Optimising use of 4D-CT phase information for radiomics analysis in lung cancer patients treated with stereotactic body radiotherapy

被引:12
作者
Davey, Angela [1 ]
van Herk, Marcel [1 ,2 ]
Faivre-Finn, Corinne [1 ,2 ,3 ]
Brown, Sean [2 ,3 ]
McWilliam, Alan [1 ,2 ]
机构
[1] Univ Manchester, Fac Biol Med & Hlth, Sch Med Sci, Div Canc Sci, Manchester, Lancs, England
[2] Christie NHS Fdn Trust, Dept Radiotherapy Related Res, Manchester, Lancs, England
[3] Christie NHS Fdn Trust, Dept Clin Oncol, Manchester, Lancs, England
关键词
radiomics; 4D-CT; lung cancer; SABR; personalised; biomarkers; MACHINE LEARNING-METHODS; ABLATIVE RADIOTHERAPY; CT; FEATURES; IMAGES; RECURRENCE; UNCERTAINTY; VARIABILITY; DELINEATION; STABILITY;
D O I
10.1088/1361-6560/abfa34
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
Purpose. 4D-CT is routine imaging for lung cancer patients treated with stereotactic body radiotherapy. No studies have investigated optimal 4D phase selection for radiomics. We aim to determine how phase data should be used to identify prognostic biomarkers for distant failure, and test whether stability assessment is required. A phase selection approach will be developed to aid studies with different 4D protocols and account for patient differences. Methods. 186 features were extracted from the tumour and peritumour on all phases for 258 patients. Feature values were selected from phase features using four methods: (A) mean across phases, (B) median across phases, (C) 50% phase, and (D) the most stable phase (closest in value to two neighbours), coined personalised selection. Four levels of stability assessment were also analysed, with inclusion of: (1) all features, (2) stable features across all phases, (3) stable features across phase and neighbour phases, and (4) features averaged over neighbour phases. Clinical-radiomics models were built for twelve combinations of feature type and assessment method. Model performance was assessed by concordance index (c-index) and fraction of new information from radiomic features. Results. The most stable phase spanned the whole range but was most often near exhale. All radiomic signatures provided new information for distant failure prediction. The personalised model had the highest c-index (0.77), and 58% of new information was provided by radiomic features when no stability assessment was performed. Conclusion. The most stable phase varies per-patient and selecting this improves model performance compared to standard methods. We advise the single most stable phase should be determined by minimising feature differences to neighbour phases. Stability assessment over all phases decreases performance by excessively removing features. Instead, averaging of neighbour phases should be used when stability is of concern. The models suggest that higher peritumoural intensity predicts distant failure.
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页数:14
相关论文
共 56 条
[21]   Spatial-temporal variability of radiomic features and its effect on the classification of lung cancer histology [J].
Lafata, Kyle ;
Car, Jing ;
Wang, Chunhao ;
Hong, Julian ;
Kelsey, Chris R. ;
Yin, Fang-Fang .
PHYSICS IN MEDICINE AND BIOLOGY, 2018, 63 (22)
[22]   Association of pre-treatment radiomic features with lung cancer recurrence following stereotactic body radiation therapy [J].
Lafata, Kyle J. ;
Hong, Julian C. ;
Geng, Ruiqi ;
Ackerson, Bradley C. ;
Liu, Jian-Guo ;
Zhou, Zhennan ;
Torok, Jordan ;
Kelsey, Chris R. ;
Yin, Fang-Fang .
PHYSICS IN MEDICINE AND BIOLOGY, 2019, 64 (02)
[23]   Radiomics: the bridge between medical imaging and personalized medicine [J].
Lambin, Philippe ;
Leijenaar, Ralph T. H. ;
Deist, Timo M. ;
Peerlings, Jurgen ;
de Jong, Evelyn E. C. ;
van Timmeren, Janita ;
Sanduleanu, Sebastian ;
Larue, Ruben T. H. M. ;
Even, Aniek J. G. ;
Jochems, Arthur ;
van Wijk, Yvonka ;
Woodruff, Henry ;
van Soest, Johan ;
Lustberg, Tim ;
Roelofs, Erik ;
van Elmpt, Wouter ;
Dekker, Andre ;
Mottaghy, Felix M. ;
Wildberger, Joachim E. ;
Walsh, Sean .
NATURE REVIEWS CLINICAL ONCOLOGY, 2017, 14 (12) :749-762
[24]   Radiomics: Extracting more information from medical images using advanced feature analysis [J].
