Radiomic phenotype features predict pathological response in non-small cell lung cancer

doi:10.1016/j.radonc.2016.04.004

Radiotherapy and Oncology

Volume 119, Issue 3, June 2016, Pages 480-486

https://doi.org/10.1016/j.radonc.2016.04.004 Get rights and content

Abstract

Background and purpose

Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).

Materials and Methods

127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison.

Results

Seven features were predictive for pathologic gross residual disease (AUC > 0.6, p-value < 0.05), and one for pathologic complete response (AUC = 0.63, p-value = 0.01). No conventional imaging features were predictive (range AUC = 0.51–0.59, p-value > 0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present a rounder shape (spherical disproportionality, AUC = 0.63, p-value = 0.009) and heterogeneous texture (LoG 5 mm 3D – GLCM entropy, AUC = 0.61, p-value = 0.03).

Conclusion

We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.

Section snippets

Patient selection

Patients with stage II–III NSCLC treated at Dana-Farber Cancer Institute between 2001 and 2013 who were treated with neoadjuvant radiotherapy and chemotherapy (chemoradiation) prior to surgical resection were included in this study. Patients with distant metastasis at presentation or delay in surgery greater than 120 days after the completion of chemoradiation were excluded. For all patients, CT imaging at the initiation of chemoradiation and prior to surgical resection was available. No

Results

127 patients with NSCLC were included in this study. The median age was 60.5 years (range 32.7–77.6 years), with a majority of women (53.5%) and white (92.1%). Tumor histology was predominantly adenocarcinoma (56.6%) and AJCC [27] stage IIIA (75.6%). The median follow-up was 41.8 months (range 2.7–117.2). The distribution of pathological response was 27 (21.3%), 33 (26.0%) and 67 (52.7%) respectively for complete response, microscopic and gross residual disease.

All treatment information can be

Discussion

Radiomics [1] is an emerging field of quantitative imaging that aims to extract phenotypic tumor information from clinical imaging data. In this study we demonstrate the predictive power of radiomic phenotypic features for pathological response in patients with NSCLC. Pathological response is a standard endpoint, assessed at time of surgery for a direct measure of neoadjuvant chemotherapy effect. Pathologic response was significantly associated with clinical outcomes in our study (distant

Disclaimer

None.

Funding

Authors acknowledge financial support from the National Institutes of Health (NIH-USA U24CA194354, and NIH-USA U01CA190234). This project was partially funded by the Kaye Scholar Award and the Brigham and Women’s Hospital Department of Radiation Oncology Clinical Translational Grant.

Conflict of interest statement

None.

References (38)

P. Lambin et al.
Radiomics: Extracting more information from medical images using advanced feature analysis
Eur J Cancer
(2012)
V. Kumar et al.
Radiomics: the process and the challenges
Magn Reson Imaging
(2012)
E. Rios Velazquez et al.
A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists’ delineations and with the surgical specimen
Radiother Oncol
(2012)
T.P. Coroller et al.
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Radiother Oncol
(2015)
K.S. Albain et al.
Radiotherapy plus chemotherapy with or without surgical resection for stage III non-small-cell lung cancer: a phase III randomised controlled trial
Lancet
(2009)
M.D. Hellmann et al.
Pathological response after neoadjuvant chemotherapy in resectable non-small-cell lung cancers: proposal for the use of major pathological response as a surrogate endpoint
Lancet Oncol
(2014)
G. Mouillet et al.
Pathologic complete response to preoperative chemotherapy predicts cure in early-stage non-small-cell lung cancer: combined analysis of two IFCT randomized trials
J Thorac Oncol
(2012)
E.A. Eisenhauer et al.
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eur J Cancer
(2009)
M. Werner-Wasik et al.
Assessment of lung cancer response after nonoperative therapy: tumor diameter, bidimensional product, and volume. A serial CT scan-based study
Int J Radiat Oncol Biol Phys
(2001)
J.D. Bradley 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 Radiat Oncol Biol Phys
(2002)

B.M. Alexander et al.

Tumor volume is a prognostic factor in non–small-cell lung cancer treated with chemoradiotherapy

Int J Radiat Oncol

(2011)

T.E. Stinchcombe et al.

Post-chemotherapy gross tumor volume is predictive of survival in patients with stage III non-small cell lung cancer treated with combined modality therapy

Lung Cancer

(2006)

R.J. Cerfolio et al.

Repeat FDG-PET after neoadjuvant therapy is a predictor of pathologic response in patients with non-small cell lung cancer

Ann Thorac Surg

(2004)

R.J. Gillies et al.

Radiomics: images are more than pictures, they are data

Radiology

(2015)

C. Parmar et al.

Robust radiomics feature quantification using semiautomatic volumetric segmentation

PLoS ONE

(2014)

R.T.H. Leijenaar et al.

Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability

Acta Oncol

(2013)

H.J.W.L. Aerts et al.

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Nat Commun

(2014)

C. Parmar et al.

Machine learning methods for quantitative radiomic biomarkers

Sci Rep

(2015)

R.T. Leijenaar et al.

External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma

Acta Oncol

(2015)

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¹: Equal contribution.

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Lung cancer radiotherapyRadiomic phenotype features predict pathological response in non-small cell lung cancer

Abstract

Background and purpose

Materials and Methods

Results

Conclusion

Section snippets

Patient selection

Results

Discussion

Disclaimer

Funding

Conflict of interest statement

Eur J Cancer

Magn Reson Imaging

Radiother Oncol

Radiother Oncol

Lancet

Lancet Oncol

J Thorac Oncol

Eur J Cancer

Int J Radiat Oncol Biol Phys

Int J Radiat Oncol Biol Phys

Int J Radiat Oncol

Lung Cancer

Ann Thorac Surg

Radiomics: images are more than pictures, they are data

Radiology

Robust radiomics feature quantification using semiautomatic volumetric segmentation

PLoS ONE

Stability of FDG-PET radiomics features: an integrated analysis of test-retest and inter-observer variability

Acta Oncol

Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

Nat Commun

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Sci Rep

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Lung cancer radiotherapy
Radiomic phenotype features predict pathological response in non-small cell lung cancer