Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.
Apprentissage automatique; Apprentissage profond; Artificial intelligence; Deep learning; Intelligence artificielle; Machine learning; Radiation therapy; Radiomics; Radiomique; Radiothérapie; Radiology, Nuclear Medicine and imaging; Oncology
Abstract :
[en] Over the last decades, the refinement of radiation therapy techniques has been associated with an increasing interest for individualized radiation therapy with the aim of increasing or maintaining tumor control and reducing radiation toxicity. Developments in artificial intelligence (AI), particularly machine learning and deep learning, in imaging sciences, including nuclear medecine, have led to significant enthusiasm for the concept of "rapid learning health system". AI combined with radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography ([18F]-FDG PET/CT) offers a unique opportunity for the development of predictive models that can help stratify each patient's risk and guide treatment decisions for optimal outcomes and quality of life of patients treated with radiation therapy. Here we present an overview of the current contribution of AI and radiomics-based machine learning models applied to (18F)-FDG PET/CT in the management of cancer treated by radiation therapy.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Lucia, François ; Département de Physique Médicale > Service médical de médecine nucléaire et imagerie onco
Lovinfosse, Pierre ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Schick, U; Radiation Oncology Department, CHU de Brest, 29200 Brest, France, LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
Le Pennec, R; Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
Pradier, O; Radiation Oncology Department, CHU de Brest, 29200 Brest, France, LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
Salaun, P-Y; Service de médecine nucléaire, CHU de Brest, Inserm UMR 1304 (Getbo), université de Bretagne Occidentale, Brest, France
Hustinx, Roland ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Bourbonne, V; Radiation Oncology Department, CHU de Brest, 29200 Brest, France, LaTim, Inserm, UMR 1101, université de Brest, 29200 Brest, France
Language :
English
Title :
Radiotherapy modification based on artificial intelligence and radiomics applied to (18F)-fluorodeoxyglucose positron emission tomography/computed tomography.
Berghmans, T., Dusart, M., Paesmans, M., Hossein-Foucher, C., Buvat, I., Castaigne, C., et al. Primary tumor standardized uptake value (SUVmax) measured on fluorodeoxyglucose positron emission tomography (FDG-PET) is of prognostic value for survival in non-small cell lung cancer (NSCLC): a systematic review and meta-analysis (MA) by the European Lung Cancer Working Party for the IASLC Lung Cancer Staging Project. J Thorac Oncol 3 (2008), 6–12.
Rahman, W.T., Wale, D.J., Viglianti, B.L., Townsend, D.M., Manganaro, M.S., Gross, M.D., et al. The impact of infection and inflammation in oncologic (18F)-FDG PET/CT imaging. Biomed Pharmacother, 117, 2019, 109168.
Kandathil, A., Kay, F.U., Butt, Y.M., Wachsmann, J.W., Subramaniam, R.M., Role of FDG PET/CT in the eighth edition of TNM staging of non-small cell lung cancer. Radiographics 38 (2018), 2134–2149.
Hatt, M., Tixier, F., Visvikis, D., Cheze Le Rest, C., Radiomics in PET/CT: more than meets the eye?. J Nucl Med 58 (2017), 365–366.
Herbst, R.S., Morgensztern, D., Boshoff, C., The biology and management of non-small cell lung cancer. Nature 553 (2018), 446–454.
Hyun, S.H., Kim, H.S., Choi, S.H., Choi, D.W., Lee, J.K., Lee, K.H., et al. Intratumoral heterogeneity of (18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma. Eur J Nucl Med Mol Imaging 43 (2016), 1461–1468.
Shangguan, C., Gan, G., Zhang, J., Wu, J., Miao, Y., Zhang, M., et al. Cancer-associated fibroblasts enhance tumor (18F)-FDG uptake and contribute to the intratumor heterogeneity of PET-CT. Theranostics 8 (2018), 1376–1388.
Uddin, S., Khan, A., Hossain, M.E., Moni, M.A., Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak, 19, 2019, 281.
Bibault, J.-E., Giraud, P., Burgun, A., Big Data and machine learning in radiation oncology: state of the art and future prospects. Cancer Lett 382 (2016), 110–117.
Chen, B., Polatkan, G., Sapiro, G., Blei, D., Dunson, D., Carin, L., Deep learning with hierarchical convolutional factor analysis. IEEE Trans Pattern Anal Mach Intell 35 (2013), 1887–1901.
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G.P.M., Granton, P., et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48 (2012), 441–446.
