Yıl: 2021 Cilt: 36 Sayı: 4 Sayfa Aralığı: 446 - 458 Metin Dili: İngilizce DOI: 10.5505/tjo.2021.2788 İndeks Tarihi: 15-06-2022

Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study

Öz:
OBJECTIVE This study aimed to predict the overall survival (OS), survival time, and time to progression in cases diagnosed with Stage III lung cancer. METHODS The sample consisted of 585 patients that underwent radiotherapy and chemotherapy with the diagnosis of Stage III lung cancer. OS prediction was undertaken in 324 cases, survival time prediction in 241 that died due to lung cancer, and prediction of time to progression in 223 that showed progression during follow-up. Twenty-seven variables were evaluated, and logistic regression, multilayer perceptron classifier (MLP), extreme gradient boosting, support vector clustering, random forest classifier (RFC), Gaussian Naive Bayes, and light gradient boosting machine algorithms were used to construct prediction models. RESULTS In OS prediction, over a median 21-month follow-up, 255 of 324 cases died and the median OS was 20 (2-101) months. The best predictive algorithms belonged to logistic regression for OS (accuracy rate: 70%, confidence interval [CI]: 0.60-0.82, area under curve [AUC]: 0.76), MLP classifier for 12- and 20-month survival times (67%, CI: 0.54-0.81, AUC: 0.64 and 71%, CI: 0.59-0.84, AUC: 0.61, respectively), and RFC for time to progression (76%, CI: 0.66-0.86, AUC: 0.78). CONCLUSION Considering high treatment costs, potential serious toxicity, the harm of early progression, and low survival in cases of ineffective treatment, machine learning-based predictive systems are promising. Personalizing prognosis and treatment using these algorithms can improve oncological results.
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  • 1. Siegel R, Naishadham D, Jemal A. Cancer statistics. CA Cancer J Clin 2013;63(1):11–30.
  • 2. Aupérin A, Le Péchoux C, Rolland E, Curran WJ, Furuse K, Fournel P, et al. Meta-analysis of concomitant versus sequential radiochemotherapy in locally advanced non-small-cell lung cancer. J Clin Oncol 2010;28(13):2181–90.
  • 3. Chen J, Jiang R, Garces YI, Jatoi A, Stoddard SM, Sun Z, et al. Prognostic factors for limited-stage small cell lung cancer: A study of 284 patients. Lung Cancer 2010;67(2):221–6.
  • 4. Meyer P, Noblet V, Mazzara C, Lallement A. Survey on deep learning for radiotherapy. Comput Biol Med 2018;98:126–46.
  • 5. Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. Br J Radiol 2019;92(1100):20190001.
  • 6. Lynch CM, Abdollahi B, Fuqua JD, de Carlo AR, Bartholomai JA, Balgemann RN, et al. Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inform 2017;108:1-8.
  • 7. Brierley J, Gospodarowicz MK, Wittekind C. TNM Classification of Malignant Tumours. 8th ed. Hoboken, NJ: John Wiley and Sons, Inc.; 2017.
  • 8. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer 2017;111:176–81.
  • 9. Hong S, Zhou T, Fang W, Xue C, Hu Z, Qin T, et al. The prognostic nutritional index (PNI) predicts overall survival of small-cell lung cancer patients. Tumor Biol 2015;36(5):3389–97.
  • 10.Zhu H, Zhou Z, Xue Q, Zhang X, He J, Wang L. Treatment modality selection and prognosis of early stage small cell lung cancer: Retrospective analysis from a single cancer institute. Eur J Cancer Care (Engl) 2013;22(6):789–96.
  • 11.Kasmann L, Bolm L, Janssen S, Rades D. Prognostic factors and treatment of early-stage small-cell lung cancer. Anticancer Res 2017;37(3):1535–8.
  • 12.Wang L, Dong T, Xin B, Xu C, Guo M, Zhang H, et al. Integrative nomogram of CT imaging, clinical, and hematological features for survival prediction of patients with locally advanced non-small cell lung cancer. Eur Radiol 2019;29(6):2958–67.
  • 13.Pöttgen C, Stuschke M, Graupner B, Theegarten D, Gauler T, Jendrossek V, et al. Prognostic model for longterm survival of locally advanced non-small-cell lung cancer patients after neoadjuvant radiochemotherapy and resection integrating clinical and histopathologic factors. BMC Cancer 2015;15:363.
