Aerts HJ, Grossmann P, Tan Y, Oxnard GR, Rizvi N, Schwartz LH et al (2016) Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC. Sci Rep 6:33860
Article
CAS
PubMed
PubMed Central
Google Scholar
Ahn HK, Lee H, Kim SG, Hyun SH (2019) Pre-treatment 18F-FDG PET-based radiomics predict survival in resected non-small cell lung cancer. Clin Radiol 74(6):467–473
AI For Medicine. https://www.deeplearning.ai/ai-for-medicine/. Accessed on 30 Oct 2020
AI resources and training. https://www.rsna.org/en/education/ai-resources-and-training. Accessed on 30 Oct 2020
Ait Skourt B, El Hassani A, Majda A (2018) Lung CT image segmentation using deep neural networks. Procedia Computer Science 127:109–113
Article
Google Scholar
Allen G, Chan T. Artificial intelligence and national security. https://www.belfercenter.org/publication/artificial-intelligence-and-national-security. Accessed on 26 Oct 2020
Antunes J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N et al (2016) Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl Oncol 9(2):155–162
Article
PubMed
PubMed Central
Google Scholar
Artificial Intelligence in Healthcare. https://online.stanford.edu/programs/artificial-intelligence-healthcare. Accessed on 30 Oct 2020
Astaraki M, Wang C, Buizza G, Toma-Dasu I, Lazzeroni M, Smedby Ö (2019) Early survival prediction in non-small cell lung cancer from PET/CT images using an intra-tumor partitioning method. Phys Med 60:58–65
Article
PubMed
Google Scholar
Baek S, He Y, Allen BG, Buatti JM, Smith BJ, Tong L et al (2019) Deep segmentation networks predict survival of non-small cell lung cancer. Sci Rep 9(1):17286
Article
PubMed
PubMed Central
CAS
Google Scholar
Bailly C, Bodet-Milin C, Couespel S, Necib H, Kraeber-Bodéré F, Ansquer C et al (2016) Revisiting the robustness of PET-based textural features in the context of multi-centric trials. PLoS One 11(7):e0159984
Article
PubMed
PubMed Central
CAS
Google Scholar
Blanc-Durand P, Campedel L, Mule S, Jegou S, Luciani A, Pigneur F et al (2020) Prognostic value of anthropometric measures extracted from whole-body CT using deep learning in patients with non-small-cell lung cancer. Eur Radiol 30(6):3528–3537
Article
PubMed
Google Scholar
Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R (2010) Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care 48(6 Suppl):S45–S51
Article
PubMed
Google Scholar
Bug D, Feuerhake F, Oswald E, Schüler J, Merhof D (2019) Semi-automated analysis of digital whole slides from humanized lung-cancer xenograft models for checkpoint inhibitor response prediction. Oncotarget. 10(44):4587–4597
Article
PubMed
PubMed Central
Google Scholar
Caramella C, Bluthgen MV, Rosellini S, Leduc C, Facchinetti F, Haspinger E et al (2015) 3133 Prognostic value of texture analysis and correlation with molecular profile in EGFR mutated/ALK rearranged advanced non-small cell lung cancer (NSCLC). Eur J Cancer 51:S647–S6S8
Article
Google Scholar
Chang K, Balachandar N, Lam C, Yi D, Brown J, Beers A et al (2018) Distributed deep learning networks among institutions for medical imaging. J Am Med Inform Assoc 25(8):945–954
Article
PubMed
PubMed Central
Google Scholar