Lambin, Philippe ;
Rios-Velazquez, Emmanuel ;
Leijenaar, Ralph ;
Carvalho, Sara ;
van Stiphout, Ruud G. P. M. ;
Granton, Patrick ;
Zegers, Catharina M. L. ;
Gillies, Robert ;
Boellard, Ronald ;
Dekker, Andre ;
Aerts, Hugo J. W. L. .
EUROPEAN JOURNAL OF CANCER, 2012, 48 (04) :441-446
[25]   4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers [J].
Larue, Ruben T. H. M. ;
Van De Voorde, Lien ;
van Timmeren, Janna E. ;
Leijenaar, Ralph T. H. ;
Berbee, Maaike ;
Sosef, Meindert N. ;
Schreurs, Wendy M. J. ;
van Elmpt, Wouter ;
Lambin, Philippe .
RADIOTHERAPY AND ONCOLOGY, 2017, 125 (01) :147-153
[26]   A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling [J].
Leger, Stefan ;
Zwanenburg, Alex ;
Pilz, Karoline ;
Lohaus, Fabian ;
Linge, Annett ;
Zoephel, Klaus ;
Kotzerke, Joerg ;
Schreiber, Andreas ;
Tinhofer, Inge ;
Budach, Volker ;
Sak, Ali ;
Stuschke, Martin ;
Balermpas, Panagiotis ;
Roedel, Claus ;
Ganswindt, Ute ;
Belka, Claus ;
Pigorsch, Steffi ;
Combs, Stephanie E. ;
Moennich, David ;
Zips, Daniel ;
Krause, Mechthild ;
Baumann, Michael ;
Troost, Esther G. C. ;
Loeck, Steffen ;
Richter, Christian .
SCIENTIFIC REPORTS, 2017, 7
[27]   CT imaging features associated with recurrence in non-small cell lung cancer patients after stereotactic body radiotherapy [J].
Li, Qian ;
Kim, Jongphil ;
Balagurunathan, Yoganand ;
Qi, Jin ;
Liu, Ying ;
Latifi, Kujtim ;
Moros, Eduardo G. ;
Schabath, Matthew B. ;
Ye, Zhaoxiang ;
Gillies, Robert J. ;
Dilling, Thomas J. .
RADIATION ONCOLOGY, 2017, 12
[28]   Imaging features from pretreatment CT scans are associated with clinical outcomes in nonsmall-cell lung cancer patients treated with stereotactic body radiotherapy [J].
Li, Qian ;
Kim, Jongphil ;
Balagurunathan, Yoganand ;
Liu, Ying ;
Latifi, Kujtim ;
Stringfield, Olya ;
Garcia, Alberto ;
Moros, Eduardo G. ;
Dilling, Thomas J. ;
Schabath, Matthew B. ;
Ye, Zhaoxiang ;
Gillies, Robert J. .
MEDICAL PHYSICS, 2017, 44 (08) :4341-4349
[29]   Stereotactic ablative radiotherapy for early stage non-small cell lung cancer: A critical literature review of predictive factors of relapse [J].
Loganadane, Gokoulakrichenane ;
Martinetti, Florent ;
Mercier, Olaf ;
Krhili, Samar ;
Riet, Francois-Georges ;
Mbagui, Rodrigue ;
To, Hanh ;
Le Pechoux, Cecile ;
Levy, Antonin .
CANCER TREATMENT REVIEWS, 2016, 50 :240-246
[30]   Definition and validation of a radiomics signature for loco-regional tumour control in patients with locally advanced head and neck squamous cell carcinoma [J].
Meneghetti, Asier Rabasco ;
Zwanenburg, Alex ;
Leger, Stefan ;
Leger, Karoline ;
Troost, Esther G. C. ;
Linge, Annett ;
Lohaus, Fabian ;
Schreiber, Andreas ;
Kalinauskaite, Goda ;
Tinhofer, Inge ;
Guberina, Nika ;
Guberina, Maja ;
Balermpas, Panagiotis ;
von der Gruen, Jens ;
Ganswindt, Ute ;
Belka, Claus ;
Peeken, Jan C. ;
Combs, Stephanie E. ;
Boeke, Simon ;
Zips, Daniel ;
Krause, Mechthild ;
Baumann, Michael ;
Loeck, Steffen .
CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY, 2021, 26 :62-70