Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab

Nardone,Valerio; Tini,Paolo; Pastina,Pierpaolo; Botta,Cirino; Reginelli,Alfonso; Carbone,Salvatore  Francesco; Giannicola,Rocco; Calabrese,Grazia; Tebala,Carmela; Guida,Cesare; Giudice,Aldo; Barbieri,Vito; Tassone,Pierfrancesco; Tagliaferri,Pierosandro; Cappabianca,Salvatore; Capasso,Rosanna; Luce,Amalia; Caraglia,Michele; Mazzei,Maria  Antonietta; Pirtoli,Luigi; Correale,Pierpaolo

doi:10.3892/ol.2019.11220

Radiomics predicts survival of patients with advanced non‑small cell lung cancer undergoing PD‑1 blockade using Nivolumab

Authors:
- Valerio Nardone
- Paolo Tini
- Pierpaolo Pastina
- Cirino Botta
- Alfonso Reginelli
- Salvatore Francesco Carbone
- Rocco Giannicola
- Grazia Calabrese
- Carmela Tebala
- Cesare Guida
- Aldo Giudice
- Vito Barbieri
- Pierfrancesco Tassone
- Pierosandro Tagliaferri
- Salvatore Cappabianca
- Rosanna Capasso
- Amalia Luce
- Michele Caraglia
- Maria Antonietta Mazzei
- Luigi Pirtoli
- Pierpaolo Correale
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Affiliations: Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I‑80147 Naples, Italy, Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I‑53100 Siena, Italy, Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I‑88100 Catanzaro, Italy, Department of Precision Medicine, University of Campania ‘L. Vanvitelli’, I‑80138 Naples, Italy, Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I‑53100 Siena, Italy, Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital ‘Bianchi Melacrino Morelli’ Reggio Calabria I‑89124, Italy, Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital ‘Bianchi Melacrino Morelli’ Reggio Calabria I‑89124, Italy, Epidemiology Unit, IRCCS Istituto Nazionale Tumori ‘Fondazione G. Pascale’, I‑80131 Naples, Italy
Published online on: December 16, 2019 https://doi.org/10.3892/ol.2019.11220
Pages: 1559-1566

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Abstract

Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD‑1 that is used in the treatment of a number of different malignancies, including non‑small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD‑1/PDL1 blockade antitumor effects occur through the reactivation of a pre‑existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre‑ and post‑contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre‑ contrast and 13 (93%) post‑contrast exams. A specific cut‑off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co‑occurrence matrix (GLCM)‑entropy (score 1 >1.80), GLCM‑Dissimilarity (score 1 >5) and GLCM‑Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0‑1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/‑ SD: 18 +/‑ 1.98 months; 95% CI 14‑21 months) and 5 months (mean +/‑ SD: 6 +/‑ 0.99 months; 95% CI: 4‑8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD‑1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.

