Background: To improve patient selection for sentinel node (SN) biopsy, the Melanoma Institute of Australia (MIA) created a predictive model based on readily available clinicopathologic factors.
Objectives: Validation of the MIA nomogram using the National Cancer Database (NCDB), a nationwide oncology outcomes database for >1500 Commission-accredited cancer programs in the United States.
Methods: A total of 60,165 patients were included in the validation. The probability of SN positivity was calculated for each patient. Using calculated probabilities, a receiver operating characteristic curve was generated to assess the model's discrimination ability.
Results: At baseline, the NCDB cohort had different clinicopathologic characteristics compared with the original MIA data set. Despite these differences, the MIA nomogram retained high-predictive accuracy within the NCDB dataset (C-statistic, 0.733 [95% CI, 0.726-0.739]), although calibration weakened for the highest risk decile.
Limitations: The NCDB collects data from hospital registries accredited by the Commission on Cancer.
Conclusions: In conclusion, this study validated the use of the MIA nomogram in a nationwide oncology outcomes database collected from >1500 Commission-accredited cancer programs in the United States, demonstrating the potential for this nomogram to predict SN positivity and reduce the number of negative SN biopsies.
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http://dx.doi.org/10.1016/j.jaad.2023.07.011 | DOI Listing |
J Thorac Dis
October 2024
Department of Ultrasound, Tianjin Medical University General Hospital, Tianjin, China.
J Surg Oncol
November 2024
Saint John's Cancer Institute at Providence St. John's Health Center, Santa Monica, California, USA.
Background And Objectives: Clinical nomograms have been developed to predict sentinel lymph node (SLN) status in early-stage melanoma patients, but the clinical utility of these tools remains debatable. We created and validated a nomogram using data from a randomized clinical trial and assessed its accuracy against the well-validated Melanoma Institute Australia (MIA) nomogram.
Methods: We developed our model to predict SLN status using logistic regression on clinicopathological patient data from the Multicenter Selective Lymphadenectomy Trial-I.
Ann Surg Oncol
November 2024
Department of Surgery, Tom Baker Cancer Centre, Calgary, AB, Canada.
Background: Four externally validated sentinel node biopsy (SNB) prediction nomograms exist for malignant melanoma that each incorporate different clinical and histopathologic variables, which can result in substantially different risk estimations for the same patient. We demonstrate this variability by using hypothetical melanoma cases.
Methods: We compared the MSKCC and MIA calculators.
Discov Oncol
August 2024
Department of General Surgery, The First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, 215006, Jiangsu, China.
Eur J Radiol Open
December 2024
Radiology Department, Second Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong Province 515041, PR China.
Purpose: To construct optimal models for predicting the invasiveness and pathological subtypes of subsolid nodules (SSNs) based on CT radiomics and clinical features.
Materials And Methods: This study was a retrospective study involving two centers. A total of 316 patients with 353 SSNs confirmed as atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) were included from January 2019 to February 2023.
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