Pitfalls in the MDCT of pancreatic cancer: strategies for minimizing errors

Haj‐Mirzaian, Arya; Kawamoto, Satomi; Zaheer, Atif; Hruban, Ralph H.; Fishman, Elliot K.; Chu, Linda C.

doi:10.1007/s00261-019-02390-9

Cited by 18 publications

(10 citation statements)

References 133 publications

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“…One study found that 76% of small PDACs have secondary changes, with 63% involving the main pancreatic duct or common bile duct, 63% showing an abrupt pancreatic duct cut-off, 21% showing parenchymal atrophy, and 14% showing contour abnormalities [13]. Our findings are similar; of the 24 CT cases with discrepant reads for pancreatic cancer, 66% of the cases showed dilated pancreatic duct.…”

Section: Discussionsupporting

confidence: 74%

“…Detecting small, resectable PDAC is the only known cure for PDAC. Studies have shown that the majority of PDACs at the time of diagnosis have progressed past the point of resectability, resulting in a poor prognosis for the patient [7,[12][13][14]. This fact is supported by the report from the American Cancer Society that the 5-year survival rate for PDAC is 39% if localized, but only 13% in the presence of regional spread [15].…”

Section: Discussionmentioning

confidence: 98%

“…CT offers a lower cost than other techniques with wider availability compared to modalities such as MRI and EUS [14]. Additionally, CT is more accurate in determining tumor resectability (73-87%) than MRI (70-79%) [13].…”

Section: Discussionmentioning

confidence: 99%

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High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis

Leonard¹,

Lemme²,

Kral³

et al. 2021

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Objective: Pancreatic ductal adenocarcinoma (PDAC) has the best survival when detected early with 5-year survival near 40% for small, resectable PDAC. We evaluate the undiagnosed PDAC imaging features on routine CT and their impact on resectability. Methods: 76 of the screened 134 CTs from 1/1/2012 to 12/31/2018 using our tumor registry were obtained prior to PDAC diagnosis for other indications at least one month before presentation. Each cross-sectional study was reviewed for features of early PDAC: pancreatic mass, pancreatic ductal dilatation, perivascular/peripancreatic soft-tissue infiltration, omental lesions/ascites, and lymphadenopathy. When such features were detectible by the reviewing radiologists, the original CT readings were classified as concordant/discrepant. Descriptive statistics are reported for discrepant reads, tumor resectability, and tumor size. Results: Of the 76 cases from 46 unique subjects (30 male/16 female), 25 CTs (33%) had undetected PDAC imaging features: masses (15/19 unreported), ductal dilatation (16/20 unreported), and peripancreatic/perivascular soft-tissue infiltration (20/36 unreported). 63% of early PDAC features were not identified initially. One year before clinical diagnosis, 75-80% of the PDAC cases were resectable; at < 6 months before clinical diagnosis, only 29% were resectable. Conclusion: Improving early detection of key PDAC features on routine CT examinations can potentially improve patient outcomes.

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Section: Discussionsupporting

confidence: 74%

Section: Discussionmentioning

confidence: 98%

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High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis

Leonard¹,

Lemme²,

Kral³

et al. 2021

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“…However, radiologists encounter various types of pancreatic tumours in clinical practice, and non-neoplastic conditions such as pancreatitis can simulate the appearance of pancreatic tumors. 9,10 Additional training will be necessary to distinguish pancreatic cancer from these other pathologies to improve the clinical utility of the model. Artificial intelligence will certainly revolutionise the practice of radiology within the next decade.…”

mentioning

confidence: 99%

Deep learning for pancreatic cancer detection: current challenges and future strategies

