BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Vaidya P, Bera K, Gupta A, Wang X, Corredor G, Fu P, Beig N, Prasanna P, Patil PD, Velu PD, Rajiah P, Gilkeson R, Feldman MD, Choi H, Velcheti V, Madabhushi A. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. Lancet Digit Health 2020;2:e116-28. [PMID: 33334576 DOI: 10.1016/S2589-7500(20)30002-9] [Cited by in Crossref: 17] [Cited by in F6Publishing: 7] [Article Influence: 8.5] [Reference Citation Analysis]
Number Citing Articles
1 Xu F, Chen H, Xu H, Li J, Hao X, Xing P, Ying J, Wang Y. Adjuvant chemotherapy in patients with recurrence after completely resected stage IB lung adenocarcinoma: Propensity‐matched analysis in a cohort of 147 recurrences. Thoracic Cancer. [DOI: 10.1111/1759-7714.14659] [Reference Citation Analysis]
2 Yi L, Peng Z, Chen Z, Tao Y, Lin Z, He A, Jin M, Peng Y, Zhong Y, Yan H, Zuo M. Identification of pulmonary adenocarcinoma and benign lesions in isolated solid lung nodules based on a nomogram of intranodal and perinodal CT radiomic features. Front Oncol 2022;12:924055. [DOI: 10.3389/fonc.2022.924055] [Reference Citation Analysis]
3 Li J, Qiu Z, Zhang C, Chen S, Wang M, Meng Q, Lu H, Wei L, Lv H, Zhong W, Zhang X. ITHscore: comprehensive quantification of intra-tumor heterogeneity in NSCLC by multi-scale radiomic features. Eur Radiol 2022. [PMID: 36001124 DOI: 10.1007/s00330-022-09055-0] [Reference Citation Analysis]
4 Ren Q, Xiong F, Zhu P, Chang X, Wang G, He N, Jin Q. Assessing the robustness of radiomics/deep learning approach in the identification of efficacy of anti–PD-1 treatment in advanced or metastatic non-small cell lung carcinoma patients. Front Oncol 2022;12:952749. [DOI: 10.3389/fonc.2022.952749] [Reference Citation Analysis]
5 Punekar SR, Shum E, Grello CM, Lau SC, Velcheti V. Immunotherapy in non-small cell lung cancer: Past, present, and future directions. Front Oncol 2022;12:877594. [DOI: 10.3389/fonc.2022.877594] [Reference Citation Analysis]
6 Li G, An C, Yu J, Huang Q. Radiomics analysis of ultrasonic image predicts sensitive effects of microwave ablation in treatment of patient with benign breast tumors. Biomedical Signal Processing and Control 2022;76:103722. [DOI: 10.1016/j.bspc.2022.103722] [Reference Citation Analysis]
7 Maniar A, Wei AZ, Dercle L, Bien H, Fojo T, Bates SE, Schwartz LH. Novel biomarkers in NSCLC: Radiomic analysis, kinetic analysis, and circulating tumor DNA. Seminars in Oncology 2022. [DOI: 10.1053/j.seminoncol.2022.06.002] [Reference Citation Analysis]
8 Sellmer L, Kovács J, Walter J, Kumbrink J, Neumann J, Kauffmann-guerrero D, Kiefl R, Schneider C, Jung A, Behr J, Tufman A. Markers of Immune Cell Exhaustion as Predictor of Survival in Surgically-Treated Early-Stage NSCLC. Front Immunol 2022;13:858212. [DOI: 10.3389/fimmu.2022.858212] [Reference Citation Analysis]
9 Yang B, Liu C, Wu R, Zhong J, Li A, Ma L, Zhong J, Yin S, Zhou C, Ge Y, Tao X, Zhang L, Lu G. Development and Validation of a DeepSurv Nomogram to Predict Survival Outcomes and Guide Personalized Adjuvant Chemotherapy in Non-Small Cell Lung Cancer. Front Oncol 2022;12:895014. [DOI: 10.3389/fonc.2022.895014] [Reference Citation Analysis]
10 Liu Y, Qi H, Wang C, Deng J, Tan Y, Lin L, Cui Z, Li J, Qi L. Predicting Chemo-Radiotherapy Sensitivity With Concordant Survival Benefit in Non-Small Cell Lung Cancer via Computed Tomography Derived Radiomic Features. Front Oncol 2022;12:832343. [DOI: 10.3389/fonc.2022.832343] [Reference Citation Analysis]