Chartrand G, Cheng PM, Vorontsov E, Drozdzal M, Turcotte S, Pal CJ et al (2017) Deep learning: a primer for radiologists. Radiographics. 37(7):2113–2131
Article
PubMed
Google Scholar
Chaudri NA (2004) Adherence to Long-term Therapies Evidence for Action. Ann Saudi Med 24(3):221–222
Article
PubMed Central
Google Scholar
Cheebsumon P, Boellaard R, de Ruysscher D, van Elmpt W, van Baardwijk A, Yaqub M et al (2012) Assessment of tumour size in PET/CT lung cancer studies: PET- and CT-based methods compared to pathology. EJNMMI Res 2(1):56
Article
PubMed
PubMed Central
Google Scholar
Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJ (2013) Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40(1):133–140
Collins GS, Moons KGM (2019) Reporting of artificial intelligence prediction models. Lancet. 393(10181):1577–1579
Article
PubMed
Google Scholar
Collins GS, Reitsma JB, Altman DG, Moons KG (2015) Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 350:g7594
Article
PubMed
Google Scholar
Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G et al (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114(3):345–350
Article
PubMed
PubMed Central
Google Scholar
Coronary flow reserve and the J curve (1988) BMJ 297(6663):1606–1608
Google Scholar
Davenport T, Kalakota R (2019) The potential for artificial intelligence in healthcare. Future Healthc J 6(2):94–98
Article
PubMed
PubMed Central
Google Scholar
De Jong EEC, Van Elmpt W, Hendriks LEL, Leijenaar RTH, Dingemans AMC, Lambin P (2016) OC-0609: Radiomic CT features for evaluation of EGFR and KRAS mutation status in patients with advanced NSCLC. Radiother Oncol 119:S290–S2S1
Article
Google Scholar
Deist TM, Dankers FJWM, Ojha P, Scott Marshall M, Janssen T, Faivre-Finn C et al (2020) Distributed learning on 20 000+ lung cancer patients - the personal health train. Radiother Oncol 144:189–200
Article
PubMed
Google Scholar
Desseroit MC, Visvikis D, Tixier F, Majdoub M, Perdrisot R, Guillevin R et al (2016) Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I-III. Eur J Nucl Med Mol Imaging 43(8):1477–1485
Dissaux G, Visvikis D, Da-Ano R, Pradier O, Chajon E, Barillot I et al (2020) Pre-treatment (18)F-FDG PET/CT Radiomics predict local recurrence in patients treated with stereotactic radiotherapy for early-stage non-small cell lung cancer: a multicentric study. J Nucl Med 61(6):814–820
Article
CAS
PubMed
Google Scholar
ECIS. European Cancer Information System. https://ecis.jrc.ec.europa.eu. Accessed on 28 Aug 2020
Emaminejad N, Qian W, Guan Y, Tan M, Qiu Y, Liu H et al (2016) Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng 63(5):1034–1043
Article
PubMed
Google Scholar
Emblem KE, Pinho MC, Zöllner FG, Due-Tonnessen P, Hald JK, Schad LR et al (2015) A generic support vector machine model for preoperative glioma survival associations. Radiology. 275(1):228–234
Article
PubMed
Google Scholar
ESMIT Autumn School (2020) https://www.eanm.org/esmit/level-2/esmit-autumn-school-2020-3/. Accessed on 30 Oct 2020
Eyuboglu E (2019) On the automatic generation of FDG-PET-CT reports
Google Scholar
FDA. Proposed regulatory framework for modifications to artificial intelligence/machine learning (ai/ML)-based software as a medical device (SaMD) - discussion paper and request for feedback; 2019.
