Using machine learning to predict radiation pneumonitis in patients with stage I non-small cell lung cancer treated with stereotactic body radiation therapy

Physics in Medicine and Biology

Volumn 61, Issue 16, 2016, Pages 6105-6120

  Valdes, Gilmer ^a   Solberg, Timothy D ^a   Heskel, Marina ^a   Ungar, Lyle ^b   Simone, Charles B ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

machine learning, Decision Trees; non small cell lung cancer; radiation pneumonitis; Random Forests; RUSBoost; stereotactic body radiation therapy (SBRT)

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; BIOLOGICAL ORGANS; CARBON MONOXIDE; COMPUTATIONAL COMPLEXITY; DECISION TREES; DISEASES; ERRORS; FORECASTING; FORESTRY; LEARNING SYSTEMS; MULTIVARIANT ANALYSIS; PATIENT MONITORING; RADIATION; RESPIRATORY SYSTEM;

NON SMALL CELL LUNG CANCER; RADIATION PNEUMONITIS; RANDOM FORESTS; RUSBOOST; STEREOTACTIC BODY RADIATION THERAPY;

RADIOTHERAPY;

AGED; ALGORITHM; CARCINOMA, NON-SMALL-CELL LUNG; FEMALE; HUMAN; LUNG NEOPLASMS; MACHINE LEARNING; MALE; MIDDLE AGED; MULTIVARIATE ANALYSIS; RADIATION PNEUMONITIS; RADIOSURGERY; REPRODUCIBILITY; VERY ELDERLY;

AGED; AGED, 80 AND OVER; ALGORITHMS; CARCINOMA, NON-SMALL-CELL LUNG; FEMALE; HUMANS; LUNG NEOPLASMS; MACHINE LEARNING; MALE; MIDDLE AGED; MULTIVARIATE ANALYSIS; RADIATION PNEUMONITIS; RADIOSURGERY; REPRODUCIBILITY OF RESULTS;

EID: 84984698490     PISSN: 00319155     EISSN: 13616560     Source Type: Journal    
DOI: 10.1088/0031-9155/61/16/6105     Document Type: Article

References (35)

