Delta-radiomics signature predicts treatment outcomes after preoperative chemoradiotherapy and surgery in rectal cancer

BackgroundTo develop and compare delta-radiomics signatures from 2- (2D) and 3-dimensional (3D) features that predict treatment outcomes following preoperative chemoradiotherapy (CCRT) and surgery for locally advanced rectal cancer.MethodsIn total, 101 patients (training cohort, n = 67; validation cohort, n = 34) with locally advanced rectal adenocarcinoma between 2008 and 2015 were included. We extracted 55 features from T2-weighted magnetic resonance imaging (MRI) scans. Delta-radiomics feature was defined as the difference in radiomics feature before and after CCRT. Signatures were developed to predict local recurrence (LR), distant metastasis (DM), and disease-free survival (DFS) from 2D and 3D features. The least absolute shrinkage and selection operator regression was used to select features and build signatures. The delta-radiomics signatures and clinical factors were integrated into Cox regression analysis to determine if the signatures were independent prognostic factors.ResultsThe radiomics signatures for LR, DM, and DFS were developed and validated using both 2D and 3D features. Outcomes were significantly different in the low- and high-risk patients dichotomized by optimal cutoff in both the training and validation cohorts. In multivariate analysis, the signatures were independent prognostic factors even when considering the clinical parameters. There were no significant differences in C-index from 2D vs. 3D signatures.ConclusionsThis is the first study to develop delta-radiomics signatures for rectal cancer. The signatures successfully predicted the outcomes and were independent prognostic factors. External validation is warranted to ensure their performance.

[1]  S. Ricci,et al.  Comparison of Magnetic Resonance Imaging and Histopathological Response to Chemoradiotherapy in Locally Advanced Rectal Cancer , 2012, Annals of Surgical Oncology.

[2]  S. Punwani,et al.  Can We Improve the Reproducibility of Quantitative Multiparametric Prostate MR Imaging Metrics? , 2016, Radiology.

[3]  T. Aparicio.  Renehan AG, Malcomson L, Emsley R, et al (2016) Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer (the OnCoRe project): a propensity-score matched cohort analysis. Lancet Oncol 17:174-83 , 2016 .

[4]  E. Medico,et al.  Predicting locally advanced rectal cancer response to neoadjuvant therapy with 18F-FDG PET and MRI radiomics features , 2019, European Journal of Nuclear Medicine and Molecular Imaging.

[5]  C. Capirci,et al.  Adjuvant chemotherapy in rectal cancer: Defining subgroups who may benefit after neoadjuvant chemoradiation and resection: A pooled analysis of 3,313 patients , 2015, International journal of cancer.

[6]  Peter Balter,et al.  Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer , 2017, Scientific Reports.

[7]  V. Lemmens,et al.  Effect of adjuvant chemotherapy on recurrence-free survival varies by neo-adjuvant treatment in patients with stage III rectal cancer. , 2015, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[8]  O. Sagol,et al.  Diffusion-weighted MRI and MR- volumetry--in the evaluation of tumor response after preoperative chemoradiotherapy in patients with locally advanced rectal cancer. , 2015, Magnetic resonance imaging.

[9]  Xiao Liang,et al.  Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer , 2018, Journal of magnetic resonance imaging : JMRI.

[10]  P. Lambin,et al.  Fractal-based radiomic approach to predict complete pathological response after chemo-radiotherapy in rectal cancer , 2018, La radiologia medica.

[11]  Fang-Fang Yin,et al.  Effect of machine learning methods on predicting NSCLC overall survival time based on Radiomics analysis , 2018, Radiation Oncology.

[12]  A. Afaq,et al.  Magnetic resonance based texture parameters as potential imaging biomarkers for predicting long‐term survival in locally advanced rectal cancer treated by chemoradiotherapy , 2017, Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland.

[13]  Jiazhou Wang,et al.  Radiomic features of pretreatment MRI could identify T stage in patients with rectal cancer: Preliminary findings , 2018, Journal of magnetic resonance imaging : JMRI.

[14]  K. Lee,et al.  CT texture analysis in patients with locally advanced rectal cancer treated with neoadjuvant chemoradiotherapy: A potential imaging biomarker for treatment response and prognosis , 2017, PloS one.

[15]  Valentina D. A. Corino,et al.  Assessment of Stability and Discrimination Capacity of Radiomic Features on Apparent Diffusion Coefficient Images , 2018, Journal of Digital Imaging.

[16]  Zhenchao Tang,et al.  Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer , 2017, Clinical Cancer Research.

