Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images.

PURPOSE Changes in textural characteristics of diffuse optical spectroscopic (DOS) functional images, accompanied by alterations in their mean values, are demonstrated here for the first time as early surrogates of ultimate treatment response in locally advanced breast cancer (LABC) patients receiving neoadjuvant chemotherapy (NAC). NAC, as a standard component of treatment for LABC patient, induces measurable heterogeneous changes in tumor metabolism which were evaluated using DOS-based metabolic maps. This study characterizes such inhomogeneous nature of response development, by determining alterations in textural properties of DOS images apparent at early stages of therapy, followed later by gross changes in mean values of these functional metabolic maps. METHODS Twelve LABC patients undergoing NAC were scanned before and at four times after treatment initiation, and tomographic DOS images were reconstructed at each time. Ultimate responses of patients were determined clinically and pathologically, based on a reduction in tumor size and assessment of residual tumor cellularity. The mean-value parameters and textural features were extracted from volumetric DOS images for several functional and metabolic parameters prior to the treatment initiation. Changes in these DOS-based biomarkers were also monitored over the course of treatment. The measured biomarkers were applied to differentiate patient responses noninvasively and compared to clinical and pathologic responses. RESULTS Responding and nonresponding patients demonstrated different changes in DOS-based textural and mean-value parameters during chemotherapy. Whereas none of the biomarkers measured prior the start of therapy demonstrated a significant difference between the two patient populations, statistically significant differences were observed at week one after treatment initiation using the relative change in contrast/homogeneity of seven functional maps (0.001<p<0.049), and mean value of water content in tissue (p=0.010). The cross-validated sensitivity and specificity of these parameters at week one of therapy ranged between 80%-100% and 67%-100%, respectively. Higher levels of statistically significant differences were exhibited at week four after start of treatment, with cross-validated sensitivities and specificities ranging between 80% and 100% for three textural and three mean-value parameters. The combination of the textural and mean-value parameters in a "hybrid" profile could better separate the two patient populations early on during a course of treatment, with cross-validated sensitivities and specificities of up to 100% (p=0.001). CONCLUSIONS The results of this study suggest that alterations in textural characteristics of DOS images, in conjunction with changes in their mean values, can classify noninvasively the ultimate clinical and pathologic response of LABC patients to chemotherapy, as early as one week after start of their treatment. This provides a basis for using DOS imaging as a tool for therapy personalization.

[1]  G. Hortobagyi,et al.  Comprehensive management of locally advanced breast cancer , 1990, Cancer.

[2]  Peter Gibbs,et al.  Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.

[3]  Jie Li,et al.  DW-MRI ADC values can predict treatment response in patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy , 2011, Medical Oncology.

[4]  Daniel L. Rubin,et al.  Dynamic contrast-enhanced MRI-based biomarkers of therapeutic response in triple-negative breast cancer. , 2013, Journal of the American Medical Informatics Association : JAMIA.

[5]  Shan Tan,et al.  Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. , 2013, International journal of radiation oncology, biology, physics.

[6]  Martin J. Yaffe,et al.  Imaging innovations for cancer therapy response monitoring , 2012 .

[7]  M. Christian,et al.  [New guidelines to evaluate the response to treatment in solid tumors]. , 2000, Bulletin du cancer.

[8]  A. Darzi,et al.  Diffuse optical imaging of the healthy and diseased breast: A systematic review , 2008, Breast Cancer Research and Treatment.

[9]  Sarah E Bohndiek,et al.  Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment , 2014, Magnetic resonance in medicine.

[10]  G. Hortobagyi,et al.  Outcome after pathologic complete eradication of cytologically proven breast cancer axillary node metastases following primary chemotherapy. , 2005, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[11]  X. Intes Time-domain optical mammography SoftScan: initial results. , 2005, Academic radiology.

[12]  Richard Su,et al.  Feasibility of optoacoustic visualization of high-intensity focused ultrasound-induced thermal lesions in live tissue. , 2010, Journal of biomedical optics.

[13]  M. Yaffe,et al.  Functional Imaging Using Diffuse Optical Spectroscopy of Neoadjuvant Chemotherapy Response in Women with Locally Advanced Breast Cancer , 2010, Clinical Cancer Research.

[14]  Isabelle Thomassin,et al.  Diffusion-weighted MRI in pretreatment prediction of response to neoadjuvant chemotherapy in patients with breast cancer , 2013, European Radiology.

[15]  G. Hortobagyi,et al.  Clinical course of breast cancer patients with complete pathologic primary tumor and axillary lymph node response to doxorubicin-based neoadjuvant chemotherapy. , 1999, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[16]  T. Powles,et al.  Good clinical response of breast cancers to neoadjuvant chemoendocrine therapy is associated with improved overall survival. , 2005, Annals of oncology : official journal of the European Society for Medical Oncology.

[17]  Michael C. Kolios,et al.  Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo. , 2013, Translational oncology.

[18]  J. Gralow,et al.  Neoadjuvant chemotherapy for locally advanced breast cancer. , 2009, Seminars in radiation oncology.

[19]  Anna L. Brown,et al.  Effect of preoperative chemotherapy on the outcome of women with operable breast cancer. , 1998, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[20]  G. Hortobagyi,et al.  Locally advanced breast cancer. , 1999, Hematology/oncology clinics of North America.

