Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features

Pol, Christian B. van der; Lee, Stefanie Y.; Tsai, Scott S. H.; Larocque, Natasha; Alayed, Abdullah; Williams, Phillip; Schieda, Nicola

doi:10.1007/s00261-018-01889-x

Cited by 27 publications

(11 citation statements)

References 37 publications

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“…A negative finding is that indicators such as pancreatic duct dilatation, peripancreatic infiltration, vascular invasion, pancreatic atrophy and clear pancreatic boundaries do not indicate the pathological grading of tumors. It’s contrary to some previous studies ( 20 , 21 , 40 ). We do not deny that because of its higher malignancy.…”

Section: Discussioncontrasting

confidence: 86%

“…In fact, we also find that maxvalue is chosen by almost all the selection algorithms. Many previous studies have found that the pathological grading of PNETs is closely related to the parameters of HISTO (Skewness, Kurtosis, Entropy and Energy) ( 21 , 23 , 40 , 46 ). However, in our study, only skewness and kurtosis are selected by algorithms, indicating that they are related to pathological grade.…”

Section: Discussionmentioning

confidence: 99%

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Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Zhang

Liu

et al. 2021

Front. Oncol.

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PurposeTo evaluate the value of multiple machine learning methods in classifying pathological grades (G1,G2, and G3), and to provide the best machine learning method for the identification of pathological grades of pancreatic neuroendocrine tumors (PNETs) based on radiomics.Materials and MethodsA retrospective study was conducted on 82 patients with Pancreatic Neuroendocrine tumors. All patients had definite pathological diagnosis and grading results. Using Lifex software to extract the radiomics features from CT images manually. The sensitivity, specificity, area under the curve (AUC) and accuracy were used to evaluate the performance of the classification model.ResultOur analysis shows that the CT based radiomics features combined with multi algorithm machine learning method has a strong ability to identify the pathological grades of pancreatic neuroendocrine tumors. DC + AdaBoost, DC + GBDT, and Xgboost+RF were very valuable for the differential diagnosis of three pathological grades of PNET. They showed a strong ability to identify the pathological grade of pancreatic neuroendocrine tumors. The validation set AUC of DC + AdaBoost is 0.82 (G1 vs G2), 0.70 (G2 vs G3), and 0.85 (G1 vs G3), respectively.ConclusionIn conclusion, based on enhanced CT radiomics features could differentiate between different pathological grades of pancreatic neuroendocrine tumors. Feature selection method Distance Correlation + classifier method Adaptive Boosting show a good application prospect.

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Section: Discussioncontrasting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Zhang

Liu

et al. 2021

Front. Oncol.

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“…Sixth, the CT protocol did not include a late phase of enhancement during which PDCAs present with increased enhancement due to desmoplastic growth. Finally, we have compared PPM to PDAC, although other pancreatic tumors, such as neuroendocrine tumors, when hypervascular, may mimic PPM [44,45].…”

Section: Discussionmentioning

confidence: 99%

Utility of CT to Differentiate Pancreatic Parenchymal Metastasis from Pancreatic Ductal Adenocarcinoma

Barat

Aldhaheri

Dohan

et al. 2021

Cancers

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Purpose: To report the computed tomography (CT) features of pancreatic parenchymal metastasis (PPM) and identify CT features that may help discriminate between PPM and pancreatic ductal adenocarcinoma (PDAC). Materials and methods: Thirty-four patients (24 men, 12 women; mean age, 63.3 ± 10.2 [SD] years) with CT and histopathologically proven PPM were analyzed by two independent readers and compared to 34 patients with PDAC. Diagnosis performances of each variable for the diagnosis of PPM against PDAC were calculated. Univariable and multivariable analyses were performed. A nomogram was developed to diagnose PPM against PDAC. Results: PPM mostly presented as single (34/34; 100%), enhancing (34/34; 100%), solid (27/34; 79%) pancreatic lesion without visible associated lymph nodes (24/34; 71%) and no Wirsung duct enlargement (29/34; 85%). At multivariable analysis, well-defined margins (OR, 6.64; 95% CI: 1.47–29.93; p = 0.014), maximal enhancement during arterial phase (OR, 6.15; 95% CI: 1.13–33.51; p = 0.036), no vessel involvement (OR, 7.19; 95% CI: 1.512–34.14) and no Wirsung duct dilatation (OR, 10.63; 95% CI: 2.27–49.91) were independently associated with PPM. The nomogram yielded an AUC of 0.92 (95% CI: 0.85–0.98) for the diagnosis of PPM vs. PDAC. Conclusion: CT findings may help discriminate between PPM and PDAC.