Google Scholar
Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, Fabro AT, Azevedo-Marques PM (2018) Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Prog Biomed 159:23–30
Article
Google Scholar
Ganeshan B, Abaleke S, Young RC, Chatwin CR, Miles KA (2010) Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 10:137–143
Article
PubMed
PubMed Central
Google Scholar
Gatta R, Depeursinge A, Ratib O, Michielin O, Leimgruber A (2020) Integrating radiomics into holomics for personalised oncology: from algorithms to bedside. Eur Radiol Exp 4(1):11
Article
PubMed
PubMed Central
Google Scholar
Gevaert O, Xu J, Hoang CD, Leung AN, Xu Y, Quon A et al (2012) Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results. Radiology. 264(2):387–396
Article
PubMed
PubMed Central
Google Scholar
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology. 278(2):563–577
Article
PubMed
Google Scholar
Grootjans W, Tixier F, van der Vos CS, Vriens D, Le Rest CC, Bussink J et al (2016) The impact of optimal respiratory gating and image noise on evaluation of intratumor heterogeneity on 18F-FDG PET imaging of lung cancer. J Nucl Med 57(11):1692–1698
Grossmann P, Stringfield O, El-Hachem N, Bui MM, Rios Velazquez E, Parmar C et al (2017) Defining the biological basis of radiomic phenotypes in lung cancer. Elife. 6:e23421
Article
PubMed
PubMed Central
Google Scholar
Hagendorff T (2020) The ethics of ai ethics: an evaluation of guidelines. Mind Mach 30(1):99–120
Article
Google Scholar
Halpenny DF, Riely GJ, Hayes S, Yu H, Zheng J, Moskowitz CS et al (2014) Are there imaging characteristics associated with lung adenocarcinomas harboring ALK rearrangements? Lung Cancer 86(2):190–194
Article
PubMed
Google Scholar
Handelman GS, Kok HK, Chandra RV, Razavi AH, Lee MJ, Asadi H (2018) eDoctor: machine learning and the future of medicine. J Intern Med 284(6):603–619
Article
CAS
PubMed
Google Scholar
Hao H, Zhou Z, Li S, Maquilan G, Folkert MR, Iyengar P et al (2018) Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer. Phys Med Biol 63(9):095007
Article
PubMed
PubMed Central
CAS
Google Scholar
Hawkins SH, Korecki JN, Balagurunathan Y, Gu Y, Kumar V, Basu S et al (2014) Predicting outcomes of nonsmall cell lung cancer using CT image features. IEEE Access 2:1418–1426
Article
Google Scholar
Holzinger A, Haibe-Kains B, Jurisica I (2019) Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data. Eur J Nucl Med Mol Imaging 46:2722
Article
PubMed
Google Scholar
https://www.hunimed.eu/course/medtec-school/. Accessed on 26 Oct 2020
https://www.radiologybusiness.com/topics/artificial-intelligence/wait-will-ai-replace-radiologists-after-all. Accessed on 26 Oct 2020
Huang S, Yang J, Fong S, Zhao Q (2019) Mining prognosis index of brain metastases using artificial intelligence. Cancers (Basel) 11(8):1140
Article
Google Scholar
Hustinx R (2019) Physician centred imaging interpretation is dying out - why should I be a nuclear medicine physician? Eur J Nucl Med Mol Imaging 46(13):2708–2714
Article
PubMed
Google Scholar
Huynh E, Coroller TP, Narayan V, Agrawal V, Hou Y, Romano J et al (2016) CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother Oncol 120(2):258–266
Article
PubMed
Google Scholar
Hyun SH, Ahn MS, Koh YW, Lee SJ (2019) A Machine-learning approach using PET-based radiomics to predict the histological subtypes of lung cancer. Clin Nucl Med 44(12):956–960
Article
PubMed
Google Scholar
Ibrahim A, Primakov S, Beuque M, Woodruff HC, Halilaj I, Wu G et al (2020) Radiomics for precision medicine: Current challenges, future prospects, and the proposal of a new framework. Methods. S1046-2023:30111
Google Scholar
Ibrahim A, Vallières M, Woodruff H, Primakov S, Beheshti M, Keek S, et al (2019) Radiomics Analysis for Clinical Decision Support in Nuclear Medicine. Seminars in Nuclear Medicine 49
James G, Witten D, Hastie T, Tibshirani R (2013) Statistical learning. In: James G, Witten D, Hastie T, Tibshirani R (eds) An introduction to statistical learning: with applications in R. Springer New York, New York, pp 15–57
Chapter
Google Scholar
Jayasurya K, Fung G, Yu S, Dehing-Oberije C, De Ruysscher D, Hope A et al (2010) Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Med Phys 37(4):1401–1407
Article
CAS
PubMed
Google Scholar
Jemaa S, Fredrickson J, Carano RAD, Nielsen T, de Crespigny A, Bengtsson T (2020) Tumor segmentation and feature extraction from whole-body FDG-PET/CT using cascaded 2D and 3D convolutional neural networks. J Digit Imaging
Jiang M, Sun D, Guo Y, Guo Y, Xiao J, Wang L et al (2020) Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result. Acad Radiol 27(2):171–179
Article
PubMed
Google Scholar
Jiang M, Zhang Y, Xu J, Ji M, Guo Y, Guo Y et al (2019) Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT. Nucl Med Commun 40(8):842–849
Article
CAS
PubMed
Google Scholar
Karami M (2015) Clinical decision support systems and medical imaging. Radiol Manage 37(2):25–32 quiz 3-4
PubMed
Google Scholar
Khorrami M, Khunger M, Zagouras A, Patil P, Thawani R, Bera K et al (2019) Combination of peri- and intratumoral radiomic features on baseline CT scans predicts response to chemotherapy in lung adenocarcinoma. Radiol Artif Intell 1(2):e180012
Article
PubMed
Google Scholar
Kirienko M, Biroli M, Gelardi F, Seregni E, Chiti A, Sollini M (in Press) Where do we stand?
