Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence

Lyu, Peijie; Neely, Ben; Solomon, Justin; Rigiroli, Francesca; Ding, Yuqin; Schwartz, Fides R.; Thomsen, Brian; Lowry, Carolyn; Samei, Ehsan; Marin, Daniele

doi:10.1016/j.ejrad.2021.109825

Cited by 24 publications

(20 citation statements)

References 31 publications

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“…11 In addition, DLIR at high strength levels was associated with the highest interreader agreement and reader confidence. 11…”

Section: Image and Data Acquisitionmentioning

confidence: 92%

“…11 In addition, DLIR at high strength levels was associated with the highest interreader agreement and reader confidence. 11 Dual-energy CT allows calculation of iodine concentration in tissues, which represents the uptake of iodine in a region of interest and reflects blood supply in tissues. 12 Nagayama et al reported that dual-layer spectral technology improves virtual monoenergetic image (VMI) quality in patients with PDAC.…”

Section: Image and Data Acquisitionmentioning

confidence: 92%

“…11 They found that DLIR at high strength levels allowed better prediction of marginnegative resections (i.e., R0 margins) with area under the curve (AUC) of 0.91, compared to filtered back projection (AUC = 0.75; P = .030) and ASiR-V (AUC = 0.81) (P = .023). 11 In addition, DLIR at high strength levels was associated with the highest interreader agreement and reader confidence. 11 Dual-energy CT allows calculation of iodine concentration in tissues, which represents the uptake of iodine in a region of interest and reflects blood supply in tissues.…”

Section: Image and Data Acquisitionmentioning

confidence: 99%

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Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

et al. 2022

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Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.

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“…11 In addition, DLIR at high strength levels was associated with the highest interreader agreement and reader confidence. 11…”

Section: Image and Data Acquisitionmentioning

confidence: 92%

Section: Image and Data Acquisitionmentioning

confidence: 92%

Section: Image and Data Acquisitionmentioning

confidence: 99%

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Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

et al. 2022

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“…For other applications of AI in CT, Abel et al developed and evaluated an algorithm based on a two-step nnU-Net architecture for automated detection of pancreatic cystic lesions (PCL) in CT 137 . Lyu et al used high strength levels of the DL image reconstruction (DLIR-H) algorithm to predict the resectability of PC 138 . Chang et al extracted radiomics features of CE-CT images by the SVM model and generated a radiomics signature by the LASSO model for the preoperative prediction of histological grades of PDAC.…”

Section: Ai In Tumor Diagnosis Processmentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

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“…A CT structured reporting template is nowadays recommended by many international societies [6,28], as it allows the reduction in the number of missing morphological and vascular features, and the improvement of inter-reader agreement compared to free-text reports [29]. Recently, a deep learning image reconstruction algorithm has been developed for CT assignment of the local resectability of PDAC with good results [30].…”

Section: Role Of Radiologistmentioning

confidence: 99%

Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma: Role of the Radiologist and Oncologist in the Era of Precision Medicine

Vernuccio

Messina²,

Merz

et al. 2021

Diagnostics

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The incidence and mortality of pancreatic ductal adenocarcinoma are growing over time. The management of patients with pancreatic ductal adenocarcinoma involves a multidisciplinary team, ideally involving experts from surgery, diagnostic imaging, interventional endoscopy, medical oncology, radiation oncology, pathology, geriatric medicine, and palliative care. An adequate staging of pancreatic ductal adenocarcinoma and re-assessment of the tumor after neoadjuvant therapy allows the multidisciplinary team to choose the most appropriate treatment for the patient. This review article discusses advancement in the molecular basis of pancreatic ductal adenocarcinoma, diagnostic tools available for staging and tumor response assessment, and management of resectable or borderline resectable pancreatic cancer.

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Effect of deep learning image reconstruction in the prediction of resectability of pancreatic cancer: Diagnostic performance and reader confidence

Cited by 24 publications

References 31 publications

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances

Artificial intelligence in pancreatic cancer

Resectable and Borderline Resectable Pancreatic Ductal Adenocarcinoma: Role of the Radiologist and Oncologist in the Era of Precision Medicine

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