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“…To assess for reproducibility of measurements, an abdominal radiologist with seven years of experience in prostate MRI (BLINDED) measured the size of tumor in a randomly selected 20% of lesions, a previously described reliable proportion of lesion needed to determine reproducibility of measurements [15].…”

Section: Cuaj -Original Researchmentioning

confidence: 99%

When to biopsy Prostate Imaging and Data Reporting System version 2 (PI-RADSv2) assessment category 3 lesions? Use of clinical and imaging variables to predict cancer diagnosis at targeted biopsy

Lim

Abreu‐Gomez

Leblond

et al. 2020

CUAJ

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Introduction: We aimed to determine if clinical and imaging features can stratify men at higher risk for clinically significant (CS, International Society of Urological Pathology [ISUP] grade group ≥2) prostate cancer (PCa) in equivocal Prostate Imaging and Data Reporting System (PI-RADS) category 3 lesions on magnetic resonance imaging (MRI). Methods: Approved by the institutional review board-approved, this retrospective study involved 184 men with 198 lesions who underwent 3T-MRI and MRI-directed transrectal ultrasound biopsy for PI-RADS 3 lesions. Men were evaluated including clinical stage, prostate-specific antigen density (PSAD), indication, and MRI lesion size. Diagnoses for all men and by indication (no cancer, any PCa, CSPCa) were compared using multivariate logistic regression, including stage, PSAD, and lesion size. Results: We found an overall PCa rate of 31.8% (63/198) and 10.1% (20/198) CSPCa (13 grade group 2, five group 3, and two group 4). Higher stage (p=0.001), PSAD (p=0.007), and lesion size (p=0.015) were associated with CSPCa, with no association between CSPCa and age, PSA, or prostate volume (p>0.05). PSAD modestly predicted CSPCa area under the curve (AUC) 0.66 (95% confidence interval [CI] 0.518–0.794) in all men and 0.64 (0.487–0.799) for those on active surveillance (AS). Model combining clinical stage, PSAD, and lesion size improved accuracy for all men and AS (AUC 0.82 [0.736–0.910], p<0.001 and 0.785 [0.666-0.904], p<0.001). In men with prior negative biopsy and persistent suspicion, PSAD (0.90 [0.767–1.000]) was not different from the model (p>0.05), with optimal cutpoint of ≥0.215 ng/mL/cc achieving sensitivity/specificity of 85.7/84.4%. Conclusions: PI-RADSv2 category 3 lesions are often not CSPCa. PSAD predicted CSPCa in men with a prior negative biopsy; however, PSAD alone had limited value, and accuracy improved when using a model incorporating PSAD with clinical stage and MR lesion size.

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Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features

Cited by 27 publications

References 37 publications

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Application of Radiomics Analysis Based on CT Combined With Machine Learning in Diagnostic of Pancreatic Neuroendocrine Tumors Patient’s Pathological Grades

Utility of CT to Differentiate Pancreatic Parenchymal Metastasis from Pancreatic Ductal Adenocarcinoma

When to biopsy Prostate Imaging and Data Reporting System version 2 (PI-RADSv2) assessment category 3 lesions? Use of clinical and imaging variables to predict cancer diagnosis at targeted biopsy

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