1. Arias M and Diez F J 2014 The problem of embedded decision nodes in cost-effectiveness Decision Trees Pharmacoeconomics 32 1141-5
2. Breiman L 2001 Random forests Mach. Learn. 45 5-32
3. Breiman L 2004 Consistency for a simple model of random forests Technical Report 670
4. Chang D T, Olivier K R, Morris C G, Liu C, Dempsey J F, Benda R K and Palta J R 2006 The impact of heterogeneity correction on dosimetric parameters that predict for radiation pneumonitis Int. J. Radiat. Oncol. Biol. Phys. 65 125-31
5. Chen S, Zhou S, Yin F F, Marks L B and Das S K 2007 Investigation of the support vector machine algorithm to predict lung radiation-induced pneumonitis Med. Phys. 34 3808-14
6. Cunliffe A, Armato S G III, Castillo R, Pham N, Guerrero T and Al-Hallaq H A 2015 Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development Int. J. Radiat. Oncol. Biol. Phys. 91 1048-56
7. Dang J, Li G, Zang S, Zhang S and Yao L 2014 Comparison of risk and predictors for early radiation pneumonitis in patients with locally advanced non-small cell lung cancer treated with radiotherapy with or without surgery Lung Cancer 86 329-33
8. El Naqa I, Bradley J, Blanco A I, Lindsay P E, Vicic M, Hope A and Deasy J O 2006 Multivariable modeling of radiotherapy outcomes, including dose-volume and clinical factors Int. J. Radiat. Oncol. Biol. Phys. 64 1275-86
9. El Naqa I, Bradley J D, Lindsay P E, Hope A J and Deasy J O 2009 Predicting radiotherapy outcomes using statistical learning techniques Phys. Med. Biol. 54 S9-30
10. Graham M V, Purdy J A, Emami B, Harms W, Bosch W, Lockett M A and Perez C A 1999 Clinical dose-volume histogram analysis for pneumonitis after 3D treatment for non-small cell lung cancer (NSCLC) Int. J. Radiat. Oncol. Biol. Phys. 45 323-9
11. Hastie T, Tibshirani R and Friedman J 2009 The Elements of Statistical Learning: Data Mining, Inference, and Prediction 2nd edn (New York: Springer)
12. Hernando M L et al 2001 Radiation-induced pulmonary toxicity: a dose-volume histogram analysis in 201 patients with lung cancer Int. J. Radiat. Oncol. Biol. Phys. 51 650-9
13. Hope A J, Lindsay P E, El Naqa I, Alaly J R, Vicic M, Bradley J D and Deasy J O 2006 Modeling radiation pneumonitis risk with clinical, dosimetric, and spatial parameters Int. J. Radiat. Oncol. Biol. Phys. 65 112-24
14. Kang J, Schwartz R, Flickinger J and Beriwal S 2015 Machine learning approaches for predicting radiation therapy outcomes: a clinician's perspective Int. J. Radiat. Oncol. Biol. Phys. 93 1127-35
15. Kim M, Lee J, Ha B, Lee R, Lee K J and Suh H S 2011 Factors predicting radiation pneumonitis in locally advanced non-small cell lung cancer Radiat. Oncol. J. 29 181-90
16. Klement R J et al 2014 Support vector machine-based prediction of local tumor control after stereotactic body radiation therapy for early-stage non-small cell lung cancer Int. J. Radiat. Oncol. Biol. Phys. 88 732-8
17. Kong F M et al 2006 Final toxicity results of a radiation-dose escalation study in patients with non-small-cell lung cancer (NSCLC): predictors for radiation pneumonitis and fibrosis Int. J. Radiat. Oncol. Biol. Phys. 65 1075-86
18. Kwa S L et al 1998 Radiation pneumonitis as a function of mean lung dose: an analysis of pooled data of 540 patients Int. J. Radiat. Oncol. Biol. Phys. 42 1-9
19. Lambin P et al 2013 'Rapid learning health care in oncology' - an approach towards decision support systems enabling customised radiotherapy Radiother. Oncol. 109 159-64
20. Lee S, Ybarra N, Jeyaseelan K, Faria S, Kopek N, Brisebois P, Bradley J D, Robinson C, Seuntjens J and El Naqa I 2015 Bayesian network ensemble as a multivariate strategy to predict radiation pneumonitis risk Med. Phys. 42 2421-30
21. Lind P A, Wennberg B, Gagliardi G, Rosfors S, Blom-Goldman U, Lidestahl A and Svane G 2006 ROC curves and evaluation of radiation-induced pulmonary toxicity in breast cancer Int. J. Radiat. Oncol. Biol. Phys. 64 765-70
22. Martel M K, Ten Haken R K, Hazuka M B, Turrisi A T, Fraass B A and Lichter A S 1994 Dose-volume histogram and 3D treatment planning evaluation of patients with pneumonitis Int. J. Radiat. Oncol. Biol. Phys. 28 575-81
23. Moiseenko V, Craig T, Bezjak A and Van Dyk J 2003 Dose-volume analysis of lung complications in the radiation treatment of malignant thymoma: a retrospective review Radiother. Oncol. 67 265-74
24. Pudil J N P and Kittler J 1994 Floating search methods in feature selection Pattern Recognit. Lett. 15 6
25. Rancati T, Ceresoli G L, Gagliardi G, Schipani S and Cattaneo G M 2003 Factors predicting radiation pneumonitis in lung cancer patients: a retrospective study Radiother. Oncol. 67 275-83
26. Rijsbergen C J V 1979 Information Retrieval 2nd edn (London: Butterworth-Heinemann)
27. Robnett T J, Machtay M, Vines E F, McKenna M G, Algazy K M and McKenna W G 2000 Factors predicting severe radiation pneumonitis in patients receiving definitive chemoradiation for lung cancer Int. J. Radiat. Oncol. Biol. Phys. 48 89-94
28. Saeys Y, Inza I and Larranaga P 2007 A review of feature selection techniques in bioinformatics Bioinformatics 23 2507-17
29. Seiffert C, Khoshgoftaar T M, Van Hulse J and Napolitano A 2010 RUSBoost: a hybrid approach to alleviating class imbalance IEEE Trans. Syst. Man Cybern. A 40 185-97
30. Simone C B II and Dorsey J F 2015 Additional data in the debate on stage I non-small cell lung cancer: surgery versus stereotactic ablative radiotherapy Ann. Transl. Med. 3 172
31. Simone C B II, Wildt B, Haas A R, Pope G, Rengan R and Hahn S M 2013 Stereotactic body radiation therapy for lung cancer Chest 143 1784-90
32. Theuws J C, Kwa S L, Wagenaar A C, Boersma L J, Damen E M, Muller S H, Baas P and Lebesque J V 1998a Dose-effect relations for early local pulmonary injury after irradiation for malignant lymphoma and breast cancer Radiother. Oncol. 48 33-43
33. Theuws J C, Kwa S L, Wagenaar A C, Seppenwoolde Y, Boersma L J, Damen E M, Muller S H, Baas P and Lebesque J V 1998b Prediction of overall pulmonary function loss in relation to the 3D dose distribution for patients with breast cancer and malignant lymphoma Radiother. Oncol. 49 233-43
34. Torre L A, Bray F, Siegel R L, Ferlay J, Lortet-Tieulent J and Jemal A 2015 Global cancer statistics, 2012 CA Cancer J. Clin. 65 87-108
35. Tsujino K, Hirota S, Kotani Y, Kado T, Yoden E, Fujii O, Soejima T, Adachi S and Takada Y 2006 Radiation pneumonitis following concurrent accelerated hyperfractionated radiotherapy and chemotherapy for limited-stage small-cell lung cancer: dose-volume histogram analysis and comparison with conventional chemoradiation Int. J. Radiat. Oncol. Biol. Phys. 64 1100-5