Papanikolaou, N., Matos, C., Koh, D.M., How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging, 20, 2020, 33.
Visvikis, D., Cheze Le Rest, C., Jaouen, V., Hatt, M., Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging 46 (2019), 2630–2637.
Avanzo, M., Wei, L., Stancanello, J., Vallières, M., Rao, A., Morin, O., et al. Machine and deep learning methods for radiomics. Med Phys 47 (2020), e185–e202.
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., et al. A survey on deep learning in medical image analysis. Med Image Anal 42 (2017), 60–88.
Hatt, M., Le Rest, C.C., Tixier, F., Badic, B., Schick, U., Visvikis, D., et al. Data are also images. J Nucl Med 60 (2019), 38S–44S.
Liu, L., Chen, J., Fieguth, P., Zhao, G., Chellappa, R., Pietikäinen, M., From BoW to CNN: two decades of texture representation for texture classification. Int J Comput Vis 127 (2019), 74–109.
Hatt, M., Krizsan, A.K., Rahmim, A., Bradshaw, T.J., Costa, P.F., Forgacs, A., et al. Joint EANM/SNMMI guideline on radiomics in nuclear medicine: jointly supported by the EANM physics committee and the SNMMI physics, instrumentation and data sciences council. Eur J Nucl Med Mol Imaging 50 (2023), 352–375.
Wu, J., Gensheimer, M.F., Dong, X., Rubin, D.L., Napel, S., Diehn, M., et al. Robust intratumor partitioning to identify high-risk subregions in lung cancer: a pilot study. Int J Radiat Oncol Biol Phys 95 (2016), 1504–1512.
Wu, G., Chen, Y., Wang, Y., Yu, J., Lv, X., Ju, X., et al. Sparse representation-based radiomics for the diagnosis of brain tumors. IEEE Trans Med Imaging 37 (2018), 893–905.
Rahmim, A., Huang, P., Shenkov, N., Fotouhi, S., Davoodi-Bojd, E., Lu, L., et al. Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images. Neuroimage Clin 16 (2017), 539–544.
Zhou, P., Zeng, R., Yu, L., Feng, Y., Chen, C., Li, F., et al. Deep-learning radiomics for discrimination conversion of alzheimer's disease in patients with mild cognitive impairment: a study based on (18F)-FDG PET imaging. Front Aging Neurosci, 13, 2021, 764872.
Yin, G., Song, Y., Li, X., Zhu, L., Su, Q., Dai, D., et al. Prediction of mediastinal lymph node metastasis based on (18F)-FDG PET/CT imaging using support vector machine in non-small cell lung cancer. Eur Radiol 31 (2021), 3983–3992.
Lucia, F., Bourbonne, V., Pleyers, C., Dupré, P.-F., Miranda, O., Visvikis, D., et al. Multicentric development and evaluation of (18F)-FDG PET/CT and MRI radiomics models to predict para-aortic lymph node involvement in locally advanced cervical cancer. Eur J Nucl Med Mol Imaging, 2023, 10.1007/s00259-023-06180-w.
Iantsen, A., Ferreira, M., Lucia, F., Jaouen, V., Reinhold, C., Bonaffini, P., et al. Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting. Eur J Nucl Med Mol Imaging 48 (2021), 3444–3456.
Huang, B., Sollee, J., Luo, Y.-H., Reddy, A., Zhong, Z., Wu, J., et al. Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT. EBioMedicine, 82, 2022, 104127.
Xu, H., Lv, W., Feng, H., Du, D., Yuan, Q., Wang, Q., et al. Subregional radiomics analysis of PET/CT imaging with intratumor partitioning: application to prognosis for nasopharyngeal carcinoma. Mol Imaging Biol 22 (2020), 1414–1426.
Falahatpour, Z., Geramifar, P., Mahdavi, S.R., Abdollahi, H., Salimi, Y., Nikoofar, A., et al. Potential advantages of FDG-PET radiomic feature map for target volume delineation in lung cancer radiotherapy. J Appl Clin Med Phys, 23, 2022, e13696.
Lovinfosse, P., Polus, M., Van Daele, D., Martinive, P., Daenen, F., Hatt, M., et al. FDG PET/CT radiomics for predicting the outcome of locally advanced rectal cancer. Eur J Nucl Med Mol Imaging 45 (2018), 365–375.
Lucia, F., Visvikis, D., Vallières, M., Desseroit, M.-C., Miranda, O., Robin, P., et al. External validation of a combined PET and MRI radiomics model for prediction of recurrence in cervical cancer patients treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging 46 (2019), 864–877.
