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For: Castaldo R, Pane K, Nicolai E, Salvatore M, Franzese M. The Impact of Normalization Approaches to Automatically Detect Radiogenomic Phenotypes Characterizing Breast Cancer Receptors Status. Cancers (Basel) 2020;12:E518. [PMID: 32102334 DOI: 10.3390/cancers12020518] [Cited by in Crossref: 20] [Cited by in F6Publishing: 20] [Article Influence: 10.0] [Reference Citation Analysis]
Number Citing Articles
1 Chitalia R, Pati S, Bhalerao M, Thakur SP, Jahani N, Belenky V, Mcdonald ES, Gibbs J, Newitt DC, Hylton NM, Kontos D, Bakas S. Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1. Sci Data 2022;9. [DOI: 10.1038/s41597-022-01555-4] [Reference Citation Analysis]
2 Fan Y, Pan X, Yang F, Liu S, Wang Z, Sun J, Chen J. Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer. American Journal of Clinical Oncology 2022;45:526-533. [DOI: 10.1097/coc.0000000000000951] [Reference Citation Analysis]
3 Zhang H, Chen H, Zhang C, Cao A, Lu Q, Wu H, Zhang J, Geng D. A radiomics feature-based machine learning models to detect brainstem infarction (RMEBI) may enable early diagnosis in non-contrast enhanced CT. Eur Radiol 2022. [PMID: 36169689 DOI: 10.1007/s00330-022-09130-6] [Reference Citation Analysis]
4 Jiang L, You C, Xiao Y, Wang H, Su G, Xia B, Zheng R, Zhang D, Jiang Y, Gu Y, Shao Z. Radiogenomic analysis reveals tumor heterogeneity of triple-negative breast cancer. Cell Reports Medicine 2022;3:100694. [DOI: 10.1016/j.xcrm.2022.100694] [Reference Citation Analysis]
5 Darvish L, Bahreyni-toossi M, Roozbeh N, Azimian H. The role of radiogenomics in the diagnosis of breast cancer: a systematic review. Egypt J Med Hum Genet 2022;23. [DOI: 10.1186/s43042-022-00310-z] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Xu A, Chu X, Zhang S, Zheng J, Shi D, Lv S, Li F, Weng X. Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study. Front Oncol 2022;12:799232. [DOI: 10.3389/fonc.2022.799232] [Reference Citation Analysis]
7 Singh A, Holzl F, Katz S, Kontos D. A comparison of feature selection methods for the development of a prognostic radiogenomic biomarker in non-small cell lung cancer patients. Medical Imaging 2022: Computer-Aided Diagnosis 2022. [DOI: 10.1117/12.2611489] [Reference Citation Analysis]
8 Castaldo R, Garbino N, Cavaliere C, Incoronato M, Basso L, Cuocolo R, Pace L, Salvatore M, Franzese M, Nicolai E. A Complex Radiomic Signature in Luminal Breast Cancer from a Weighted Statistical Framework: A Pilot Study. Diagnostics 2022;12:499. [DOI: 10.3390/diagnostics12020499] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Davey MG, Davey MS, Boland MR, Ryan ÉJ, Lowery AJ, Kerin MJ. Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis. Eur J Radiol 2021;144:109996. [PMID: 34624649 DOI: 10.1016/j.ejrad.2021.109996] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 6.0] [Reference Citation Analysis]
10 Baselice S, Castaldo R, Giannatiempo R, Casaretta G, Franzese M, Salvatore M, Mirabelli P. Impact of Breast Tumor Onset on Blood Count, Carcinoembryonic Antigen, Cancer Antigen 15-3 and Lymphoid Subpopulations Supported by Automatic Classification Approach: A Pilot Study. Cancer Control 2021;28:10732748211048612. [PMID: 34620015 DOI: 10.1177/10732748211048612] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
11 La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021;13:3015. [PMID: 34208595 DOI: 10.3390/cancers13123015] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
12 Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Radiomic Based Machine Learning Performance for a Three Class Problem in Neuro-Oncology: Time to Test the Waters? Cancers (Basel) 2021;13:2568. [PMID: 34073840 DOI: 10.3390/cancers13112568] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
13 Priya S, Liu Y, Ward C, Le NH, Soni N, Pillenahalli Maheshwarappa R, Monga V, Zhang H, Sonka M, Bathla G. Radiomics-Based Differentiation between Glioblastoma, CNS Lymphoma, and Brain Metastases: Comparing Performance across MRI Sequences and Machine Learning Models. Cancers 2021;13:2261. [DOI: 10.3390/cancers13092261] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review. J Med Internet Res 2021;23:e22394. [PMID: 33792552 DOI: 10.2196/22394] [Cited by in Crossref: 11] [Cited by in F6Publishing: 11] [Article Influence: 11.0] [Reference Citation Analysis]
15 Zanfardino M, Castaldo R, Pane K, Affinito O, Aiello M, Salvatore M, Franzese M. MuSA: a graphical user interface for multi-OMICs data integration in radiogenomic studies. Sci Rep 2021;11:1550. [PMID: 33452365 DOI: 10.1038/s41598-021-81200-z] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
16 Xing L, Tang X, Wu K, Huang X, Yi Y, Huan J. LncRNA HAND2-AS1 suppressed the growth of triple negative breast cancer via reducing secretion of MSCs derived exosomal miR-106a-5p. Aging (Albany NY) 2020;13:424-36. [PMID: 33290256 DOI: 10.18632/aging.202148] [Cited by in Crossref: 10] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
17 Wang Y, Wang Y, Guo C, Xie X, Liang S, Zhang R, Pang W, Huang L. Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics. Biomark Med 2020;14:1151-64. [PMID: 32969248 DOI: 10.2217/bmm-2020-0248] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
18 Pane K, Mirabelli P, Coppola L, Illiano E, Salvatore M, Franzese M. New Roadmaps for Non-muscle-invasive Bladder Cancer With Unfavorable Prognosis. Front Chem 2020;8:600. [PMID: 32850635 DOI: 10.3389/fchem.2020.00600] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
19 Castaldo R, Cavaliere C, Soricelli A, Salvatore M, Pecchia L, Franzese M. Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review (Preprint).. [DOI: 10.2196/preprints.22394] [Reference Citation Analysis]
20 Evangelista L, Fanti S. What Is the Role of Imaging in Cancers? Cancers (Basel) 2020;12:E1494. [PMID: 32521685 DOI: 10.3390/cancers12061494] [Reference Citation Analysis]