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1	Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D and Bray F: Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 136:E359–E386. 2015. View Article : Google Scholar : PubMed/NCBI
2	Cetin K, Ettinger DS, Hei YJ and O'Malley CD: Survival by histologic subtype in stage IV nonsmall cell lung cancer based on data from the surveillance, epidemiology and end results program. Clin Epidemiol. 3:139–148. 2011. View Article : Google Scholar : PubMed/NCBI
3	Pilkington G, Boland A, Brown T, Oyee J, Bagust A and Dickson R: A systematic review of the clinical effectiveness of first-line chemotherapy for adult patients with locally advanced or metastatic non-small cell lung cancer. Thorax. 70:359–367. 2015. View Article : Google Scholar : PubMed/NCBI
4	Scagliotti GV, Parikh P, von Pawel J, Biesma B, Vansteenkiste J, Manegold C, Serwatowski P, Gatzemeier U, Digumarti R, Zukin M, et al: Phase III study comparing cisplatin plus gemcitabine with cisplatin plus pemetrexed in chemotherapy-naive patients with advanced-stage non-small-cell lung cancer. J Clin Oncol. 26:3543–3551. 2008. View Article : Google Scholar : PubMed/NCBI
5	Noh JM, Kim JM, Ahn YC, Pyo H, Kim B, Oh D, Ju SG, Kim JS, Shin JS, Hong CS, et al: Effect of radiation therapy techniques on outcome in N3-positive IIIB non-small cell lung cancer treated with concurrent chemoradiotherapy. Cancer Res Treat. 48:106–114. 2016. View Article : Google Scholar : PubMed/NCBI
6	Luke JJ and Ott PA: PD-1 pathway inhibitors: The next generation of immunotherapy for advanced melanoma. Oncotarget. 6:3479–3492. 2015. View Article : Google Scholar : PubMed/NCBI
7	Pardoll DM: The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 12:252–264. 2012. View Article : Google Scholar : PubMed/NCBI
8	Creelan BC: Update on immune checkpoint inhibitors in lung cancer. Cancer Control. 21:80–89. 2014. View Article : Google Scholar : PubMed/NCBI
9	Horn L, Spigel DR, Vokes EE, Holgado E, Ready N, Steins M, Poddubskaya E, Borghaei H, Felip E, Paz-Ares L, et al: Nivolumab versus docetaxel in previously treated patients with advanced non-small-cell lung cancer: Two-year outcomes from two randomized, open-label, phase III trials (CheckMate 017 and CheckMate 057). J Clin Oncol. 35:3924–3933. 2017. View Article : Google Scholar : PubMed/NCBI
10	Pastina P, Nardone V, Botta C, Croci S, Tini P, Battaglia G, Ricci V, Cusi MG, Gandolfo C, Misso G, et al: Radiotherapy prolongs the survival of advanced non-small-cell lung cancer patients undergone to an immune-modulating treatment with dose-fractioned cisplatin and metronomic etoposide and bevacizumab (mPEBev). Oncotarget. 8:75904–75913. 2017. View Article : Google Scholar : PubMed/NCBI
11	Pastina P, Nardone V, Croci S, Battaglia G, Vanni F, Bellan C, Barbarino M, Ricci V, Costantini S, Capone F, et al: Anti-cancer activity of dose-fractioned mPE +/- bevacizumab regimen is paralleled by immune-modulation in advanced squamous NSLC patients. J Thorac Dis. 9:3123–3131. 2017. View Article : Google Scholar : PubMed/NCBI
12	Nardone V, Pastina P, Giannicola R, Agostino R, Croci S, Tini P, Pirtoli L, Giordano A, Tagliaferri P and Correale P: How to increase the efficacy of immunotherapy in NSCLC and HNSCC: Role of radiation therapy, chemotherapy, and other strategies. Front Immunol. 9:29412018. View Article : Google Scholar : PubMed/NCBI
13	Ganeshan B, Panayiotou E, Burnand K, Dizdarevic S and Miles K: Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: A potential marker of survival. Eur Radiol. 22:796–802. 2012. View Article : Google Scholar : PubMed/NCBI
