Pancreatic Neuroendocrine Neoplasm Invading the Entire Main Pancreatic Duct Diagnosed by a Preoperative Endoscopic Biopsy

Kimura, Tsutomu; Sugimoto, Mitsuru; Takagi, Tadayuki; Suzuki, Rei; Konno, Naoki; Asama, Hiroyuki; Sato, Yuki; Irie, Hiroki; Nakamura, Jun; Takasumi, Mika; Hashimoto, Minami; Kato, Tsunetaka; Kofunato, Yasuhide; Kimura, Takashi; Yamada, Shuhei; Hashimoto, Yuko; Marubashi, Shigeru; Hikichi, Takuto; Ohira, Hiromasa

doi:10.2169/internalmedicine.4546-20

Cited by 1 publication

(1 citation statement)

References 35 publications

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“…(6) In the primary site of the tumor, we found that the prognosis of GP-NET originated from the pancreas was significantly worse than that originated from the gastrointestinal tract, probably related to the rapid progression of pancreatic tumors. Therefore, for patients with the above conditions, special attention should be paid to the possibility of GP-NENs progression [22,23,24].…”

Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

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Purpose. Based on computerized tomography (CT) radiomics and clinical data, a model was established to predict the prognosis of patients with gastrointestinal pancreatic neuroendocrine neoplasms (GP-NENs). Methods. In the data collection, the clinical imaging and survival follow-up data of 225 GP-NENs patients admitted to Xiangyang No.1 People’s Hospital and Jiangsu Province Hospital of Chinese Medicine from August 2015 to February 2021 were collected. According to the follow-up results, they were divided into the nonrecurrent group ( n = 108 ) and the recurrent group ( n = 117 ), based on which a training set and a test set were established at a ratio of 7/3. In the training set, a variety of models were established with significant clinical and imaging data ( P < 0.05 ) to predict the prognosis of GP-NENs patients, and then these models were verified in the test set. Results. Our newly developed combined prediction model had high predictive efficacy. Univariate analysis showed that Radscore 1/2/3, age, Ki-67 index, tumor pathological type, tumor primary site, and TNM stage were risk factors for the prognosis of GP-NENs patients (all P < 0.05 ). The area under the receiver operating characteristic (ROC) curves (AUC) of the combined model was significantly higher [AUC:0.824, 95% CI 0.0342 (0.751-0.883)] than that of the clinical data model [AUC:0.786, 95% CI 0.0384(0.709-0.851)] and the radiomics model [AUC:0.712, 95% CI 0.0426(0.631-0.785)]. The decision curve also confirmed that the combined model had a higher clinical net benefit. The same results were achieved in the test set. Conclusion. The prognosis of patients with GP-NENs is generally poor. The combined model based on clinical data and CT radiomics can help to early predict the prognosis of patients with GP-NENs, and then necessary interventions could be provided to improve the survival rate and quality of life of patients.

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Section: Computational and Mathematical Methods In Medicinementioning

confidence: 99%

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

Zhang

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

Pancreatic Neuroendocrine Neoplasm Invading the Entire Main Pancreatic Duct Diagnosed by a Preoperative Endoscopic Biopsy

Cited by 1 publication

References 35 publications

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

Clinical Data-CT Radiomics-Based Model for Predicting Prognosis of Patients with Gastrointestinal Pancreatic Neuroendocrine Neoplasms (GP-NENs)

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