Patra, Anurima

Purpose This study aimed to compare accuracy and efficiency of a convolutional neural network (CNN)–enhanced workflow for pancreas segmentation versus radiologists in the context of interreader reliability. Methods Volumetric pancreas segmentations on a data set of 294 portal venous computed tomographies were performed by 3 radiologists (R1, R2, and R3) and by a CNN. Convolutional neural network segmentations were reviewed and, if needed, corrected (“corrected CNN [c-CNN]” segmentations) by radiologists. Ground truth was obtained from radiologists' manual segmentations using simultaneous truth and performance level estimation algorithm. Interreader reliability and model's accuracy were evaluated with Dice-Sorenson coefficient (DSC) and Jaccard coefficient (JC). Equivalence was determined using a two 1-sided test. Convolutional neural network segmentations below the 25th percentile DSC were reviewed to evaluate segmentation errors. Time for manual segmentation and c-CNN was compared. Results Pancreas volumes from 3 sets of segmentations (manual, CNN, and c-CNN) were noninferior to simultaneous truth and performance level estimation–derived volumes [76.6 cm3 (20.2 cm3), P < 0.05]. Interreader reliability was high (mean [SD] DSC between R2-R1, 0.87 [0.04]; R3-R1, 0.90 [0.05]; R2-R3, 0.87 [0.04]). Convolutional neural network segmentations were highly accurate (DSC, 0.88 [0.05]; JC, 0.79 [0.07]) and required minimal-to-no corrections (c-CNN: DSC, 0.89 [0.04]; JC, 0.81 [0.06]; equivalence, P < 0.05). Undersegmentation (n = 47 [64%]) was common in the 73 CNN segmentations below 25th percentile DSC, but there were no major errors. Total inference time (minutes) for CNN was 1.2 (0.3). Average time (minutes) taken by radiologists for c-CNN (0.6 [0.97]) was substantially lower compared with manual segmentation (3.37 [1.47]; savings of 77.9%–87% [P < 0.0001]). Conclusions Convolutional neural network–enhanced workflow provides high accuracy and efficiency for volumetric pancreas segmentation on computed tomography.

Prostate‐specific membrane antigen (PSMA) is a validated target for molecular diagnostics and targeted radionuclide therapy. Our purpose was to evaluate PSMA expression in hepatocellular carcinoma (HCC), cholangiocarcinoma (CCA), and hepatic adenoma (HCA); investigate the genetic pathways in HCC associated with PSMA expression; and evaluate HCC detection rate with 68Ga‐PSMA‐11 positron emission tomography (PET). In phase 1, PSMA immunohistochemistry (IHC) on HCC (n = 148), CCA (n = 111), and HCA (n = 78) was scored. In a subset (n = 30), messenger RNA (mRNA) data from the Cancer Genome Atlas HCC RNA sequencing were correlated with PSMA expression. In phase 2, 68Ga‐PSMA‐11 PET was prospectively performed in patients with treatment‐naïve HCC on a digital PET scanner using cyclotron‐produced 68Ga. Uptake was graded qualitatively and semi‐quantitatively using standard metrics. On IHC, PSMA expression was significantly higher in HCC compared with CCA and HCA (P < 0.0001); 91% of HCCs (n = 134) expressed PSMA, which principally localized to tumor‐associated neovasculature. Higher tumor grade was associated with PSMA expression (P = 0.012) but there was no association with tumor size (P = 0.14), fibrosis (P = 0.35), cirrhosis (P = 0.74), hepatitis B virus (P = 0.31), or hepatitis C virus (P = 0.15). Overall survival tended to be longer in patients without versus with PSMA expression (median overall survival: 4.2 vs. 1.9 years; P = 0.273). FGF14 (fibroblast growth factor 14) mRNA expression correlated positively (rho = 0.70; P = 1.70 × 10‐5) and MAD1L1 (Mitotic spindle assembly checkpoint protein MAD1) correlated negatively with PSMA expression (rho = −0.753; P = 1.58 × 10‐6). Of the 190 patients who met the eligibility criteria, 31 patients with 39 HCC lesions completed PET; 64% (n = 25) lesions had pronounced 68Ga‐PSMA‐11 standardized uptake value: SUVmax (median [range] 9.2 [4.9‐28.4]), SUVmean 4.7 (2.4‐12.7), and tumor‐to‐liver background ratio 2 (1.1‐11). Conclusion: Ex vivo expression of PSMA in neovasculature of HCC translates to marked tumor avidity on 68Ga‐PSMA‐11 PET, which suggests that PSMA has the potential as a theranostic target in patients with HCC.