[17]  Gina Brown,et al.  Magnetic Resonance Tumor Regression Grade and Residual Mucosal Abnormality as Predictors for Pathological Complete Response in Rectal Cancer Postneoadjuvant Chemoradiotherapy , 2016, Diseases of the colon and rectum.

[18]  Karin Haustermans,et al.  Long-term outcome in patients with a pathological complete response after chemoradiation for rectal cancer: a pooled analysis of individual patient data. , 2010, The Lancet. Oncology.

[19]  H. Lee,et al.  Impact of Tumor Regression Grade as a Major Prognostic Factor in Locally Advanced Rectal Cancer after Neoadjuvant Chemoradiotherapy: A Proposal for a Modified Staging System , 2018, Cancers.

[20]  G. Collewet,et al.  Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. , 2004, Magnetic resonance imaging.

[21]  Marco De Spirito,et al.  Delta radiomics for rectal cancer response prediction with hybrid 0.35 T magnetic resonance-guided radiotherapy (MRgRT): a hypothesis-generating study for an innovative personalized medicine approach , 2018, La radiologia medica.

[22]  Richard Emsley,et al.  Watch-and-wait approach versus surgical resection after chemoradiotherapy for patients with rectal cancer (the OnCoRe project): a propensity-score matched cohort analysis. , 2016, The Lancet. Oncology.

[23]  T. Niu,et al.  Rectal Cancer: Assessment of Neoadjuvant Chemoradiation Outcome based on Radiomics of Multiparametric MRI , 2016, Clinical Cancer Research.

[24]  W. Lee,et al.  Local excision after neoadjuvant chemoradiation therapy in advanced rectal cancer: a national multicenter analysis. , 2013, American journal of surgery.

[25]  T. Beißbarth,et al.  Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[26]  M. Picchio,et al.  Long-term Outcome of Local Excision After Preoperative Chemoradiation for ypT0 Rectal Cancer , 2014, Diseases of the colon and rectum.

[27]  C. Buchpiguel,et al.  Consolidation chemotherapy during neoadjuvant chemoradiation (CRT) for distal rectal cancer leads to sustained decrease in tumor metabolism when compared to standard CRT regimen , 2016, Radiation Oncology.

[28]  H. Putter,et al.  Adjuvant chemotherapy after preoperative (chemo)radiotherapy and surgery for patients with rectal cancer: a systematic review and meta-analysis of individual patient data. , 2015, The Lancet. Oncology.

[29]  W. Hohenberger,et al.  Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years. , 2012, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  B. Ganeshan,et al.  Performance of diffusion-weighted imaging, perfusion imaging, and texture analysis in predicting tumoral response to neoadjuvant chemoradiotherapy in rectal cancer patients studied with 3T MR: initial experience , 2016, Abdominal Radiology.

[31]  Christophe Hennequin,et al.  Comparison of two neoadjuvant chemoradiotherapy regimens for locally advanced rectal cancer: results of the phase III trial ACCORD 12/0405-Prodige 2. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[32]  V. Goh,et al.  Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. , 2011, Radiology.

[33]  Andre Dekker,et al.  Magnetic Resonance, Vendor-independent, Intensity Histogram Analysis Predicting Pathologic Complete Response After Radiochemotherapy of Rectal Cancer. , 2018, International journal of radiation oncology, biology, physics.

[34]  Liang Xu,et al.  Application of texture analysis based on apparent diffusion coefficient maps in discriminating different stages of rectal cancer , 2017, Journal of magnetic resonance imaging : JMRI.

[35]  G. Beets,et al.  MRI and Diffusion-weighted MRI Volumetry for Identification of Complete Tumor Responders After Preoperative Chemoradiotherapy in Patients With Rectal Cancer: A Bi-institutional Validation Study , 2015, Annals of surgery.

[36]  Rui Bi,et al.  Apparent Diffusion Coefficient (ADC) Value: A Potential Imaging Biomarker That Reflects the Biological Features of Rectal Cancer , 2014, PloS one.

[37]  L. Ellis,et al.  Quantified pathologic response assessed as residual tumor burden is a predictor of recurrence‐free survival in patients with rectal cancer who undergo resection after neoadjuvant chemoradiotherapy , 2013, Cancer.

[38]  D. Dong,et al.  Radiomics Features of Multiparametric MRI as Novel Prognostic Factors in Advanced Nasopharyngeal Carcinoma , 2017, Clinical Cancer Research.

[39]  Fábio Guilherme Campos,et al.  Operative Versus Nonoperative Treatment for Stage 0 Distal Rectal Cancer Following Chemoradiation Therapy: Long-term Results , 2004, Annals of surgery.

[40]  Chaofeng Liang,et al.  A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme , 2017, Scientific Reports.