Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists

Chu, Linda C.; Park, Seyoun; Soleimani, Sahar; Fouladi, Daniel Fadaei; Shayesteh, Shahab; He, Jie; Javed, Ammar A.; Wolfgang, Christopher L.; Vogelstein, Bert; Kinzler, Kenneth W.; Hruban, Ralph H.; Afghani, Elham; Lennon, Anne Marie; Fishman, Elliot K.; Kawamoto, Satomi

doi:10.1007/s00261-022-03663-6

Cited by 16 publications

(9 citation statements)

References 28 publications

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“…PDAC and seven non-PDAC lesion subtypes (PNET, SPT, IPMN, MCN, SCN, chronic pancreatitis and ‘other’) 33 , 41 , 45 were targeted in this study. In the first four cohorts, PDAC and non-PDAC lesions were confirmed by surgical or biopsy histopathology.…”

Section: Methodsmentioning

confidence: 99%

“…We used the testing set of our prior work 47 as the source of the internal test cohort of the current study, given that interpretations on this set by 11 readers had been collected. Furthermore, we excluded ampullary and common bile duct cancer cases because they were usually not categorized as pancreatic lesions in the literature 41 , 45 . In addition, one normal participant was re-categorized as having chronic pancreatitis (actually autoimmune pancreatitis, but treated as chronic pancreatitis in our study) after carefully checking the patient records; and one normal participant was excluded due to a severe pancreatic duct dilation.…”

Section: Methodsmentioning

confidence: 99%

“…Differential diagnosis is an eight-class classification task for the seven most common pancreatic lesion types and ‘other’, following the pancreatic tumor–cyst classification task 41 , 45 , without normal patients included and with each patient having a lesion type assigned. The confusion matrices are used to present the detailed classification results.…”

Section: Methodsmentioning

confidence: 99%

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Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Cao,

Xia,

Yao

et al. 2023

Nat Med

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Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Cao,

Xia,

Yao

et al. 2023

Nat Med

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“…In a larger retrospective study including 214 patients who underwent resection for pancreatic cysts at Johns Hopkins, the radiomics-based random forest model yielded an AUC of 0.940 for distinguishing between five types of cystic neoplasms (IPMNs, MCNs, SPNs, SCAs, and cystic NETs) [57]. The radiomics model was compared to radiologists' diagnostic interpretation; the AUC for academic radiologists reached 0.895.…”

Section: Cross-sectional Imaging In Pclsmentioning

confidence: 99%

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Jiang

Chao

Culp

et al. 2023

Cancers

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Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65–75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

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“…The use of specific radiomic features have shown to be superior to standard radiologic features in diagnosing SCA [26]. A recent study showed radiomic-based approaches have an equivalent performance as an academic radiologist with more than 25 years of experience [27].…”

Section: Cross-sectional Imagingmentioning

confidence: 99%

Serous Cystadenoma: A Review on Diagnosis and Management

Ning,

Salamone,

Manos

et al. 2023

JCM

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Incidental pancreatic cysts are highly prevalent, with management dependent on the risk of malignant progression. Serous cystadenomas (SCAs) are the most common benign pancreatic cysts seen on imaging. They have typical morphological patterns but may also show atypical features that mimic precancerous and cancerous cysts. If a confident diagnosis of SCA is made, no further follow-up is warranted. Therefore, a preoperative distinction between SCA and precancerous or cancerous lesions is critically essential. Distinguishing an SCA from other types of pancreatic cysts on imaging remains a challenge, thus leading to misdiagnosis and ramifications. This review summarizes the current evidence on diagnosing and managing SCA.

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Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists

Cited by 16 publications

References 28 publications

Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Large-scale pancreatic cancer detection via non-contrast CT and deep learning

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Serous Cystadenoma: A Review on Diagnosis and Management

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