Kirienko M, Cozzi L, Rossi A, Voulaz E, Antunovic L, Fogliata A et al (2018a) Ability of FDG PET and CT radiomics features to differentiate between primary and metastatic lung lesions. Eur J Nucl Med Mol Imaging 45(10):1649–1660
Article
PubMed
Google Scholar
Kirienko M, Sollini M, Silvestri G, Mognetti S, Voulaz E, Antunovic L et al (2018b) Convolutional neural networks promising in lung cancer T-parameter assessment on baseline FDG-PET/CT. Contrast Media Mol Imaging 2018:1382309
Article
PubMed
PubMed Central
CAS
Google Scholar
Kocak B, Kus EA, Kilickesmez O (2020) How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts. Eur Radiol
Koyasu S, Nishio M, Isoda H, Nakamoto Y, Togashi K (2019) Usefulness of gradient tree boosting for predicting histological subtype and EGFR mutation status of non-small cell lung cancer on 18F FDG-PET/CT. (2020) Ann Nucl Med 34:49–57.
Lafata KJ, Hong JC, Geng R, Ackerson BG, Liu JG, Zhou Z et al (2019) Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy. Phys Med Biol 64(2):025007
Article
PubMed
Google Scholar
Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762
Article
PubMed
Google Scholar
Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS et al (2013) Stability of FDG-PET Radiomics features: an integrated analysis of test-retest and inter-observer variability. Acta Oncol 52(7):1391–1397
Article
CAS
PubMed
Google Scholar
Leung KH, Marashdeh W, Wray R, Ashrafinia S, Pomper MG, Rahmim A et al (2020) A physics-guided modular deep-learning based automated framework for tumor segmentation in PET. Phys Med Biol
Li X, Yin G, Zhang Y, Dai D, Liu J, Chen P et al (2019) Predictive power of a radiomic signature based on 18F-FDG PET/CT images for EGFR mutational status in NSCLC. Front Oncol 9:1062
Article
PubMed
PubMed Central
Google Scholar
Li XY, Xiong JF, Jia TY, Shen TL, Hou RP, Zhao J et al (2018) Detection of epithelial growth factor receptor. J Thorac Dis 10(12):6624–6635
Article
PubMed
PubMed Central
Google Scholar
Lian C, Ruan S, Denœux T, Jardin F, Vera P (2016) Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Med Image Anal 32:257–268
Article
PubMed
Google Scholar
Liao F, Liang M, Li Z, Hu X, Song S (2019) Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-OR network. IEEE Trans Neural Netw Learn Syst 30(11):3484–3495
Article
PubMed
Google Scholar
Luo Y, McShan DL, Matuszak MM, Ray D, Lawrence TS, Jolly S et al (2018) A multiobjective Bayesian networks approach for joint prediction of tumor local control and radiation pneumonitis in nonsmall-cell lung cancer (NSCLC) for response-adapted radiotherapy Jun 4:10.1002/mp.13029. Med Phys 45(8):3980
Article
CAS
Google Scholar
Mandl KD, Glauser T, Krantz ID, Avillach P, Bartels A, Beggs AH et al (2020) The genomics research and innovation network: creating an interoperable, federated, genomics learning system. Genet Med 22(2):371–380
Article
CAS
PubMed
Google Scholar
Minsky M, Papert S (1972) Perceptrons : an introduction to computational geometry
Google Scholar
Montgomery DW, Amira A, Zaidi H (2007) Fully automated segmentation of oncological PET volumes using a combined multiscale and statistical model. Med Phys 34(2):722–736
Article
PubMed
Google Scholar
Moore JH, Raghavachari N, Speakers W (2019) Artificial intelligence based approaches to identify molecular determinants of exceptional health and life span-an interdisciplinary workshop at the National Institute on Aging. Front Artif Intell 2:12
