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ASCO Guidelines provide recommendations with comprehensive review and analyses of the relevant literature for each recommendation, following the guideline development process as outlined in the ASCO Guidelines Methodology Manual . ASCO Guidelines follow the ASCO Conflict of Interest Policy for Clinical Practice Guidelines . Clinical Practice Guidelines and other guidance (“Guidance”) provided by ASCO is not a comprehensive or definitive guide to treatment options. It is intended for voluntary use by clinicians and should be used in conjunction with independent professional judgment. Guidance may not be applicable to all patients, interventions, diseases or stages of diseases. Guidance is based on review and analysis of relevant literature and is not intended as a statement of the standard of care. ASCO does not endorse third-party drugs, devices, services, or therapies and assumes no responsibility for any harm arising from or related to the use of this information. See complete disclaimer in Appendix 1 and 2 (online only) for more. PURPOSE To develop a clinical practice guideline and recommendations for symptom management of patients with well-differentiated grade 1 to grade 3 metastatic gastroenteropancreatic neuroendocrine tumors. METHODS ASCO convened an Expert Panel to develop a clinical practice guideline by reviewing the literature for relevant guidelines, systematic reviews, randomized controlled trials (RCTs), and observational studies to develop recommendations for clinical practice. RESULTS The literature review identified eight guidelines, 19 systematic reviews, and three RCTs that informed the development of guideline recommendations. RECOMMENDATIONS Recommendations are included for carcinoid syndrome, carcinoid heart disease and carcinoid crisis, and functional pancreatic neuroendocrine tumor syndromes. Recommendations are provided for surgical management, liver-directed therapy, and systemic therapy options, as well as palliative care. Limited guidance is provided for sequencing of interventions. Additional information is available at www.asco.org/gastrointestinal-cancer-guidelines .

PURPOSE To develop recommendations for systemic therapy for well-differentiated grade 1 (G1) to grade 3 (G3) metastatic gastroenteropancreatic neuroendocrine tumors (GEP-NETs). METHODS ASCO convened an Expert Panel to conduct a systematic review of relevant studies and develop recommendations for clinical practice. RESULTS Eight randomized controlled trials met the inclusion criteria for the systematic review. RECOMMENDATIONS Somatostatin analogs (SSAs) are recommended as first-line systemic therapy for most patients with G1-grade 2 (G2) metastatic well-differentiated GI-NETs. Observation is an option for patients with low-volume or slow-growing disease without symptoms. After progression on SSAs, peptide receptor radionuclide therapy (PRRT) is recommended as systematic therapy for patients with somatostatin receptor (SSTR)–positive tumors. Everolimus is an alternative second-line therapy, particularly in nonfunctioning NETs and patients with SSTR-negative tumors. SSAs are standard first-line therapy for SSTR-positive pancreatic (pan)NETs. Rarely, observation may be appropriate for asymptomatic patients until progression. Second-line systemic options for panNETs include PRRT (for SSTR-positive tumors), cytotoxic chemotherapy, everolimus, or sunitinib. For SSTR-negative tumors, first-line therapy options are chemotherapy, everolimus, or sunitinib. There are insufficient data to recommend particular sequencing of therapies. Patients with G1-G2 high-volume disease, relatively high Ki-67 index, and/or symptoms related to tumor growth may benefit from early cytotoxic chemotherapy. For G3 GEP-NETs, systemic options for G1-G2 may be considered, although cytotoxic chemotherapy is likely the most effective option for patients with tumor-related symptoms, and SSAs are relatively ineffective. Qualifying statements are provided to assist with treatment choice. Multidisciplinary team management is recommended, along with shared decision making with patients, incorporating their values and preferences, potential benefits and harms, and other characteristics and circumstances, such as comorbidities, performance status, geographic location, and access to care. Additional information is available at www.asco.org/gastrointestinal-cancer-guidelines .

The NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Neuroendocrine and Adrenal Gland Tumors focus on the diagnosis, treatment, and management of patients with neuroendocrine tumors (NETs), adrenal tumors, pheochromocytomas, paragangliomas, and multiple endocrine neoplasia. NETs are generally subclassified by site of origin, stage, and histologic characteristics. Appropriate diagnosis and treatment of NETs often involves collaboration between specialists in multiple disciplines, using specific biochemical, radiologic, and surgical methods. Specialists include pathologists, endocrinologists, radiologists (including nuclear medicine specialists), and medical, radiation, and surgical oncologists. These guidelines discuss the diagnosis and management of both sporadic and hereditary neuroendocrine and adrenal tumors and are intended to assist with clinical decision-making. This article is focused on the 2021 NCCN Guidelines principles of genetic risk assessment and counseling and recommendations for well-differentiated grade 3 NETs, poorly differentiated neuroendocrine carcinomas, adrenal tumors, pheochromocytomas, and paragangliomas.

Abstract This manuscript is the result of the North American Neuroendocrine Tumor Society consensus conference on the medical management and surveillance of metastatic and unresectable pheochromocytoma and paraganglioma held on October 2 and 3, 2019. The panelists consisted of endocrinologists, medical oncologists, surgeons, radiologists/nuclear medicine physicians, nephrologists, pathologists, and radiation oncologists. The panelists performed a literature review on a series of questions regarding the medical management of metastatic and unresectable pheochromocytoma and paraganglioma as well as questions regarding surveillance after resection. The panelists voted on controversial topics, and final recommendations were sent to all panel members for final approval.

BackgroundPretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision‐making.PurposeTo evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low‐grade (grade I–II) from high‐grade (grade III–IV) in stage I and II renal cell carcinoma.Study TypeRetrospective.PopulationIn all, 376 patients with 430 renal cell carcinoma lesions from 2008–2019 in a multicenter cohort were acquired. The 353 Fuhrman‐graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set.Field Strength/Sequence1.5T and 3.0T/T2‐weighted and T1 contrast‐enhanced sequences.AssessmentThe accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision‐recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model.Statistical TestsMann–Whitney U‐test for continuous data and the chi‐square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low‐ and high‐grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity.ResultsThe final deep‐learning model achieved a test accuracy of 0.88 (95% CI: 0.73–0.96), sensitivity of 0.89 (95% CI: 0.74–0.96), and specificity of 0.88 (95% CI: 0.73–0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73–0.90), sensitivity of 0.92 (95% CI: 0.84–0.97), and specificity of 0.78 (95% CI: 0.68–0.86) in the WHO/ISUP test set.Data ConclusionDeep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy.Level of Evidence3Technical Efficacy Stage2

Abstract Purpose: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. Experimental Design: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. Results: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). Conclusions: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.