Abstract Purpose: Around 30% of PDAC less than 2-cm tend to go undetected on CT due to their subtle imaging signatures. Automated detection of PDAC using AI represents an opportunity to augment physician expertise and to improve outcomes through early detection of PDAC. Our purpose was to develop a 3D-CNN for fully automated detection of PDAC and to further evaluate the impact of inclusion of pancreas segmentation on the accuracy of this 3D-CNN. Methods: A Medical Imaging Data Readiness Scale (MIDaR) level A dataset (portal venous phase CTs, slice thickness ≤ 3.75 mm) of 466 treatment-naïve biopsy-proven PDAC and 1994 subjects with normal pancreas was created after exclusion of CTs with suboptimal image quality or biliary stents. Volumetric pancreas and tumor segmentations on CTs were done by two radiologists using 3D Slicer. A total of 370 CTs with PDAC and 370 CTs with normal pancreas were randomly selected for separate training and validation sets, and 396 CTs (96 CTs with PDAC and 300 CTs with normal pancreas) were utilized for testing. Two separate 3D-CNNs were trained. A three-stage bounding-box-only model (A): stage 1 was based on a UNET-like architecture and localized the pancreas on CT with a bounding box; stage 2 utilized an Inception ResNet architecture and classified each slice through the pancreas into PDAC vs. normal; and stage 3 utilized the output of stage 2 to generate final classification for a given CT. Conversely, a four-stage pancreas segmentation-based model (B) included stage 1 of model A followed by an additional stage of automated pancreas and tumor segmentation (stage 2), classification of each slice through the pancreas into PDAC vs. normal (stage 3) and, finally, generation of final classification score (stage 4) for a given CT. Area under the receiver operating characteristic curve (AUROC) of the two models were compared on the test set. Results: Mean (SD) PDAC diameter in the test set was 1.1 (0.43) cm. Model A (three-stage bounding-box-only) correctly classified 305 (77%) out of 396 CTs from the test set into PDAC vs. normal. It incorrectly classified 12/96 (12.5%) CTs with PDAC as normal and 79/300 (26%) normal CTs as PDAC. AUROC for model A was 0.85. Model B (four-stage pancreas segmentation-based) correctly classified 351 (88%) out of 396 CTs. It incorrectly classified 13/96 (13.5%) CTs with PDAC as normal and 32/300 (10.7%) normal CTs as PDAC. AUROC for model B was 0.94. AUROC for model B was significantly higher than model A (p&lt;0.005). Conclusion: A 3D-CNN can detect small PDAC with high accuracy using automated localization of pancreas with a bounding box without relying on separate pancreas segmentation. Inclusion of an additional automated pancreas segmentation step reduced false positives with consequent incremental gain in the model’s accuracy. Prospective validation and subsequent integration of such models into clinical workflows has the potential to reduce inadvertent errors in detection of subtle or small PDAC on standard-of-care CT scans. Citation Format: Anurima Patra, Korfiatis Panagiotis, Garima Suman, Ananya Panda, Sushil Kumar Garg, Ajit Goenka. Automated detection of pancreatic ductal adenocarcinoma (PDAC) on CT scans using artificial intelligence (AI): Impact of inclusion of automated pancreas segmentation on the accuracy of 3D-convolutional neural network (CNN) [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-084.