14	Ganeshan B, Abaleke S, Young RC, Chatwin CR and Miles KA: 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. 2010. View Article : Google Scholar : PubMed/NCBI
15	Ganeshan B, Miles KA, Young RC and Chatwin CR: Hepatic enhancement in colorectal cancer: Texture analysis correlates with hepatic hemodynamics and patient survival. Acad Radiol. 14:1520–1530. 2007. View Article : Google Scholar : PubMed/NCBI
16	Alobaidli S, McQuaid S, South C, Prakash V, Evans P and Nisbet A: The role of texture analysis in imaging as an outcome predictor and potential tool in radiotherapy treatment planning. Br J Radiol. 87:201403692014. View Article : Google Scholar : PubMed/NCBI
17	Mattonen SA, Tetar S, Palma DA, Louie AV, Senan S and Ward AD: Imaging texture analysis for automated prediction of lung cancer recurrence after stereotactic radiotherapy. J Med Imaging (Bellingham). 2:0410102015. View Article : Google Scholar : PubMed/NCBI
18	Mattonen SA, Palma DA, Haasbeek CJ, Senan S and Ward AD: Early prediction of tumor recurrence based on CT texture changes after stereotactic ablative radiotherapy (SABR) for lung cancer. Med Phys. 41:0335022014. View Article : Google Scholar : PubMed/NCBI
19	Coroller TP, Agrawal V, Narayan V, Hou Y, Grossmann P, Lee SW, Mak RH and Aerts HJ: Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiother Oncol. 119:480–486. 2016. View Article : Google Scholar : PubMed/NCBI
20	Nardone V, Tini P, Carbone SF, Grassi A, Biondi M, Sebaste L, Carfagno T, Vanzi E, De Otto G, Battaglia G, et al: Bone texture analysis using CT-simulation scans to individuate risk parameters for radiation-induced insufficiency fractures. Osteoporos Int. 28:1915–1923. 2017. View Article : Google Scholar : PubMed/NCBI
21	Nardone V, Tini P, Nioche C, Biondi M, Sebaste L, Mazzei MA, Banci Buonamici F and Pirtoli L: Texture analysis of parotid gland as a predictive factor of radiation induced xerostomia: A subset analysis. Radiother Oncol. 122:3212017. View Article : Google Scholar : PubMed/NCBI
22	Nardone V, Tini P, Croci S, Carbone SF, Sebaste L, Carfagno T, Battaglia G, Pastina P, Rubino G, Mazzei MA and Pirtoli L: 3D bone texture analysis as a potential predictor of radiation-induced insufficiency fractures. Quant Imaging Med Surg. 8:14–24. 2018. View Article : Google Scholar : PubMed/NCBI
23	Nardone V, Tini P, Nioche C, Mazzei MA, Carfagno T, Battaglia G, Pastina P, Grassi R, Sebaste L and Pirtoli L: Texture analysis as a predictor of radiation-induced xerostomia in head and neck patients undergoing IMRT. Radiol Med. 123:415–423. 2018. View Article : Google Scholar : PubMed/NCBI
24	Liu Y, Liu S, Qu F, Li Q, Cheng R and Ye Z: Tumor heterogeneity assessed by texture analysis on contrast-enhanced CT in lung adenocarcinoma: Association with pathologic grade. Oncotarget. 8:53664–53674. 2017.PubMed/NCBI
25	Bae JM, Jeong JY, Lee HY, Sohn I, Kim HS, Son JY, Kwon OJ, Choi JY, Lee KS and Shim YM: Pathologic stratification of operable lung adenocarcinoma using radiomics features extracted from dual energy CT images. Oncotarget. 8:523–535. 2017. View Article : Google Scholar : PubMed/NCBI
26	Lee G, Lee HY, Park H, Schiebler ML, van Beek EJR, Ohno Y, Seo JB and Leung A: Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: State of the art. Eur J Radiol. 86:297–307. 2017. View Article : Google Scholar : PubMed/NCBI
27	Craigie M, Squires J and Miles K: Can CT measures of tumour heterogeneity stratify risk for nodal metastasis in patients with non-small cell lung cancer? Clin Radiol. 72:899.e1–899.e7. 2017. View Article : Google Scholar