Article
PubMed
PubMed Central
Google Scholar
Nair VS, Gevaert O, Davidzon G, Napel S, Graves EE, Hoang CD et al (2012) Prognostic PET 18F-FDG uptake imaging features are associated with major oncogenomic alterations in patients with resected non-small cell lung cancer. Cancer Res 72(15):3725–3734
Nair VS, Gevaert O, Davidzon G, Plevritis SK, West R (2014) NF-κB protein expression associates with 18F-FDG PET tumor uptake in non-small cell lung cancer: a radiogenomics validation study to understand tumor metabolism. Lung Cancer 83(2):189–196
Ninatti G, Kirienko M, Neri E, Sollini M, Chiti A (2020) Imaging-based prediction of molecular therapy targets in NSCLC by radiogenomics and AI approaches: a systematic review. Diagnostics (Basel) 10(6):359
Article
Google Scholar
Ohri N, Duan F, Snyder BS, Wei B, Machtay M, Alavi A et al (2016) Pretreatment 18F-FDG PET textural features in locally advanced non-small cell lung cancer: secondary analysis of ACRIN 6668/RTOG 0235. J Nucl Med 57(6):842–848
Article
CAS
PubMed
Google Scholar
Oikonomou A, Khalvati F, Tyrrell PN, Haider MA, Tarique U, Jimenez-Juan L et al (2018) Radiomics analysis at PET/CT contributes to prognosis of recurrence and survival in lung cancer treated with stereotactic body radiotherapy. Sci Rep 8(1):4003
Article
PubMed
PubMed Central
CAS
Google Scholar
Oliver JA, Budzevich M, Zhang GG, Dilling TJ, Latifi K, Moros EG (2015) Variability of image features computed from conventional and respiratory-gated PET/CT images of lung cancer. Transl Oncol 8(6):524–534
Article
PubMed
PubMed Central
Google Scholar
Ongena YP, Haan M, Yakar D, Kwee TC (2020) Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire. Eur Radiol 30(2):1033–1040
Article
PubMed
Google Scholar
Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 1(2):207–226
Article
PubMed
PubMed Central
Google Scholar
Paul R, Hawkins SH, Balagurunathan Y, Schabath MB, Gillies RJ, Hall LO et al (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography. 2(4):388–395
Article
PubMed
PubMed Central
Google Scholar
Perk T, Bradshaw T, Chen S, Im HJ, Cho S, Perlman S et al (2018) Automated classification of benign and malignant lesions in 18F-NaF PET/CT images using machine learning. Phys Med Biol 63(22):225019
Pesapane F, Volonté C, Codari M, Sardanelli F (2018) Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States. Insights into Imaging 9(5):745–753
Article
PubMed
PubMed Central
Google Scholar
Pinto Dos Santos D, Giese D, Brodehl S, Chon SH, Staab W, Kleinert R et al (2019) Medical students' attitude towards artificial intelligence: a multicentre survey. Eur Radiol 29(4):1640–1646
Article
CAS
PubMed
Google Scholar
Pons E, Braun LM, Hunink MG, Kors JA (2016) Natural language processing in radiology: a systematic review. Radiology. 279(2):329–343
Article
PubMed
Google Scholar
Poole D, Mackworth A, Goebel R (1998) Computational intelligence: a logical approach
Google Scholar
Porenta G (2019) Is there value for artificial intelligence applications in molecular imaging and nuclear medicine? J Nucl Med 60(10):1347–1349
Article
PubMed
Google Scholar
Remedios S, Roy S, Blaber J, Bermudez C, Nath V, Patel MB et al (2019) Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury. Proc SPIE Int Soc Opt Eng 10949:109490A
PubMed
PubMed Central
Google Scholar
Remedios SW, Roy S, Bermudez C, Patel MB, Butman JA, Landman BA et al (2020) Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation. Med Phys 47(1):89–98
