Top

International Journal of Computer Assisted Radiology and Surgery

Published in:

01-01-2020 | Computed Tomography | Original Article

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms

Authors: José Raniery Ferreira-Junior, Marcel Koenigkam-Santos, Ariane Priscilla Magalhães Tenório, Matheus Calil Faleiros, Federico Enrique Garcia Cipriano, Alexandre Todorovic Fabro, Janne Näppi, Hiroyuki Yoshida, Paulo Mazzoncini de Azevedo-Marques

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 1/2020

Abstract

Purpose

As some of the most important factors for treatment decision of lung cancer (which is the deadliest neoplasm) are staging and histology, this work aimed to associate quantitative contrast-enhanced computed tomography (CT) features from malignant lung tumors with distant and nodal metastases (according to clinical TNM staging) and histopathology (according to biopsy and surgical resection) using radiomics assessment.

Methods

A local cohort of 85 patients were retrospectively (2010–2017) analyzed after approval by the institutional research review board. CT images acquired with the same protocol were semiautomatically segmented by a volumetric segmentation method. Tumors were characterized by quantitative CT features of shape, first-order, second-order, and higher-order textures. Statistical and machine learning analyses assessed the features individually and combined with clinical data.

Results

Univariate and multivariate analyses identified 40, 2003, and 45 quantitative features associated with distant metastasis, nodal metastasis, and histopathology (adenocarcinoma and squamous cell carcinoma), respectively. A machine learning model yielded the highest areas under the receiver operating characteristic curves of 0.92, 0.84, and 0.88 to predict the same previous patterns.

Conclusion

Several radiomic features (including wavelet energies, information measures of correlation and maximum probability from co-occurrence matrix, busyness from neighborhood intensity-difference matrix, directionalities from Tamura’s texture, and fractal dimension estimation) significantly associated with distant metastasis, nodal metastasis, and histology were discovered in this work, presenting great potential as imaging biomarkers for pathological diagnosis and target therapy decision.

Available only for authorised users

Wong MC, Lao XQ, Ho KF, Goggins WB, Tse SLA (2017) Incidence and mortality of lung cancer: global trends and association with socioeconomic status. Sci Rep 7:14300PubMedPubMedCentral

Howlader N, Noone AM, Krapcho M, Miller D, Bishop K, Altekruse SF, Kosary CL, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA (2016) SEER cancer statistics review, 1975–2013. www.seer.cancer.gov/csr/1975_2013/. Accessed 22 July 2019

Cooper WA, O’Toole S, Boyer M, Horvath L, Mahar A (2011) What’s new in non-small cell lung cancer for pathologists: the importance of accurate subtyping, EGFR mutations and ALK rearrangements. Pathology 43:103–115PubMed

Koenigkam-Santos M, Muley T, Warth A, Paula W, Lederlin M, Schnabel P, Schlemmer HP, Kauczor HU, Heussel CP, Puderbach M (2014) Morphological computed tomography features of surgically resectable pulmonary squamous cell carcinomas: impact on prognosis and comparison with adenocarcinomas. Eur J Radiol 83:1275–1281PubMed

Tailor TD, Schmidt RA, Eaton KD, Wood D, Pipavath S (2015) The pseudocavitation sign of lung adenocarcinoma: a distinguishing feature and imaging biomarker of lepidic growth. J Thorac Imaging 30:308–313PubMed

Yip S, Liu Y, Parmar C, Li Q, Liu S, Qu F, Ye Z, Gillies R, Aerts H (2017) Associations between radiologist-defined semantic and automatically computed radiomic features in non-small cell lung cancer. Sci Rep 7:3519PubMedPubMedCentral

Giger M (2018) Machine learning in medical imaging. J Am Coll Radiol 15:512–520PubMed

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577PubMed

Larue RT, Defraene G, Ruysscher De, Lambin P, van Elmpt W (2017) Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol 90:20160665PubMedPubMedCentral

10.

Aerts H, Velazquez E, Leijenaar R, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen M, Leemans C, Dekker A, Quackenbush J, Gillies R, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006PubMed

11.

