scholarly journals Preoperative Differentiation of Pancreatic Cystic Neoplasm Subtypes on CT-Based Radiomics: SCN, MCN And IPMN

2021 ◽  
Author(s):  
Shanshan Xu ◽  
Yifan Zhang ◽  
Jin Wu ◽  
Shengnan Tang ◽  
Jian He
Keyword(s):  
Prediction Models ◽  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Multivariable Logistic Regression Analysis ◽  
Study Cohort ◽  
Tumor Diameter ◽  
Pancreatic Cystic Neoplasm ◽  
Net Benefit ◽  
Class Prediction ◽  
Radiomics Signature

Abstract Background:The serous cystic neoplasm (SCN), mucinous cystic neoplasm (MCN), intraductal papillary mucinous neoplasm (IPMN) comprise the large proportion of pancreatic cystic neoplasm (PCN). The appropriate clinical management of MCN and IPMN isextremely essential to improve the 5-years survival rate for the early detection of pancreatic cancer. However, the differential diagnosis of patients with PCN before the treatment is still a tough challenge for all surgeons. Therefore, a reliable diagnosis tool is urgently required to be established for the improvement of precision diagnostics.Method:Between February 2016 and December 2020, 143 consecutive patients with PCN who were confirmed by postoperative pathology were retrospectively included in the study cohort, randomized into development and test cohort at the ratio of 7:3. The predictors of preoperative clinical-radiologic paraments were evaluated by the use of univariate and multivariable logistic regression analysis. A total of 1218 radiomics features were computationally extracted from the enhanced computed tomography (CT) of tumor region and a radiomics signature was established by the random forest algorithm. In the development cohort, the multi-class and binary-class radiomics models integrating preoperative variables and radiomics features were constructed to distinguish between the three types of PCN. The independent internal test cohort was applied to validate the classification models.Result:All preoperative prediction models were built by integrating the radiomics signature with thirteen diagnosis-related radiomics features and three important clinical-radiologic parameters of age, sex and tumor diameter. The multi-class prediction model presented an overall accuracy of 0.804 in the development cohort and 0.707 in the test cohort. The binary-class prediction models displayed the higher overall accuracy of 0.853, 0.866, 0.928 in the development dataset and 0.750, 0.839, 0.889 in the test dataset. In the test cohort, the binary-class radiomics models showed better predictive performances (AUC = 0.914, 0.863 ,0.926) than the multi-class radiomics model (AUC = 0.850), with a large net benefit in the decisive curve analysis. The radiomics-based nomogram provided the correct predicted probability for the diagnosis of PCN.Conclusion: The proposed radiomics models with clinical-radiologic parameters and radiomics features helped predict the accurate diagnosis among SCN, MCN, and IPMN to advance personalized medicine.

scholarly journals The Pathological Features and Prognoses of Intraductal Papillary Mucinous Neoplasm and Mucinous Cystic Neoplasm After Surgical Resection: A Single Institution Series

2020 ◽  
Author(s):  
Yuqiong Li ◽  
Zhongfei Zhu ◽  
Lisi Peng ◽  
Zhendong Jin ◽  
Liqi Sun ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Study Cohort ◽  
Single Institution ◽  
Cystic Neoplasms ◽  
Mucinous Neoplasms ◽  
Transformation Potential ◽  
Independent Risk Factors