Article
PubMed
Google Scholar
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Article
PubMed
Google Scholar
Schwyzer M, Ferraro DA, Muehlematter UJ, Curioni-Fontecedro A, Huellner MW, von Schulthess GK et al (2018) Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results. Lung Cancer 126:170–173
Article
PubMed
Google Scholar
Sheller MJ, Reina GA, Edwards B, Martin J, Bakas S (2019) Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. Brainlesion. 11383:92–104
PubMed
PubMed Central
Google Scholar
Shen D, Wu G, Suk HI (2017) Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 19:221–248
Article
CAS
PubMed
PubMed Central
Google Scholar
Sibille L, Seifert R, Avramovic N, Vehren T, Spottiswoode B, Zuehlsdorff S et al (2020) F-FDG PET/CT uptake classification in lymphoma and lung cancer by using deep convolutional neural networks. Radiology. 294(2):445–452
Article
PubMed
Google Scholar
Sim I, Gorman P, Greenes RA, Haynes RB, Kaplan B, Lehmann H et al (2001) Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 8(6):527–534
Article
CAS
PubMed
PubMed Central
Google Scholar
Smith JA, Abhari RE, Hussain Z, Heneghan C, Collins GS, Carr AJ (2020) Industry ties and evidence in public comments on the FDA framework for modifications to artificial intelligence/machine learning-based medical devices: a cross sectional study. BMJ Open 10(10):e039969
Article
PubMed
PubMed Central
Google Scholar
Sollini M, Antunovic L, Chiti A, Kirienko M (2019a) Towards clinical application of image mining: a systematic review on artificial intelligence and radiomics. Eur J Nucl Med Mol Imaging 46(13):2656–2672
Article
PubMed
PubMed Central
Google Scholar
Sollini M, Bandera F, Kirienko M (2019b) Quantitative imaging biomarkers in nuclear medicine: from SUV to image mining studies. Highlights from annals of nuclear medicine 2018. Eur J Nucl Med Mol Imaging 46(13):2737–2745
Article
PubMed
Google Scholar
Sollini M, Cozzi L, Chiti A, Kirienko M (2018) Texture analysis and machine learning to characterize suspected thyroid nodules and differentiated thyroid cancer: where do we stand? Eur J Radiol 99:1–8
Article
PubMed
Google Scholar
Sollini M, Cozzi L, Ninatti G, Antunovic L, Cavinato L, Chiti A et al. (2020a) PET/CT radiomics in breast cancer: Mind the step. Methods S1046-2023(19)30263–4
Sollini M, Gelardi F, Matassa G, Delgado Bolton RC, Chiti A, Kirienko M (2020b) Interdisciplinarity: an essential requirement for translation of radiomics research into clinical practice -a systematic review focused on thoracic oncology. Rev Esp Med Nucl Imagen Mol 39(3):146–156
CAS
PubMed
Google Scholar
Song SH, Park H, Lee G, Lee HY, Sohn I, Kim HS et al (2017) Imaging phenotyping using radiomics to predict micropapillary pattern within lung adenocarcinoma. J Thorac Oncol 12(4):624–632
Article
PubMed
Google Scholar
Soufi M, Kamali-Asl A, Geramifar P, Rahmim A (2017) A novel framework for automated segmentation and labeling of homogeneous versus heterogeneous lung tumors in [ 18F]FDG-PET Imaging. Mol Imaging Biol 19(3):456–468
Spyns P (1996) Natural language processing in medicine: an overview. Methods Inf Med 35(4-5):285–301
CAS
PubMed
Google Scholar
Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J Royal Stat Soc Series B (Methodological) 36(2):111–147
Article
Google Scholar
Suk H-I, Liu M, Yan P, Lian C. Machine Learning in Medical Imaging 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 2019.