  • 14.de Haan L, Ferreira A. Extreme Value Theory: An Introduction. Berlin: Springer Science Business Media; 2007.
  • 15.Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP. SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res 2002;16:321–57.
  • 16.Ron K. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann; 1995. p. 1137–43.
  • 17.Gupta S, Tran T, Luo W, Phung D, Kennedy RL, Broad A, et al. Machine learning prediction of cancer survival: A retrospective study using electronic administrative records and a cancer registry. BMJ Open 2014;4(3):e004007.
  • 18.Chen YC, Ke WC, Chiu HW. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories. Comput Biol Med 2014;48:1-7.
  • 19.Parikh RB, Manz C, Chives C, Regli SH, Braun J, Draugelis ME, et al. Machine learning approaches to predict 6-month mortality among patients with cancer. JAMA Netw Open 2019;2(10):e1915997.
  • 20.Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inform Decis Mak 2019;19:48.
  • 21.Li Y, Ge D, Gu J, Xu F, Zhu Q, Lu C, et al. A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies. BMC Cancer 2019;19:886.
  • 22.Asamura H, Chansky K, Crowley J, Goldstraw P, Rusch VW, Vansteenkiste JF, et al. The international associa-tion for the study of lung cancer lung cancer staging project: Proposals for the revision of the N descriptors in the forthcoming 8th edition of the TNM classification for lung cancer. J Thorac Oncol 2015;10(12):1675- 84.
  • 23.Firat S, Bousamra M, Gore E, Byhardt RW. Comorbidity and KPS are independent prognostic factors in stage I non-small-cell lung cancer. Int J Rad Oncol Biol Phys 2002;52(4):1047–57.
  • 24.Hirsch FR, Spreafico A, Novella S, Wood MD, Simms L, Papotti M. The prognostic and predictive role of histology in advanced non-small cell lung cancer a literature review. J Thorac Oncol 2008;3(12):1468–81.
  • 25.Bradley JD, Ieumwananonthachai N, Purdy JA, Wasserman TH, Lockett MA, Graham MV, et al. Gross tumor volume, critical prognostic factor in patients treated with three-dimensional conformal radiation therapy for non–small-cell lung carcinoma. Int J Rad Oncol Biol Phys 2002;52(1):49–57.
  • 26.Etiz D, Marks LB, Zhou SM, Bentel GC, Clough R, Hernando ML, et al. Influence of tumor volume on survival in patients irradiated for non–small-cell lung cancer. Int J Rad Oncol Biol Phys 2002;53(4):835–46.
  • 27.Gupta P, Chiang SF, Sahoo PK, Mohapatra SK, You JF, Onthoni DD, et al. Prediction of colon cancer stages and survival period with machine learning approach. Cancers 2019;11(12):2007.
  • 28.Diakos CI, Charles KA, McMillan DC, Clarke SJ. Cancer-related inflammation and treatment effectiveness. Lancet Oncol 2014;15(11):e493–503.
  • 29.Yuan A, Hsiao YJ, Chen HY, Chen HW, Ho CC, Chen YY, et al. Opposite effects of M1 and M2 macrophage subtypes on lung cancer progression. Sci Rep 2015;5:14273.
  • 30.Tao H, Mimura Y, Aoe K, Kobayashi S, Yamamoto H, Matsuda E, et al. Prognostic potential of FOXP3 expression in non-small cell lung cancer cells combined with tumor-infiltrating regulatory T cells. Lung Cancer 2012;75(1):95–101.
  • 31.Pei D, Zhu F, Chen X, Qian J, He S, Qian Y, et al. Preadjuvant chemotherapy leukocyte count may predict the outcome for advanced gastric cancer after radical resection. Biomed Pharmacother 2014;68(2):213–7.
  • 32.Tsai YD, Wang CP, Chen CY, Lin LW, Hwang TZ, Lu LF, et al. Pretreatment circulating monocyte count associated with poor prognosis in patients with oral cavity cancer. Head Neck 2014;36(7):947–53.