11 Masciale V, Banchelli F, Grisendi G, D’amico R, Maiorana A, Stefani A, Morandi U, Stella F, Dominici M, Aramini B. Cancer Stem Cells and Cell Cycle Genes as Independent Predictors of Relapse in Non-small Cell Lung Cancer: Secondary Analysis of a Prospective Study. Stem Cells Translational Medicine 2022. [DOI: 10.1093/stcltm/szac040] [Reference Citation Analysis]
12 Vaidya P, Bera K, Linden PA, Gupta A, Rajiah PS, Jones DR, Bott M, Pass H, Gilkeson R, Jacono F, Hsieh KL, Lan G, Velcheti V, Madabhushi A. Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study. Front Oncol 2022;12:902056. [DOI: 10.3389/fonc.2022.902056] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
13 Lee S, Jung J, Hong H, Kim B. Prediction of Two-Year Recurrence-Free Survival in Operable NSCLC Patients Using Radiomic Features from Intra- and Size-Variant Peri-Tumoral Regions on Chest CT Images. Diagnostics 2022;12:1313. [DOI: 10.3390/diagnostics12061313] [Reference Citation Analysis]
14 Wei G, Jiang P, Tang Z, Qu A, Deng X, Guo F, Sun H, Zhang Y, Gu L, Zhang S, Mu W, Wang J, Tian J. MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone. Magnetic Resonance Imaging 2022. [DOI: 10.1016/j.mri.2022.05.019] [Reference Citation Analysis]
15 Pang X, Xie P, Yu L, Chen H, Zheng J, Meng X, Wan X. A new magnetic resonance imaging tumour response grading scheme for locally advanced rectal cancer. Br J Cancer 2022. [PMID: 35388140 DOI: 10.1038/s41416-022-01801-x] [Reference Citation Analysis]
16 Chan LW, Ding T, Shao H, Huang M, Hui WF, Cho WC, Wong SC, Tong KW, Chiu KW, Huang L, Zhou H. Augmented Features Synergize Radiomics in Post-Operative Survival Prediction and Adjuvant Therapy Recommendation for Non-Small Cell Lung Cancer. Front Oncol 2022;12:659096. [DOI: 10.3389/fonc.2022.659096] [Reference Citation Analysis]
17 Williams TL, Saadat LV, Gonen M, Wei A, Do RKG, Simpson AL. Radiomics in surgical oncology: applications and challenges. Comput Assist Surg (Abingdon) 2021;26:85-96. [PMID: 34902259 DOI: 10.1080/24699322.2021.1994014] [Reference Citation Analysis]
18 Wu J, Li C, Gensheimer M, Padda S, Kato F, Shirato H, Wei Y, Schönlieb CB, Price SJ, Jaffray D, Heymach J, Neal JW, Loo BW Jr, Wakelee H, Diehn M, Li R. Radiological tumor classification across imaging modality and histology. Nat Mach Intell 2021;3:787-98. [PMID: 34841195 DOI: 10.1038/s42256-021-00377-0] [Cited by in Crossref: 4] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
19 Smyczynska U, Grabia S, Nowicka Z, Papis-Ubych A, Bibik R, Latusek T, Rutkowski T, Fijuth J, Fendler W, Tomasik B. Prediction of Radiation-Induced Hypothyroidism Using Radiomic Data Analysis Does Not Show Superiority over Standard Normal Tissue Complication Models. Cancers (Basel) 2021;13:5584. [PMID: 34771747 DOI: 10.3390/cancers13215584] [Reference Citation Analysis]
20 Walls GM, Osman SOS, Brown KH, Butterworth KT, Hanna GG, Hounsell AR, McGarry CK, Leijenaar RTH, Lambin P, Cole AJ, Jain S. Radiomics for Predicting Lung Cancer Outcomes Following Radiotherapy: A Systematic Review. Clin Oncol (R Coll Radiol) 2021:S0936-6555(21)00372-1. [PMID: 34763965 DOI: 10.1016/j.clon.2021.10.006] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 6.0] [Reference Citation Analysis]