Sun YW, Xu J, Zhou J, Liu WJ (2018) Targeted drugs for systemic therapy of lung cancer with brain metastases. Oncotarget. 9(4):5459–5472
Article
PubMed
Google Scholar
Suzuki K (2012) A review of computer-aided diagnosis in thoracic and colonic imaging. Quant Imaging Med Surg 2(3):163–176
PubMed
PubMed Central
Google Scholar
Tau N, Stundzia A, Yasufuku K, Hussey D, Metser U (2020) Convolutional neural networks in predicting nodal and distant metastatic potential of newly diagnosed non-small cell lung cancer on FDG PET images. AJR Am J Roentgenol 215(1):192–197
Article
PubMed
Google Scholar
Thie JA (2004) Understanding the standardized uptake value, its methods, and implications for usage. J Nucl Med 45(9):1431–1434
PubMed
Google Scholar
Tixier F, Hatt M, Valla C, Fleury V, Lamour C, Ezzouhri S et al (2014) Visual versus quantitative assessment of intratumor 18F-FDG PET uptake heterogeneity: prognostic value in non-small cell lung cancer. J Nucl Med 55(8):1235–1241
Tseng H-H, Luo Y, Cui S, Chien J-T, Ten Haken RK, Naqa IE (2017) Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys 44(12):6690–6705
Article
CAS
PubMed
Google Scholar
Tzanoukos G, Athanasiadis E, Gaitanis A, Georgakopoulos A, Chatziioannou A, Chatziioannou S et al (2016) SPNsim: A database of simulated solitary pulmonary nodule PET/CT images facilitating computer aided diagnosis. J Biomed Inform 63:357–365
Article
CAS
PubMed
Google Scholar
Tzanoukos G, Kafouris P, Georgakopoulos A, Gaitanis A, Maroulis D, Chatziioannou S et al (2019) Design and initial implementation of a computer aided diagnosis system for PET/CT solitary pulmonary nodule risk estimation. In: 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE), pp 28–30
Google Scholar
Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14(11):e0224365
Article
CAS
PubMed
PubMed Central
Google Scholar
Vallières M, Freeman CR, Skamene SR, El Naqa I (2015) A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol 60(14):5471–5496
Article
PubMed
Google Scholar
van Amsterdam WAC, Verhoeff JJC, de Jong PA, Leiner T, Eijkemans MJC (2019) Eliminating biasing signals in lung cancer images for prognosis predictions with deep learning. NPJ Digit Med 2:122
Article
PubMed
PubMed Central
Google Scholar
van Engelen JE, Hoos HH (2020) A survey on semi-supervised learning. Mach Learn 109(2):373–440
Article
Google Scholar
van Hoek J, Huber A, Leichtle A, Härmä K, Hilt D, von Tengg-Kobligk H et al (2019) A survey on the future of radiology among radiologists, medical students and surgeons: students and surgeons tend to be more skeptical about artificial intelligence and radiologists may fear that other disciplines take over. Eur J Radiol 121:108742
Article
PubMed
Google Scholar
van Velden FH, Kramer GM, Frings V, Nissen IA, Mulder ER, de Langen AJ et al (2016) Repeatability of radiomic features in non-small-cell lung cancer [18F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol 18(5):788–795
Varma S, Simon R (2006) Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics 7:91
Article
PubMed
PubMed Central
CAS
Google Scholar
Vepakomma P, Gupta O, Swedish T, Raskar R (2018) Split learning for health: distributed deep learning without sharing raw patient data. arXiv 1812:00564
Google Scholar
Volpp K, Mohta S (2016) Improved engagement leads to better outcomes, but better tools are needed.: NEJM Catalyst
Google Scholar
Wang H, Zhou Z, Li Y, Chen Z, Lu P, Wang W et al (2017b) Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from. EJNMMI Res 7(1):11
Article
PubMed
PubMed Central
Google Scholar
Wang S, Shi J, Ye Z, Dong D, Yu D, Zhou M et al (2019b) Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning. Eur Respir J 53(3):1800986
Article
PubMed
