assess diagnostic performance and intraobserver agreement for detecting cartilage lesions for two individual evaluations performed by the cartilage lesion detection system. Results The sensitivity and specificity of the cartilage lesion detection system at the optimal threshold according to the Youden index were 84.1% and 85.2%, respectively, for evaluation 1 and 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively, indicating high overall diagnostic accuracy for detecting cartilage lesions. There was good intraobserver agreement between the two individual evaluations, with a κ of 0.76. Conclusion This study demonstrated the feasibility of using a fully automated deep learning-based cartilage lesion detection system to evaluate the articular cartilage of the knee joint with high diagnostic performance and good intraobserver agreement for detecting cartilage degeneration and acute cartilage injury. © RSNA, 2018 Online supplemental material is available for this article .