Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.

By Yanqi Huang, C. Liang, Lan He, Jie Tian, Cuishan Liang, Xin Chen, Zelan Ma, Zai-Yi Liu. Journal of Clinical Oncology, 2016

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Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures (2017, methodology): “Patient and clinical characteristics might be added to the input variable list, as they potentially influence not only the outcome variable, but also the extracted radiomic features themselves.(72,89) Table 1.”
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges (2019, methodology): “Huang et al. found that the radiomic signature was associated with DFS [155]. http://www.thno.org”
Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning (2019, background): “This curve shows that if the threshold probability of a patient or doctor is >10%, using the deep learning model to predict EGFR mutation status in lung adenocarcinoma adds more benefit than either the treat-all-patients scheme or the treat-none scheme [35].”
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study (2019, neutral): “Thesamplesizeof thiscohortwaschosensuchthat thepatientnumberwascomparableto thesamplesizesin similarstudies[35,36].”
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology (2017, background): “Similar studies were published in colorectal cancers [85, 86].”