Abstract Background: Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) represent the tumors with malignant transformation potential. The objective of the study was to verify their pathological characteristics, prognoses, and recurrence factors. Methods: 218 IPMNs and 27 MCNs resected at a single institution were included. The demographic, preoperative, histopathological and follow-up data of the patients were recorded and analyzed. Results: Of the 218 IPMN and 27 MCN patients, 93 (42.7%) and 8 (29.6 %) cases were malignant, respectively. IPMNs occurred in older patients compared with MCN patients (median 63 years vs 54 years, P<0.0001) and MCNs occurred exclusively in females (100%). Of the overall study cohort, the pathological specimens presented peripheral invasion in 37 (15.1%) patients and incisal margin invasion was observed in 46 (18.8%) patients. After a median follow-up of 34 months, 37(14.9%) patients relapsed. The 1, 3, 5 -year overall survival rate (OS) and diseases-free survival (DFS) rate for IPMNs were 98.75%, 98.75%, 97.5%, and 85.7%, 81.1%, 80.6%; and for MCNs the rates were 95.7%, 95.7%, 95.7%, and 91.3%%, 87.0%, 87.0%, respectively. There were four independent risk factors associated with recurrence: pathological diagnoses with malignancy (Odds rate, OR=3.65), presence of oncocytic type for IPMN (OR=1.69), peripheral invasion (OR=12.87) and incisal margin invasion (OR=1.99). Conclusions: IPMNs and MCNs are indolent tumors with favorable prognoses after surgical resection in terms of their relatively high OS and DFS rate. Patients with malignant pathological-related diagnoses should accept strict tumor surveillance in view of their higher risk of recurrence.

scholarly journals The pathological features and prognoses of intraductal papillary mucinous neoplasm and mucinous cystic neoplasm after surgical resection: a single institution series

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Yuqiong Li ◽  
Zhongfei Zhu ◽  
Lisi Peng ◽  
Zhendong Jin ◽  
Liqi Sun ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Disease Free Survival ◽  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Study Cohort ◽  
Single Institution ◽  
Cystic Neoplasms ◽  
Initial Surgery ◽  
Transformation Potential

Abstract Background Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) represent the tumors with malignant transformation potential. The objective of the study was to verify their pathological characteristics, prognoses, and recurrence factors. Methods Two hundred eighteen IPMNs and 27 MCNs resected at a single institution were included. The demographic, preoperative, histopathological, and follow-up data of the patients were recorded and analyzed. Overall survival (OS) and disease-free survival (DFS) were defined as the interval from the date of initial surgery to death or the last follow-up (OS) and to diagnosis of recurrence or death at follow-up (DFS). Results Of the 218 IPMN and 27 MCN patients, 93 (42.7%) and 8 (29.6%) cases were malignant, respectively. IPMNs occurred in older patients compared with MCN patients (median 63 years vs 54 years, P < 0.0001), and MCNs occurred exclusively in females (100%). Of the overall study cohort, the pathological specimens presented peripheral invasion in 37 (15.1%) patients and incisal margin invasion was observed in 46 (18.8%) patients. After a median follow-up of 34 months, 37 (14.9%) patients relapsed. The 5-year OS and DFS rates of IPMNs were 97.5% and 80.6%; and the OS and DFS rates of MCNs were 95.7% and 87.0%, respectively. There were four independent risk factors associated with recurrence: pathological diagnoses with malignancy (odds ratio, OR = 3.65), presence of oncocytic type for IPMN (OR = 1.69), peripheral invasion (OR = 12.87), and incisal margin invasion (OR = 1.99). Conclusions IPMNs and MCNs are indolent tumors with favorable prognoses after surgical resection in terms of their relatively high OS and DFS rate. Patients with malignant pathological-related diagnoses should accept strict tumor surveillance in view of their higher risk of recurrence.

scholarly journals The pathological features and prognoses of intraductal papillary mucinous neoplasm and mucinous cystic neoplasm after surgical resection: a single institution series

2020 ◽  
Author(s):  
Yuqiong Li ◽  
Zhongfei Zhu ◽  
Lisi Peng ◽  
Zhendong Jin ◽  
Liqi Sun ◽  
...  
Keyword(s):  
Surgical Resection ◽  
Disease Free Survival ◽  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Study Cohort ◽  
Single Institution ◽  
Cystic Neoplasms ◽  
Initial Surgery ◽  
Transformation Potential