Halpenny DF, Plodkowski A, Riely G, Zheng J, Litvak A, Moscowitz C, Ginsberg M (2017) Radiogenomic evaluation of lung cancer—Are there imaging characteristics associated with lung adenocarcinomas harboring BRAF mutations? Clin Imaging 42:147–151PubMed

12.

Sacconi B, Anzidei M, Leonardi A, Boni F, Saba L, Scipione R, Anile M, Rengo M, Longo F, Bezzi M, Venuta F, Napoli A, Laghi A, Catalano C (2017) Analysis of CT features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with EGFR mutations and survival rates. Clin Radiol 72:443–450PubMed

13.

Permuth J, Choi J, Balarunathan Y, Kim J, Chen DT, Chen L, Orcutt S, Doepker M, Gage K, Zhang G, Latifi K, Hoffe S, Jiang K, Coppola D, Centeno B, Magliocco A, Li Q, Trevino J, Merchant N, Gillies R, Malafa M (2016) Combining radiomic features with a miRNA classifier may improve prediction of malignant pathology for pancreatic intraductal papillary mucinous neoplasms. Oncotarget 7:85785PubMedPubMedCentral

14.

Fedorov A, Beichel R, Cramer J, Finet J, Robin J, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller J, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341PubMedPubMedCentral

15.

Egger J, Kapur T, Fedorov A, Pieper S, Miller J, Veeraraghavan H, Freisleben B, Golby A, Nimsky C, Kikinis R (2013) GBM volumetry using the 3D Slicer medical image computing platform. Sci Rep 3:1364PubMedPubMedCentral

16.

Velazquez E, Parmar C, Jermoumi M, Mak R, van Baardwijk A, Fennessy F, Lewis J, Ruysscher D, Kikinis R, Lambin P, Aerts H (2013) Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep 3:3529PubMedPubMedCentral

17.

Parmar C, Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak R, Mitra S, Shankar B, Kikinis R, Haibe-Kains B, Lambin P, Aerts H (2014) Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS ONE 9:e102107PubMedPubMedCentral

18.

Pinter C, Lasso A, Wang A, Jaffray D, Fichtinger G (2012) SlicerRT: radiation therapy research toolkit for 3D Slicer. Med Phys 39:6332–6338PubMed

19.

Zhang L, Fried DV, Fave XJ, Hunter L, Yang J, Court L (2015) IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys 42:1341–1353PubMedPubMedCentral

20.

Lux M, Marques O (2013) Visual information retrieval using Java and LIRE. Morgan & Claypool Publishers, Williston

21.

Schneider C, Rasband W, Eliceiri K (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675PubMedPubMedCentral

22.

Frank E, Hall M, Witten I (2016) The WEKA workbench. Online appendix for data mining: practical machine learning tools and techniques. Morgan Kaufmann, Burlington

23.

Zameer A, Arshad J, Khan A, Raja M (2017) Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks. Energy Convers Manag 134:361–372

24.

Ferreira Junior J, Koenigkam-Santos M, Cipriano F, Fabro A, Azevedo-Marques P (2018) Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed 159:23–30PubMed

25.

Emaminejad N, Qian W, Guan Y, Tan M, Qiu Y, Liu H, Zheng B (2016) Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng 63:1034–1043PubMed

26.

Coroller T, Grossmann P, Hou Y, Velazquez E, Leijenaar R, Hermann G, Lambin P, Haibe-Kains B, Mak R, Aerts H (2015) CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 114:345–350PubMedPubMedCentral

27.

Shroff GS, Benveniste MF, Groot PM, Wu C, Viswanathan C, Papadimitrakopoulou V, Truong M (2017) Targeted therapy and imaging findings. J Thorac Imaging 32:313–322PubMed

28.

Mok TS, Wu YL, Thongprasert S, Yang C, Chu D, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang J, Chewaskulyong B, Jiang H, Duffield E, Watkins C, Armour A, Fukuoka M (2009) Gefitinib or carboplatin–paclitaxel in pulmonary adenocarcinoma. N Engl J Med 361:947–957PubMed

29.

Tamura T, Kurishima K, Nakazawa K, Kagohashi K, Ishikawa H, Satoh H, Hizawa N (2015) Specific organ metastases and survival in metastatic non-small cell lung cancer. Mol Clin Oncol 3:217–221PubMed

30.