28	Fave X, Zhang L, Yang J, Mackin D, Balter P, Gomez D, Followill D, Jones AK, Stingo F, Liao Z, et al: Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci Rep. 7:5882017. View Article : Google Scholar : PubMed/NCBI
29	Miles KA: How to use CT texture analysis for prognostication of non-small cell lung cancer. Cancer Imaging. 16:102016. View Article : Google Scholar : PubMed/NCBI
30	Sacconi B, Anzidei M, Leonardi A, Boni F, Saba L, Scipione R, Anile M, Rengo M, Longo F, Bezzi M, et al: Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: A correlation with EGFR mutations and survival rates. Clin Radiol. 72:443–450. 2017. View Article : Google Scholar : PubMed/NCBI
31	Zhou M, Leung A, Echegaray S, Gentles A, Shrager JB, Jensen KC, Berry GJ, Plevritis SK, Rubin DL, Napel S and Gevaert O: Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology. 286:307–315. 2018. View Article : Google Scholar : PubMed/NCBI
32	Liu Y, Kim J, Balagurunathan Y, Li Q, Garcia AL, Stringfield O, Ye Z and Gillies RJ: Radiomic features are associated with EGFR mutation status in lung adenocarcinomas. Clin Lung Cancer. 17:441–448.e6. 2016. View Article : Google Scholar : PubMed/NCBI
33	Halpenny DF, Plodkowski A, Riely G, Zheng J, Litvak A, Moscowitz C and Ginsberg MS: Radiogenomic evaluation of lung cancer-are there imaging characteristics associated with lung adenocarcinomas harboring BRAF mutations? Clin Imaging. 42:147–151. 2017. View Article : Google Scholar : PubMed/NCBI
34	Nioche C, Orlhac F, Boughdad S, Reuze S, Soussan M, Robert C, Barakat C and Buvat I: A freeware for tumor heterogeneity characterization in PET, SPECT, CT, MRI and US to accelerate advances in radiomics. J Nucl Med. 58 (Suppl 1):S13162017.
35	Camp RL, Dolled-Filhart M and Rimm DL: X-tile: A new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 10:7252–7259. 2004. View Article : Google Scholar : PubMed/NCBI
36	van der Schaaf A, Xu CJ, van Luijk P, Van't Veld AA, Langendijk JA and Schilstra C: Multivariate modeling of complications with data driven variable selection: Guarding against overfitting and effects of data set size. Radiother Oncol. 105:115–121. 2012. View Article : Google Scholar : PubMed/NCBI
37	Giannicola R, D'Arrigo G, Botta C, Agostino R, Del Medico P, Falzea AC, Barbieri V, Staropoli N, Del Giudice T, Pastina P, et al: Early blood rise in auto-antibodies to nuclear and smooth muscle antigens is predictive of prolonged survival and autoimmunity in metastatic-non-small cell lung cancer patients treated with PD-1 immune-check point blockade by nivolumab. Mol Clin Oncol. 11:81–90. 2019.PubMed/NCBI
38	Mazzei MA, Nardone V, Di Giacomo L, Bagnacci G, Gentili F, Tini P, Marrelli D and Volterrani L: The role of delta radiomics in gastric cancer. Quant Imaging Med Surg. 8:719–721. 2018. View Article : Google Scholar : PubMed/NCBI
39	Nardone V, Reginelli A, Scala F, Carbone SF, Mazzei MA, Sebaste L, Carfagno T, Battaglia G, Pastina P, Correale P, et al: Magnetic-resonance-imaging texture analysis predicts early progression in rectal cancer patients undergoing neoadjuvant chemoradiation. Gastroenterol Res Pract. 2019:85057982019. View Article : Google Scholar : PubMed/NCBI
40	Lee HY, Jeong JY, Lee KS, Yi CA, Kim BT, Kang H, Kwon OJ, Shim YM and Han J: Histopathology of lung adenocarcinoma based on new IASLC/ATS/ERS classification: Prognostic stratification with functional and metabolic imaging biomarkers. J Magn Reson Imaging. 38:905–913. 2013. View Article : Google Scholar : PubMed/NCBI
41	Kim YN, Lee HY, Lee KS, Seo JB, Chung MJ, Ahn MJ, Park K, Kim TS and Yi CA: Dual-energy CT in patients treated with anti-angiogenic agents for non-small cell lung cancer: New method of monitoring tumor response? Korean J Radiol. 13:702–710. 2012. View Article : Google Scholar : PubMed/NCBI