21 Lu C, Shiradkar R, Liu Z. Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review. Chin J Cancer Res 2021;33:563-73. [PMID: 34815630 DOI: 10.21147/j.issn.1000-9604.2021.05.03] [Reference Citation Analysis]
22 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Reference Citation Analysis]
23 Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2021. [PMID: 34663898 DOI: 10.1038/s41571-021-00560-7] [Cited by in Crossref: 1] [Cited by in F6Publishing: 22] [Article Influence: 1.0] [Reference Citation Analysis]
24 Euler A, Laqua FC, Cester D, Lohaus N, Sartoretti T, Pinto Dos Santos D, Alkadhi H, Baessler B. Virtual Monoenergetic Images of Dual-Energy CT-Impact on Repeatability, Reproducibility, and Classification in Radiomics. Cancers (Basel) 2021;13:4710. [PMID: 34572937 DOI: 10.3390/cancers13184710] [Reference Citation Analysis]
25 Bera K, Katz I, Madabhushi A. Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology. JCO Clin Cancer Inform 2020;4:1039-50. [PMID: 33166198 DOI: 10.1200/CCI.20.00110] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
26 Able H, Wolf-Ringwall A, Rendahl A, Ober CP, Seelig DM, Wilke CT, Lawrence J. Computed tomography radiomic features hold prognostic utility for canine lung tumors: An analytical study. PLoS One 2021;16:e0256139. [PMID: 34403435 DOI: 10.1371/journal.pone.0256139] [Reference Citation Analysis]
27 Chen S, Lu L, Xian J, Shi C, Chen J, Rao B, Qiu F, Lu J, Yang L. Prognostic Value of Germline Copy Number Variants and Environmental Exposures in Non-small Cell Lung Cancer. Front Genet 2021;12:681857. [PMID: 34178039 DOI: 10.3389/fgene.2021.681857] [Reference Citation Analysis]
28 Zhu C, Huang H, Liu X, Chen H, Jiang H, Liao C, Pang Q, Dang J, Liu P, Lu H. A Clinical-Radiomics Nomogram Based on Computed Tomography for Predicting Risk of Local Recurrence After Radiotherapy in Nasopharyngeal Carcinoma. Front Oncol 2021;11:637687. [PMID: 33816279 DOI: 10.3389/fonc.2021.637687] [Cited by in Crossref: 1] [Cited by in F6Publishing: 6] [Article Influence: 1.0] [Reference Citation Analysis]
29 Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021;10:1186-99. [PMID: 33718055 DOI: 10.21037/tlcr-20-708] [Cited by in Crossref: 1] [Cited by in F6Publishing: 12] [Article Influence: 1.0] [Reference Citation Analysis]
30 Rodríguez M, Ajona D, Seijo LM, Sanz J, Valencia K, Corral J, Mesa-Guzmán M, Pío R, Calvo A, Lozano MD, Zulueta JJ, Montuenga LM. Molecular biomarkers in early stage lung cancer. Transl Lung Cancer Res 2021;10:1165-85. [PMID: 33718054 DOI: 10.21037/tlcr-20-750] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
31 Overhoff D, Kohlmann P, Frydrychowicz A, Gatidis S, Loewe C, Moltz J, Kuhnigk JM, Gutberlet M, Winter H, Völker M, Hahn H, Schoenberg SO; Vorstandskommission Radiomics und Big data:., Vorstand der Deutschen Röntgengesellschaft:., Präsidium der Österreichischen Röntgengesellschaft:. The International Radiomics Platform - An Initiative of the German and Austrian Radiological Societies - First Application Examples. Rofo 2021;193:276-88. [PMID: 33242898 DOI: 10.1055/a-1244-2775] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
32 Vaidya P, Bera K, Patil PD, Gupta A, Jain P, Alilou M, Khorrami M, Velcheti V, Madabhushi A. Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. J Immunother Cancer 2020;8:e001343. [PMID: 33051342 DOI: 10.1136/jitc-2020-001343] [Cited by in Crossref: 9] [Cited by in F6Publishing: 21] [Article Influence: 4.5] [Reference Citation Analysis]