PubMed Central
Google Scholar
Wang S, Zhou M, Liu Z, Gu D, Zang Y, Dong D et al (2017a) Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation. Med Image Anal 40:172–183
Article
PubMed
PubMed Central
Google Scholar
Wang X, Kong C, Xu W, Yang S, Shi D, Zhang J et al (2019a) Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT-based radiomics signature. Thorac Cancer 10(10):1904–1912
Article
CAS
PubMed
PubMed Central
Google Scholar
Way GP, Allaway RJ, Bouley SJ, Fadul CE, Sanchez Y, Greene CS (2017) A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma. BMC Genomics 18(1):127
Article
PubMed
PubMed Central
CAS
Google Scholar
Weikert T, Akinci D'Antonoli T, Bremerich J, Stieltjes B, Sommer G, Sauter AW (2019) Evaluation of an AI-powered lung nodule algorithm for detection and 3D segmentation of primary lung tumors. Contrast Media Mol Imaging 2019:1545747
PubMed
PubMed Central
Google Scholar
Weiss GJ, Ganeshan B, Miles KA, Campbell DH, Cheung PY, Frank S et al (2014) Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. PLoS One 9(7):e100244
Article
PubMed
PubMed Central
CAS
Google Scholar
Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A et al (2016) The FAIR guiding principles for scientific data management and stewardship. Sci Data 3:160018
Article
PubMed
PubMed Central
Google Scholar
Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J et al (2016) Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 6:71
PubMed
PubMed Central
Google Scholar
Wu Y, Liu J, Han C, Liu X, Chong Y, Wang Z et al (2020) Preoperative prediction of lymph node metastasis in patients with early-T-stage non-small cell lung cancer by machine learning algorithms. Front Oncol 10:743
Article
PubMed
PubMed Central
Google Scholar
Xiong JF, Jia TY, Li XY, Yu W, Xu ZY, Cai XW et al (2018) Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks. Br J Radiol 91(1092):20180334
Article
PubMed
PubMed Central
Google Scholar
Xu Y, Hosny A, Zeleznik R, Parmar C, Coroller T, Franco I et al (2019) Deep learning predicts lung cancer treatment response from serial medical imaging. Clin Cancer Res 25(11):3266–3275
Article
PubMed
PubMed Central
Google Scholar
Yoon HJ, Sohn I, Cho JH, Lee HY, Kim JH, Choi YL et al (2015) Decoding tumor phenotypes for ALK, ROS1, and RET fusions in lung adenocarcinoma using a radiomics approach. Medicine (Baltimore) 94(41):e1753
Article
CAS
Google Scholar
Yoon J, Suh YJ, Han K, Cho H, Lee HJ, Hur J et al (2020) Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas. Thorac Cancer 11(4):993–1004
Article
CAS
PubMed
PubMed Central
Google Scholar
Ypsilantis PP, Siddique M, Sohn HM, Davies A, Cook G, Goh V et al (2015) Predicting response to neoadjuvant chemotherapy with PET imaging using convolutional neural networks. PLoS One 10(9):e0137036
Article
PubMed
PubMed Central
CAS
Google Scholar
Zhang H, Molitoris J, Tan S, Giacomelli I, Scartoni D, Gzell C et al (2016) SU-F-R-04: radiomics for survival prediction in glioblastoma (GBM). Med Phys 43(6Part6):3373
Article
Google Scholar
Zhang L, Chen B, Liu X, Song J, Fang M, Hu C et al (2018) Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer. Transl Oncol 11(1):94–101
Article
PubMed
Google Scholar
Zhang R, Cheng C, Zhao X, Li X (2019) Multiscale mask R-CNN-based lung tumor detection using PET imaging. Mol Imaging 18:1536012119863531
Article
PubMed
PubMed Central
Google Scholar
Zhao W, Yang J, Ni B, Bi D, Sun Y, Xu M et al (2019) Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning. Cancer Med 8(7):3532–3543
Article
CAS
PubMed
PubMed Central
Google Scholar
Zittrain J. Ethics and governance of artificial intelligence. https://www.media.mit.edu/groups/ethics-and-governance/overview/. Accessed on 26 Oct 2020