Chu

Fishman

2020

The Lancet Digital Health

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In The Lancet Digital Health, Kao-Lang Liu and colleagues 1 describe the applications of a convolutional neural network (CNN) in distinguishing CT images of pancreatic cancer tissue from non-cancerous pancreatic tissue. 1 Contrast-enhanced CT images of 370 patients with pancreatic cancer and 320 controls from a Taiwanese centre were manually segmented and CNN was trained to classify image patches as cancerous or noncancerous. Performance of CNN was compared with radiology reports in the local test sets. Similar to results from previous studies on this topic, 2,3 CNN achieved a remarkable performance, with accuracy of 0•986-0•989 in the local test dataset. CNN achieved higher sensitivity than that of radiologists in the local test sets (0•983 vs 0•929; p=0•014). The three tumours that were missed by the CNN were 1•1-1•2 cm in size, of which two were correctly classified by radiologists. Impressively, CNN was able to correctly classify 11 of the 12 tumours that were missed by radiologists, which were 1•0-3•3 cm in size. 1 These promising results show that CNN as a second reader can reduce misdiagnosis of pancreatic cancer and possibly lead to improved patient outcomes.Whereas previous publications relied on data from a single institution, 2, 3 Liu and colleagues also externally validated data with publicly available datasets from the USA, with an accuracy of 0•832. Importantly, external datasets of individuals with normal pancreas and pancreatic cancer patients originated from different sources: 82 patients with healthy pancreas from the Cancer Imaging Archive dataset and 281 patients with pancreatic cancer from the Medical Segmentation Decathlon dataset from Memorial Sloan Kettering Cancer Center, New York, NY, USA. Ideally, all patients from the external data should be from the same institution to ensure that the classification task is not affected by technical differences between the two institutions. The authors showed that a significant positive correlation existed between tumour size and CNN performance on both the internal and external datasets, which suggested that the CNN detected actual differences in pancreatic imaging features, rather than technical differences.These preliminary experiences in applying a CNN to detect pancreatic cancer show tremendous promise, but

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“…

The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20,8]. Previous work focuses on developing segmentation or classification methods only for certain mass types.

…”

mentioning

confidence: 99%

3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management

Zhao¹,

Cao²,

Yao³

et al. 2020

Preprint

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The pancreatic disease taxonomy includes ten types of masses (tumors or cysts) [20,8]. Previous work focuses on developing segmentation or classification methods only for certain mass types. Differential diagnosis of all mass types is clinically highly desirable [20] but has not been investigated using an automated image understanding approach.We exploit the feasibility to distinguish pancreatic ductal adenocarcinoma (PDAC) from the nine other nonPDAC masses using multi-phase CT imaging. Both image appearance and the 3D organ-mass geometry relationship are critical. We propose a holistic segmentation-meshclassification network (SMCN) to provide patient-level diagnosis, by fully utilizing the geometry and location information, which is accomplished by combining the anatomical structure and the semantic detection-by-segmentation network. SMCN learns the pancreas and mass segmentation task and builds an anatomical correspondence-aware organ mesh model by progressively deforming a pancreas prototype on the raw segmentation mask (i.e., mask-to-mesh). A new graph-based residual convolutional network (Graph-ResNet), whose nodes fuse the information of the mesh model and feature vectors extracted from the segmentation network, is developed to produce the patient-level differential classification results. Extensive experiments on 661 patients' CT scans (five phases per patient) show that SMCN can improve the mass segmentation and detection accuracy compared to the strong baseline method nnUNet (e.g., for nonPDAC, Dice: 0.611 vs. 0.478; detection rate: 89% vs. 70%), achieve similar sensitivity and specificity in differentiating PDAC and nonPDAC as expert radiologists (i.e., 94% and 90%), and obtain results comparable to a multimodality test [20] that combines clinical, imaging, and molecular testing for clinical management of patients.

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Pitfalls in the MDCT of pancreatic cancer: strategies for minimizing errors

Cited by 18 publications

References 133 publications

High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis

High-Percentage of Early Resectable Pancreatic Ductal Adenocarcinoma is Unidentified on Abdominal CT Obtained for Unrelated Diagnosis

Deep learning for pancreatic cancer detection: current challenges and future strategies

3D Graph Anatomy Geometry-Integrated Network for Pancreatic Mass Segmentation, Diagnosis, and Quantitative Patient Management

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