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https://doi.org/10.2139/ssrn.4898481

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/jamaoncol.2019.1558openalex-extracted
Value of Molecular Classification for Prognostic Assessment of Adrenocortical Carcinoma
inferredcites →https://doi.org/10.1001/jamasurg.2015.4516openalex-extracted
Nomograms to Predict Recurrence-Free and Overall Survival After Curative Resection of Adrenocortical Carcinoma
inferredcites →https://doi.org/10.1002/cncr.24030openalex-extracted
Limited prognostic value of the 2004 International Union Against Cancer staging classification for adrenocortical carcinoma
inferredcites →https://doi.org/10.1002/sim.4058openalex-extracted
An application of Harrell's C‐index to PH frailty models
inferredcites →https://doi.org/10.1002/sim.4154openalex-extracted
On the C‐statistics for evaluating overall adequacy of risk prediction procedures with censored survival data
inferredcites →https://doi.org/10.1007/s00268-005-0329-xopenalex-extracted
Extent of Disease at Presentation and Outcome for Adrenocortical Carcinoma: Have We Made Progress?
inferredcites →https://doi.org/10.1016/j.beem.2008.10.008openalex-extracted
Clinical management of adrenocortical carcinoma
inferredcites →https://doi.org/10.1016/j.beem.2020.101434openalex-extracted
Next-generation therapies for adrenocortical carcinoma
inferredcites →https://doi.org/10.1016/j.canlet.2020.03.032openalex-extracted
Machine Learning in oncology: A clinical appraisal
inferredcites →https://doi.org/10.1016/j.ccell.2016.04.002openalex-extracted
Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma
inferredcites →https://doi.org/10.1016/j.ejca.2013.02.034openalex-extracted
Adrenocortical carcinoma: A population-based study on incidence and survival in the Netherlands since 1993
inferredcites →https://doi.org/10.1038/ng.2953openalex-extracted
Integrated genomic characterization of adrenocortical carcinoma
inferredcites →https://doi.org/10.1038/s41598-021-86327-7openalex-extracted
Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival
inferredcites →https://doi.org/10.1056/nejmc2305287openalex-extracted
Artificial Intelligence and Machine Learning in Clinical Medicine
inferredcites →https://doi.org/10.1093/ejendo/lvad112openalex-extracted
Performance of DNA-based biomarkers for classification of adrenocortical carcinoma: a prognostic study
inferredcites →https://doi.org/10.1097/ede.0b013e3181c30fb2openalex-extracted
Assessing the Performance of Prediction Models
inferredcites →https://doi.org/10.1210/er.2013-1029openalex-extracted
Adrenocortical Carcinoma
inferredcites →https://doi.org/10.1210/jc.2012-2559openalex-extracted
The Role of Surgery in the Management of Recurrent Adrenocortical Carcinoma
inferredcites →https://doi.org/10.1210/jc.2014-3182openalex-extracted
Major Prognostic Role of Ki67 in Localized Adrenocortical Carcinoma After Complete Resection
inferredcites →https://doi.org/10.1210/jc.2018-01348openalex-extracted
Targeted Molecular Analysis in Adrenocortical Carcinomas: A Strategy Toward Improved Personalized Prognostication
inferredcites →https://doi.org/10.1530/eje-18-0608openalex-extracted
European Society of Endocrinology Clinical Practice Guidelines on the management of adrenocortical carcinoma in adults, in collaboration with the European Network for the Study of Adrenal Tumors
inferredcites →https://doi.org/10.1530/eje-21-0510openalex-extracted
S-GRAS score for prognostic classification of adrenocortical carcinoma: an international, multicenter ENSAT study
inferredcites →https://doi.org/10.18632/oncotarget.5195openalex-extracted
Validation of a rectal cancer outcome prediction model with a cohort of Chinese patients
inferredcites →https://doi.org/10.3389/fsurg.2022.966307openalex-extracted
Establishment of prognostic models of adrenocortical carcinoma using machine learning and big data
inferredcites →https://doi.org/10.3390/jcm12113771openalex-extracted
Assessment of Self-Reported Executive Function in Patients with Irritable Bowel Syndrome Using a Machine-Learning Framework
inferredcites →https://doi.org/10.48550/arxiv.2110.02484openalex-extracted
Shapley variable importance clouds for interpretable machine learning
inferredcites →https://doi.org/10.5167/uzh-255563openalex-extracted
Prediction of metastatic pheochromocytoma and paraganglioma: a machine learning modelling study using data from a cross sectional cohort

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1097/med.0000000000000921openalex-snapshot
Emerging treatment options for adrenocortical carcinoma
inferred← citeshttps://doi.org/10.1210/clinem/dgaf096openalex-snapshot
Machine Learning–Based Survival Prediction Tool for Adrenocortical Carcinoma
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