Abstract Background: Intraductal papillary mucinous neoplasms (IPMNs) and mucinous cystic neoplasms (MCNs) represent the tumors with malignant transformation potential. The objective of the study was to verify their pathological characteristics, prognoses, and recurrence factors.Methods: 218 IPMNs and 27 MCNs resected at a single institution were included. The demographic, preoperative, histopathological and follow-up data of the patients were recorded and analyzed. Overall survival (OS) and disease-free survival (DFS) were defined as the interval from the date of initial surgery to death or the last follow up (OS) and to diagnosis of recurrence or death at follow-up (DFS).Results: Of the 218 IPMN and 27 MCN patients, 93 (42.7%) and 8 (29.6 %) cases were malignant, respectively. IPMNs occurred in older patients compared with MCN patients (median 63 years vs 54 years, P<0.0001) and MCNs occurred exclusively in females (100%). Of the overall study cohort, the pathological specimens presented peripheral invasion in 37 (15.1%) patients and incisal margin invasion was observed in 46 (18.8%) patients. After a median follow-up of 34 months, 37(14.9%) patients relapsed. The 5-year OS and DFS rate of IPMNs were 97.5% and 80.6%; and the OS and DFS rates of MCNs were 95.7% and 87.0%, respectively. There were four independent risk factors associated with recurrence: pathological diagnoses with malignancy (Odds ratio, OR=3.65), presence of oncocytic type for IPMN (OR=1.69), peripheral invasion (OR=12.87) and incisal margin invasion (OR=1.99).Conclusions: IPMNs and MCNs are indolent tumors with favorable prognoses after surgical resection in terms of their relatively high OS and DFS rate. Patients with malignant pathological-related diagnoses should accept strict tumor surveillance in view of their higher risk of recurrence.

A case of pancreatic mucinous cystic neoplasm: long-term follow-up with ultrasonography

Choonpa Igaku ◽  
2011 ◽  
Vol 38 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Junko FUKUDA ◽  
Sachiko TANAKA ◽  
Miho NAKAO ◽  
Eri UEDA ◽  
Reiko SUZUKI ◽  
...  
Keyword(s):  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Long Term Follow Up

Robotic Left Hepatectomy for Mucinous Cystic Neoplasm: Tips for Intrahepatic Bleeding Control

2020 ◽  
pp. 000313482095634
Author(s):  
Iswanto Sucandy ◽  
Janelle Spence ◽  
Sharona Ross ◽  
Alexander Rosemurgy
Keyword(s):  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Bleeding Control ◽  
Left Hepatectomy

scholarly journals A radiomic nomogram based on arterial phase of CT for differential diagnosis of ovarian cancer

2021 ◽  
Author(s):  
Yumin Hu ◽  
Qiaoyou Weng ◽  
Haihong Xia ◽  
Tao Chen ◽  
Chunli Kong ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ovarian Cancer ◽  
Clinical Data ◽  
Arterial Phase ◽  
Multivariable Logistic Regression Analysis ◽  
Clinical Factors ◽  
Good Discrimination ◽  
Training Cohort ◽  
Ovarian Cancers ◽  
Radiomics Signature

Abstract Purpose To develop and validate a radiomic nomogram based on arterial phase of CT to discriminate the primary ovarian cancers (POCs) and secondary ovarian cancers (SOCs). Methods A total of 110 ovarian cancer patients in our hospital were reviewed from January 2010 to December 2018. Radiomic features based on the arterial phase of CT were extracted by Artificial Intelligence Kit software (A.K. software). The least absolute shrinkage and selection operation regression (LASSO) was employed to select features and construct the radiomics score (Rad-score) for further radiomics signature calculation. Multivariable logistic regression analysis was used to develop the predicting model. The predictive nomogram model was composed of rad-score and clinical data. Nomogram discrimination and calibration were evaluated. Results Two radiomic features were selected to build the radiomics signature. The radiomics nomogram that incorporated 2 radiomics signature and 2 clinical factors (CA125 and CEA) showed good discrimination in training cohort (AUC 0.854), yielding the sensitivity of 78.8% and specificity of 90.7%, which outperformed the prediction model based on radiomics signature or clinical data alone. A visualized differential nomogram based on the radiomic score, CEA, and CA125 level was established. The calibration curve demonstrated the clinical usefulness of the proposed nomogram. Conclusion The presented nomogram, which incorporated radiomic features of arterial phase of CT with clinical features, could be useful for differentiating the primary and secondary ovarian cancers.