42	van Elmpt W, Zegers CML, Reymen B, Even AJG, Dingemans AC, Oellers M, Wildberger JE, Mottaghy FM, Das M, Troost EGC and Lambin P: Multiparametric imaging of patient and tumour heterogeneity in non-small-cell lung cancer: Quantification of tumour hypoxia, metabolism and perfusion. Eur J Nucl Med Mol Imaging. 43:240–248. 2016. View Article : Google Scholar : PubMed/NCBI
43	Chi JT, Thrall DE, Jiang C, Snyder S, Fels D, Landon C, McCall L, Lan L, Hauck M, MacFall JR, et al: Comparison of genomics and functional imaging from canine sarcomas treated with thermoradiotherapy predicts therapeutic response and identifies combination therapeutics. Clin Cancer Res. 17:2549–2560. 2011. View Article : Google Scholar : PubMed/NCBI
44	Tini P, Nardone V, Pastina P, Pirtoli L, Correale P and Giordano A: The effects of radiotherapy on the survival of patients with unresectable non-small cell lung cancer. Expert Rev Anticancer Ther. 18:593–602. 2018. View Article : Google Scholar : PubMed/NCBI
45	Virginia BM, Laura F, Silvia R, Roberto F, Francesco F, Eva H, Charles F, Samy A, Stefan M, Jean-Charles S, et al: Prognostic value of histogram analysis in advanced non-small cell lung cancer: A radiomic study. Oncotarget. 9:1906–1914. 2018. View Article : Google Scholar : PubMed/NCBI
46	Bera K, Velcheti V and Madabhushi A: Novel quantitative imaging for predicting response to therapy: Techniques and clinical applications. Am Soc Clin Oncol Educ Book. 38:1008–1018. 2018. View Article : Google Scholar : PubMed/NCBI
47	Trebeschi S, Drago SG, Birkbak NJ, Kurilova I, Cǎlin AM, Pizzi AD, Lalezari F, Lambregts DMJ, Rohaan M, Parmar C, et al: Predicting response to cancer immunotherapy using non-invasive radiomic biomarkers. Ann Oncol. Mar 21–2019.(Epub ahead of print). View Article : Google Scholar : PubMed/NCBI
48	Harmon S, Seder CW, Chen S, Traynor A, Jeraj R and Blasberg JD: Quantitative FDG PET/CT may help risk-stratify early-stage non-small cell lung cancer patients at risk for recurrence following anatomic resection. J Thorac Dis. 11:1106–1116. 2019. View Article : Google Scholar : PubMed/NCBI
49	Shi L, He Y, Yuan Z, Benedict S, Valicenti R, Qiu J and Rong Y: Radiomics for response and outcome assessment for non-small cell lung cancer. Technol Cancer Res Treat. 17:15330338187827882018. View Article : Google Scholar : PubMed/NCBI
50	Einenkel J, Braumann UD, Horn LC, Pannicke N, Kuska JP, Schütz A, Hentschel B and Höckel M: Evaluation of the invasion front pattern of squamous cell cervical carcinoma by measuring classical and discrete compactness. Comput Med Imaging Graph. 31:428–435. 2007. View Article : Google Scholar : PubMed/NCBI
51	Nardone V, Nanni S, Pastina P, Vinciguerra C, Cerase A, Correale P, Guida C, Giordano A, Tini P, Reginelli A, et al: Role of perilesional edema and tumor volume in the prognosis of non-small cell lung cancer (NSCLC) undergoing radiosurgery (SRS) for brain metastases. Strahlenther Onkol. 195:734–744. 2019. View Article : Google Scholar : PubMed/NCBI
52	Grove O, Berglund AE, Schabath MB, Aerts HJ, Dekker A, Wang H, Velazquez ER, Lambin P, Gu Y, Balagurunathan Y, et al: Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS One. 10:e01182612015. View Article : Google Scholar : PubMed/NCBI
53	Liao Y, Fan X and Wang X: Effects of different metastasis patterns, surgery and other factors on the prognosis of patients with stage IV non-small cell lung cancer: A surveillance, epidemiology, and end results (SEER) linked database analysis. Oncol Lett. 18:581–592. 2019.PubMed/NCBI