Zhou H, Dong D, Chen B, Fang M, Cheng Y, Gan Y, Zhang R, Zhang L, Zang Y, Liu Z, Zheng H, Li W, Tian J (2018) Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol 11:31–36PubMed

31.

Litjens G, Kooi T, Bejnordi B, Setio A, Ciompi F, Ghafoorian M, van der Laak J, van Ginneken B, Sánchez C (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88PubMed

32.

Zhu X, Dong D, Chen Z, Fang M, Zhang L, Song J, Yu D, Zang Y, Liu Z, Shi J, Tian J (2018) Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. Eur Radiol 28:2772–2778PubMed

33.

Digumarthy SR, Padole AM, Gullo R, Sequist LV, Kalra MK (2019) Can CT radiomic analysis in NSCLC predict histology and EGFR mutation status? Medicine 98:e13963PubMedPubMedCentral

34.

Ferreira JR, Azevedo-Marques PM, Oliveira MC (2017) Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. Int J Comput Assist Radiol Surg 12:509–517PubMed

35.

Guo Y, Bennamoun M, Sohel F, Lu M, Wan J, Kwok N (2016) A comprehensive performance evaluation of 3D local feature descriptors. Int J Comput Vis 116:66–89

36.

Dhara AK, Mukhopadhyay S, Saha P, Garg M, Khandelwal N (2016) Differential geometry-based techniques for characterization of boundary roughness of pulmonary nodules in CT images. Int J Comput Assist Radiol Surg 11:337–349PubMed

37.

D’Antonoli TA, Farchione A, Lenkowicz J, Chiappetta M, Cicchetti G, Martino A, Ottavianelli A, Manfredi R, Margaritora S, Bonomo L, Valentini V, Larici AR (2019) CT radiomics signature of tumor and peritumoral lung parenchyma to predict nonsmall cell lung cancer postsurgical recurrence risk. Acad Radiol. https://doi.org/10.1016/j.acra.2019.05.019 CrossRef

38.

Levman JE, Martel AL (2011) A margin sharpness measurement for the diagnosis of breast cancer from magnetic resonance imaging examinations. Acad Radiol 18:1577–1581PubMed

39.

Ferreira JR Jr, Oliveira MC, Azevedo-Marques PM (2018) Characterization of pulmonary nodules based on features of margin sharpness and texture. J Digit Imaging 31:451–463PubMed

40.

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444PubMed

41.

Chartrand G, Cheng P, Vorontsov E, Drozdzal M, Turcotte S, Pal C, Kadoury S, Tang A (2017) Deep learning: a primer for radiologists. Radiographics 37:2113–2131PubMed

42.

Shen W, Zhou M, Yang F, Yu D, Dong D, Yang C, Zang Y, Tian J (2017) Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification. Pattern Recognit 61:663–673

43.

Paul R, Hawkins S, Balagurunathan Y, Schabath M, Gillies R, Hall L, Goldgof D (2016) Deep feature transfer learning in combination with traditional features predicts survival among patients with lung adenocarcinoma. Tomography 2:388–395PubMedPubMedCentral

Title: CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms
Authors: José Raniery Ferreira-Junior
Marcel Koenigkam-Santos
Ariane Priscilla Magalhães Tenório
Matheus Calil Faleiros
Federico Enrique Garcia Cipriano
Alexandre Todorovic Fabro
Janne Näppi
Hiroyuki Yoshida
Paulo Mazzoncini de Azevedo-Marques
Publication date: 01-01-2020
Publisher: Springer International Publishing
Keywords: Computed Tomography
Lung Cancer
Metastasis
Computed Tomography
Lung Cancer
Lung Cancer
Biomarkers
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 1/2020
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-019-02093-y

At a glance: The STEP trials

Springer Medicine

CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms

Abstract

Purpose

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Purpose

Methods

Results

Conclusion

Please log in to get access to this content

Other articles of this Issue 1/2020

Real-time surgical needle detection using region-based convolutional neural networks

Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches

Interactive exploration of a 3D intracranial aneurysm wall model extracted from histologic slices

Normative ranges of nasal airflow variables in healthy adults

Combining position-based dynamics and gradient vector flow for 4D mitral valve segmentation in TEE sequences

CIGuide: in situ augmented reality laser guidance