A nomogram for predicting pancreatic mucinous cystic neoplasm and serous cystic neoplasm

2021 ◽  
Author(s):  
Chengwei Shao ◽  
Xiaochen Feng ◽  
Jieyu Yu ◽  
Yinghao Meng ◽  
Fang Liu ◽  
...  
Keyword(s):  
Mucinous Cystic Neoplasm ◽  
Cystic Neoplasm ◽  
Serous Cystic Neoplasm

Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features

2021 ◽  
pp. 1-12
Author(s):  
Zongqiong Sun ◽  
Linfang Jin ◽  
Shuai Zhang ◽  
Shaofeng Duan ◽  
Wei Xing ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Clinical Features ◽  
Calibration Curve ◽  
Prediction Models ◽  
Ct Images ◽  
Clinical Findings ◽  
Training Set ◽  
Prediction Probability ◽  
Radiomics Signature ◽  
Testing Set

PURPOSE: To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS: The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS: In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p <  0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p >  0.05). CONCLUSION: The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.

scholarly journals Prediction of COVID-19 severity using laboratory findings on admission: informative values, thresholds, ML model performance

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e044500
Author(s):  
Yauhen Statsenko ◽  
Fatmah Al Zahmi ◽  
Tetiana Habuza ◽  
Klaus Neidl-Van Gorkom ◽  
Nazar Zaki
Keyword(s):  
Lymphocyte Count ◽  
Prediction Models ◽  
Model Performance ◽  
Area Under The Curve ◽  
Predictive Biomarkers ◽  
Supervised Machine Learning ◽  
Study Cohort ◽  
Threshold Values ◽  
Laboratory Findings ◽  
Study Results

BackgroundDespite the necessity, there is no reliable biomarker to predict disease severity and prognosis of patients with COVID-19. The currently published prediction models are not fully applicable to clinical use.ObjectivesTo identify predictive biomarkers of COVID-19 severity and to justify their threshold values for the stratification of the risk of deterioration that would require transferring to the intensive care unit (ICU).MethodsThe study cohort (560 subjects) included all consecutive patients admitted to Dubai Mediclinic Parkview Hospital from February to May 2020 with COVID-19 confirmed by the PCR. The challenge of finding the cut-off thresholds was the unbalanced dataset (eg, the disproportion in the number of 72 patients admitted to ICU vs 488 non-severe cases). Therefore, we customised supervised machine learning (ML) algorithm in terms of threshold value used to predict worsening.ResultsWith the default thresholds returned by the ML estimator, the performance of the models was low. It was improved by setting the cut-off level to the 25th percentile for lymphocyte count and the 75th percentile for other features. The study justified the following threshold values of the laboratory tests done on admission: lymphocyte count <2.59×109/L, and the upper levels for total bilirubin 11.9 μmol/L, alanine aminotransferase 43 U/L, aspartate aminotransferase 32 U/L, D-dimer 0.7 mg/L, activated partial thromboplastin time (aPTT) 39.9 s, creatine kinase 247 U/L, C reactive protein (CRP) 14.3 mg/L, lactate dehydrogenase 246 U/L, troponin 0.037 ng/mL, ferritin 498 ng/mL and fibrinogen 446 mg/dL.ConclusionThe performance of the neural network trained with top valuable tests (aPTT, CRP and fibrinogen) is admissible (area under the curve (AUC) 0.86; 95% CI 0.486 to 0.884; p<0.001) and comparable with the model trained with all the tests (AUC 0.90; 95% CI 0.812 to 0.902; p<0.001). Free online tool at https://med-predict.com illustrates the study results.

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