  • 33.Forget P, Machiels JP, Coulie PG, Berliere M, Poncelet AJ, Tombal B, et al. Neutrophil: Lymphocyte ratio and intraoperative use of ketorolac or diclofenac are prognostic factors in different cohorts of patients undergoing breast, lung, and kidney cancer surgery. Ann Surg Oncol 2013;20(Suppl 3):S650–60.
  • 34.Yang HB, Xing M, Ma LN, Feng LX, Yu Z. Prognostic significance of neutrophil-lymphocyteratio/plateletlymphocyteratioin lung cancers: A meta-analysis. Oncotarget 2016;7(47):76769–78.
  • 35.Botta C, Barbieri V, Ciliberto D, Rossi A, Rocco D, Addeo R, et al. Systemic inflammatory status at baseline predicts bevacizumab benefit in advanced nonsmall cell lung cancer patients. Cancer Biol Ther 2013;14(6):469–75.
  • 36.Cho H, Hur HW, Kim SW, Kim SH, Kim JH, Kim YT, et al. Pre-treatment neutrophil to lymphocyte ratio is elevated in epithelial ovarian cancer and predicts survival after treatment. Cancer Immunol Immunother 2009;58(1):15–23.
APA Yakar M, Etiz D, Yılmaz Ş, Çelik Ö, AK G, Metintas M (2021). Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. , 446 - 458. 10.5505/tjo.2021.2788
Chicago Yakar Melek,Etiz Durmuş,Yılmaz Şenay,Çelik Özer,AK Güntülü,Metintas Muzaffer Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. (2021): 446 - 458. 10.5505/tjo.2021.2788
MLA Yakar Melek,Etiz Durmuş,Yılmaz Şenay,Çelik Özer,AK Güntülü,Metintas Muzaffer Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. , 2021, ss.446 - 458. 10.5505/tjo.2021.2788
AMA Yakar M,Etiz D,Yılmaz Ş,Çelik Ö,AK G,Metintas M Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. . 2021; 446 - 458. 10.5505/tjo.2021.2788
Vancouver Yakar M,Etiz D,Yılmaz Ş,Çelik Ö,AK G,Metintas M Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. . 2021; 446 - 458. 10.5505/tjo.2021.2788
IEEE Yakar M,Etiz D,Yılmaz Ş,Çelik Ö,AK G,Metintas M "Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study." , ss.446 - 458, 2021. 10.5505/tjo.2021.2788
ISNAD Yakar, Melek vd. "Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study". (2021), 446-458. https://doi.org/10.5505/tjo.2021.2788
APA Yakar M, Etiz D, Yılmaz Ş, Çelik Ö, AK G, Metintas M (2021). Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. Türk Onkoloji Dergisi, 36(4), 446 - 458. 10.5505/tjo.2021.2788
Chicago Yakar Melek,Etiz Durmuş,Yılmaz Şenay,Çelik Özer,AK Güntülü,Metintas Muzaffer Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. Türk Onkoloji Dergisi 36, no.4 (2021): 446 - 458. 10.5505/tjo.2021.2788
MLA Yakar Melek,Etiz Durmuş,Yılmaz Şenay,Çelik Özer,AK Güntülü,Metintas Muzaffer Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. Türk Onkoloji Dergisi, vol.36, no.4, 2021, ss.446 - 458. 10.5505/tjo.2021.2788
AMA Yakar M,Etiz D,Yılmaz Ş,Çelik Ö,AK G,Metintas M Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. Türk Onkoloji Dergisi. 2021; 36(4): 446 - 458. 10.5505/tjo.2021.2788
Vancouver Yakar M,Etiz D,Yılmaz Ş,Çelik Ö,AK G,Metintas M Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study. Türk Onkoloji Dergisi. 2021; 36(4): 446 - 458. 10.5505/tjo.2021.2788
IEEE Yakar M,Etiz D,Yılmaz Ş,Çelik Ö,AK G,Metintas M "Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study." Türk Onkoloji Dergisi, 36, ss.446 - 458, 2021. 10.5505/tjo.2021.2788
ISNAD Yakar, Melek vd. "Prediction of Survival and Progression-free Survival Using Machine Learning in Stage III Lung Cancer: A Pilot Study". Türk Onkoloji Dergisi 36/4 (2021), 446-458. https://doi.org/10.5505/tjo.2021.2788