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For: Astaraki M, Wang C, Buizza G, Toma-dasu I, Lazzeroni M, Smedby Ö. Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Physica Medica 2019;60:58-65. [DOI: 10.1016/j.ejmp.2019.03.024] [Cited by in Crossref: 16] [Cited by in F6Publishing: 18] [Article Influence: 5.3] [Reference Citation Analysis]
Number Citing Articles
1 Manafi-Farid R, Askari E, Shiri I, Pirich C, Asadi M, Khateri M, Zaidi H, Beheshti M. [18F]FDG-PET/CT radiomics and artificial intelligence in lung cancer: Technical aspects and potential clinical applications. Semin Nucl Med 2022:S0001-2998(22)00035-6. [PMID: 35717201 DOI: 10.1053/j.semnuclmed.2022.04.004] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Astaraki M, Yang G, Zakko Y, Toma-Dasu I, Smedby Ö, Wang C. A Comparative Study of Radiomics and Deep-Learning Based Methods for Pulmonary Nodule Malignancy Prediction in Low Dose CT Images. Front Oncol 2021;11:737368. [PMID: 34976794 DOI: 10.3389/fonc.2021.737368] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
3 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: 5] [Article Influence: 6.0] [Reference Citation Analysis]
4 Le VH, Kha QH, Hung TNK, Le NQK. Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer. Cancers (Basel) 2021;13:3616. [PMID: 34298828 DOI: 10.3390/cancers13143616] [Cited by in F6Publishing: 8] [Reference Citation Analysis]
5 Lin M, Wynne JF, Zhou B, Wang T, Lei Y, Curran WJ, Liu T, Yang X. Artificial intelligence in tumor subregion analysis based on medical imaging: A review. J Appl Clin Med Phys 2021;22:10-26. [PMID: 34164913 DOI: 10.1002/acm2.13321] [Cited by in F6Publishing: 3] [Reference Citation Analysis]
6 Sadaghiani MS, Rowe SP, Sheikhbahaei S. Applications of artificial intelligence in oncologic 18F-FDG PET/CT imaging: a systematic review. Ann Transl Med 2021;9:823. [PMID: 34268436 DOI: 10.21037/atm-20-6162] [Cited by in F6Publishing: 4] [Reference Citation Analysis]
7 Astaraki M, Zakko Y, Toma Dasu I, Smedby Ö, Wang C. Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features. Physica Medica 2021;83:146-53. [DOI: 10.1016/j.ejmp.2021.03.013] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 1.0] [Reference Citation Analysis]
8 Piñeiro-Fiel M, Moscoso A, Pubul V, Ruibal Á, Silva-Rodríguez J, Aguiar P. A Systematic Review of PET Textural Analysis and Radiomics in Cancer. Diagnostics (Basel) 2021;11:380. [PMID: 33672285 DOI: 10.3390/diagnostics11020380] [Cited by in Crossref: 4] [Cited by in F6Publishing: 12] [Article Influence: 4.0] [Reference Citation Analysis]
9 Buizza G, Paganelli C, D'Ippolito E, Fontana G, Molinelli S, Preda L, Riva G, Iannalfi A, Valvo F, Orlandi E, Baroni G. Radiomics and Dosiomics for Predicting Local Control after Carbon-Ion Radiotherapy in Skull-Base Chordoma. Cancers (Basel) 2021;13:339. [PMID: 33477723 DOI: 10.3390/cancers13020339] [Cited by in Crossref: 2] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
10 Sollini M, Bartoli F, Marciano A, Zanca R, Slart RHJA, Erba PA. Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology. Eur J Hybrid Imaging 2020;4:24. [PMID: 34191197 DOI: 10.1186/s41824-020-00094-8] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
11 Bortolotto C, Lancia A, Stelitano C, Montesano M, Merizzoli E, Agustoni F, Stella G, Preda L, Filippi AR. Radiomics features as predictive and prognostic biomarkers in NSCLC. Expert Rev Anticancer Ther 2021;21:257-66. [PMID: 33216651 DOI: 10.1080/14737140.2021.1852935] [Cited by in Crossref: 1] [Cited by in F6Publishing: 4] [Article Influence: 0.5] [Reference Citation Analysis]
12 Amugongo LM, Osorio EV, Green A, Cobben D, van Herk M, McWilliam A. Identification of patterns of tumour change measured on CBCT images in NSCLC patients during radiotherapy. Phys Med Biol 2020;65:215001. [PMID: 32693397 DOI: 10.1088/1361-6560/aba7d3] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
13 Kothari G, Korte J, Lehrer EJ, Zaorsky NG, Lazarakis S, Kron T, Hardcastle N, Siva S. A systematic review and meta-analysis of the prognostic value of radiomics based models in non-small cell lung cancer treated with curative radiotherapy. Radiother Oncol 2021;155:188-203. [PMID: 33096167 DOI: 10.1016/j.radonc.2020.10.023] [Cited by in Crossref: 4] [Cited by in F6Publishing: 13] [Article Influence: 2.0] [Reference Citation Analysis]
14 Krarup MMK, Krokos G, Subesinghe M, Nair A, Fischer BM. Artificial Intelligence for the Characterization of Pulmonary Nodules, Lung Tumors and Mediastinal Nodes on PET/CT. Semin Nucl Med 2021;51:143-56. [PMID: 33509371 DOI: 10.1053/j.semnuclmed.2020.09.001] [Cited by in Crossref: 1] [Cited by in F6Publishing: 7] [Article Influence: 0.5] [Reference Citation Analysis]
15 Avanzo M, Stancanello J, Pirrone G, Sartor G. Radiomics and deep learning in lung cancer. Strahlenther Onkol 2020;196:879-87. [PMID: 32367456 DOI: 10.1007/s00066-020-01625-9] [Cited by in Crossref: 8] [Cited by in F6Publishing: 39] [Article Influence: 4.0] [Reference Citation Analysis]
16 Bianconi F, Palumbo I, Spanu A, Nuvoli S, Fravolini ML, Palumbo B. PET/CT Radiomics in Lung Cancer: An Overview. Applied Sciences 2020;10:1718. [DOI: 10.3390/app10051718] [Cited by in Crossref: 10] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
17 Alaswad M, Kleefeld C, Foley M. Optimal tumour control for early-stage non-small-cell lung cancer: A radiobiological modelling perspective. Physica Medica 2019;66:55-65. [DOI: 10.1016/j.ejmp.2019.09.074] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
18 Yu Y, Mao L, Lu X, Yuan W, Chen Y, Jiang L, Ding L, Sang L, Xu Z, Tian T, Wu S, Zhuang X, Chu M. Functional Variant in 3'UTR of FAM13A Is Potentially Associated with Susceptibility and Survival of Lung Squamous Carcinoma. DNA Cell Biol 2019;38:1269-77. [PMID: 31539274 DOI: 10.1089/dna.2019.4892] [Cited by in Crossref: 5] [Cited by in F6Publishing: 8] [Article Influence: 1.7] [Reference Citation Analysis]