Ferreira, M., Lovinfosse, P., Hermesse, J., Decuypere, M., Rousseau, C., Lucia, F., et al. (18F)-FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation. Eur J Nucl Med Mol Imaging 48 (2021), 3432–3443.
Lovinfosse, P., Janvary, Z.L., Coucke, P., Jodogne, S., Bernard, C., Hatt, M., et al. FDG PET/CT texture analysis for predicting the outcome of lung cancer treated by stereotactic body radiation therapy. Eur J Nucl Med Mol Imaging 43 (2016), 1453–1460.
Peng, H., Dong, D., Fang, M.-J., Li, L., Tang, L.-L., Chen, L., et al. Prognostic value of deep learning PET/CT-based radiomics: potential role for future individual induction chemotherapy in advanced nasopharyngeal carcinoma. Clin Cancer Res 25 (2019), 4271–4279.
van Dijk, L.V., Noordzij, W., Brouwer, C.L., Boellaard, R., Burgerhof, J.G.M., Langendijk, J.A., et al. (18F)-FDG PET image biomarkers improve prediction of late radiation-induced xerostomia. Radiother Oncol 126 (2018), 89–95.
Lafata, K.J., Chang, Y., Wang, C., Mowery, Y.M., Vergalasova, I., Niedzwiecki, D., et al. Intrinsic radiomic expression patterns after 20 Gy demonstrate early metabolic response of oropharyngeal cancers. Med Phys 48 (2021), 3767–3777.
Suga, M., Nishii, R., Miwa, K., Kamitaka, Y., Yamazaki, K., Tamura, K., et al. Differentiation between non-small cell lung cancer and radiation pneumonitis after carbon-ion radiotherapy by (18F)-FDG PET/CT texture analysis. Sci Rep, 11, 2021, 11509.
Bury, T., Corhay, J.L., Duysinx, B., Daenen, F., Ghaye, B., Barthelemy, N., et al. Value of FDG-PET in detecting residual or recurrent nonsmall cell lung cancer. Eur Respir J 14 (1999), 1376–1380.
Lee, K., Le, T., Hau, E., Hanna, G.G., Gee, H., Vinod, S., et al. A systematic review into the radiologic features predicting local recurrence after stereotactic ablative body radiotherapy (SABR) in patients with non-small cell lung cancer (NSCLC). Int J Radiat Oncol Biol Phys 113 (2022), 40–59.
Brooks, E.D., Verma, V., Senan, S., De Baere, T., Lu, S., Brunelli, A., et al. Salvage therapy for locoregional recurrence after stereotactic ablative radiotherapy for early-stage NSCLC. J Thorac Oncol 15 (2020), 176–189.
Ulaner, G.A., Lyall, A., Identifying and distinguishing treatment effects and complications from malignancy at FDG PET/CT. Radiographics 33 (2013), 1817–1834.
Zwanenburg, A., Vallières, M., Abdalah, M.A., Aerts, H.J.W.L., Andrearczyk, V., Apte, A., et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295 (2020), 328–338.
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F.A., Brendel, W., ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. 2018, arXiv.org [https://arxiv.org/abs/1811.12231v3. Accessed 14 May 2023].
Klyuzhin, I.S., Xu, Y., Ortiz, A., Ferres, J.L., Hamarneh, G., Rahmim, A., Testing the ability of convolutional neural networks to learn radiomic features. medRxiv, 2022 [2020.09.19.20198077].
Hatt, M., Lucia, F., Schick, U., Visvikis, D., Multicentric validation of radiomics findings: challenges and opportunities. EBioMedicine 47 (2019), 20–21.
Tixier, F., Jaouen, V., Hognon, C., Gallinato, O., Colin, T., Visvikis, D., Evaluation of conventional and deep learning based image harmonization methods in radiomics studies. Phys Med Biol, 66, 2021, 10.1088/1361-6560/ac39e5.
Bailey, D.L., Willowson, K.P., Quantitative SPECT/CT: SPECT joins PET as a quantitative imaging modality. Eur J Nucl Med Mol Imaging 41 (2014), S17–S25.
Boeke, S., Winter, R.M., Leibfarth, S., Krueger, M.A., Bowden, G., Cotton, J., et al. Machine learning identifies multi-parametric functional PET/MR imaging cluster to predict radiation resistance in preclinical head and neck cancer models. Eur J Nucl Med Mol Imaging, 2023, 10.1007/s00259-023-06254-9.
