Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence. Issue 141 (August 2021)

• Record Type: Journal Article
• Authors: Lyu, Peijie; Neely, Ben; Solomon, Justin; Rigiroli, Francesca; Ding, Yuqin; Schwartz, Fides Regina; Thomsen, Brian; Lowry, Carolyn; Samei, Ehsan; Marin, Daniele
• Is Part Of: European journal of radiology. Issue 141(2021)
• Publication Date: 2021-08
• Subjects: ASiR-V hybrid model-based adaptive statistical iterative reconstruction -- DLIR deep learning image reconstruction -- FBP filtered back projection -- NPS noise power spectrum -- MTF modulation transfer function -- MDCT multidetector computed tomography -- IR iterative reconstruction; Multidetector computed tomography -- Image processing -- Computer-assisted -- Deep learning -- Pancreatic neoplasms; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109825
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Deep learning image reconstruction (DLIR) overcomes the limitation of iterative reconstruction in noise texture and low-contrast spatial resolution. DLIR can improve the performance and confidence of predicting the resectability of pancreatic cancer and reduce the interreader variability. DLIR optimizes the tradeoff between high spatial resolution and low image noise in thin-slice CT images. The high-strength DLIR provided the most balanced image in terms of image quality, diagnostic performance and reader confidence. Abstract: Objective: To assess the diagnostic performance and reader confidence in determining the resectability of pancreatic cancer at computed tomography (CT) using a new deep learning image reconstruction (DLIR) algorithm. Methods: A retrospective review was conduct of on forty-seven patients with pathologically confirmed pancreatic cancers who underwent baseline multiphasic contrast-enhanced CT scan. Image data sets were reconstructed using filtered back projection (FBP), hybrid model-based adaptive statistical iterative reconstruction (ASiR-V) 60 %, and DLIR "TrueFidelity" at low(L), medium(M), and high strength levels(H). Four board-certified abdominal radiologists reviewed the CT images and classified cancers as resectable, borderline resectable, or unresectable. Diagnostic performance and reader confidence for categorizing the resectability of pancreatic cancer were evaluated based on the reference standards, and the interreader agreement was assessed using Fleiss k statistics. Results: For prediction of margin-negative resections(ie, R0), the average area under the receiver operating characteristic curve was significantly higher with DLIR-H (0.91; 95 % confidence interval [CI]: 0.79, 0.98) than FBP (0.75; 95 % CI:0.60, 0.86) and ASiR-V (0.81; 95 % CI:0.67, 0.91) (p = 0.030 and 0.023 respectively). Reader confidence scores were significantly better using DLIR compared to FBP and ASiR-V 60 % and increased linearly with the increase of DLIR strength level (all p < 0.001). Among the image reconstructions, DLIR-H showed the highest interreader agreement in the resectability classification and lowest subject variability in the reader confidence. Conclusions: The DLIR-H algorithm may improve the diagnostic performance and reader confidence in the CT assignment of the local resectability of pancreatic cancer while reducing the interreader variability.