[21]  Carsten Denkert,et al.  Response-guided neoadjuvant chemotherapy for breast cancer. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[22]  B. Tromberg,et al.  Imaging in breast cancer: Diffuse optics in breast cancer: detecting tumors in pre-menopausal women and monitoring neoadjuvant chemotherapy , 2005, Breast Cancer Research.

[23]  Kevin M Brindle,et al.  Imaging tumour cell metabolism using hyperpolarized 13C magnetic resonance spectroscopy. , 2010, Biochemical Society transactions.

[24]  M. Giger,et al.  Volumetric texture analysis of breast lesions on contrast‐enhanced magnetic resonance images , 2007, Magnetic resonance in medicine.

[25]  Xia Li,et al.  Analyzing Spatial Heterogeneity in DCE- and DW-MRI Parametric Maps to Optimize Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer. , 2014, Translational oncology.

[26]  H. Sagili,et al.  Study of tumour cellularity in locally advanced breast carcinoma on neo-adjuvant chemotherapy. , 2014, Journal of clinical and diagnostic research : JCDR.

[27]  K. Miles,et al.  Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.

[28]  Michael E Phelps,et al.  Positron emission tomography scanning: current and future applications. , 2002, Annual review of medicine.

[29]  Hany Soliman,et al.  Diffuse optical spectroscopy evaluation of treatment response in women with locally advanced breast cancer receiving neoadjuvant chemotherapy. , 2012, Translational oncology.

[30]  Steinar Lundgren,et al.  Dynamic contrast‐enhanced MRI texture analysis for pretreatment prediction of clinical and pathological response to neoadjuvant chemotherapy in patients with locally advanced breast cancer , 2014, NMR in biomedicine.

[31]  P. Fumoleau,et al.  [18F]FDG-PET predicts complete pathological response of breast cancer to neoadjuvant chemotherapy , 2007, European Journal of Nuclear Medicine and Molecular Imaging.

[32]  Omar Falou,et al.  Quantitative ultrasound visualization of cell death: Emerging clinical applications for detection of cancer treatment response , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[33]  Hon J. Yu,et al.  Predicting pathologic response to neoadjuvant chemotherapy in breast cancer by using MR imaging and quantitative 1H MR spectroscopy. , 2009, Radiology.

[34]  Salim Djeziri,et al.  Optical tomography as adjunct to x-ray mammography: methods and results , 2007, SPIE BiOS.

[35]  W. Han,et al.  Early metabolic response using FDG PET/CT and molecular phenotypes of breast cancer treated with neoadjuvant chemotherapy , 2011, BMC Cancer.

[36]  J. Bradley,et al.  Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[37]  S. Giordano,et al.  Update on locally advanced breast cancer. , 2003, The oncologist.

[38]  Chih-Kuang Yeh,et al.  Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images. , 2011, Medical physics.

[39]  Martin J. Yaffe,et al.  Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture , 2014, Oncotarget.

[40]  Omar Falou,et al.  Quantitative ultrasound spectral parametric maps: Early surrogates of cancer treatment response , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[41]  E. Wolin,et al.  Texture analysis of medical images in radiotherapy , 2016 .

[42]  Q. Chu,et al.  Neoadjuvant Chemotherapy in Stage III Breast Cancer , 2005, The American surgeon.

[43]  A. Nishioka,et al.  Early prediction of response to neoadjuvant chemotherapy in patients with breast cancer using diffusion-weighted imaging and gray-scale ultrasonography , 2014, Oncology reports.

[44]  G. Hortobagyi,et al.  Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[45]  B. Tromberg,et al.  Diffuse optical monitoring of blood flow and oxygenation in human breast cancer during early stages of neoadjuvant chemotherapy. , 2007, Journal of biomedical optics.

[46]  G. Hortobagyi,et al.  Pathological assessment of response to induction chemotherapy in breast cancer. , 1986, Cancer research.

[47]  M. Tozaki,et al.  Predicting pathological response to neoadjuvant chemotherapy in breast cancer with quantitative 1H MR spectroscopy using the external standard method , 2010, Journal of magnetic resonance imaging : JMRI.

[48]  Ali Sadeghi-Naini,et al.  Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties. , 2014, Medical physics.

[49]  Michael E. Phelps,et al.  Usefulness of 3′-[F-18]Fluoro-3′-deoxythymidine with Positron Emission Tomography in Predicting Breast Cancer Response to Therapy , 2005, Molecular Imaging and Biology.

[50]  Kevin Brindle,et al.  New approaches for imaging tumour responses to treatment , 2008, Nature Reviews Cancer.

[51]  M. Helvie,et al.  Locally advanced breast carcinoma: accuracy of mammography versus clinical examination in the prediction of residual disease after chemotherapy. , 1996, Radiology.

[52]  B. Tromberg,et al.  Optical imaging of breast cancer oxyhemoglobin flare correlates with neoadjuvant chemotherapy response one day after starting treatment , 2011, Proceedings of the National Academy of Sciences.

[53]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .

[54]  Ming-Ting Wu,et al.  Monitoring breast cancer response to neoadjuvant systemic chemotherapy using parametric contrast-enhanced MRI: a pilot study. , 2007, Academic radiology.

[55]  F. Gallagher,et al.  Detecting treatment response in a model of human breast adenocarcinoma using hyperpolarised [1-13C]pyruvate and [1,4-13C2]fumarate , 2010, British Journal of Cancer.

[56]  Michael C. Kolios,et al.  Low-frequency quantitative ultrasound imaging of cell death in vivo. , 2013, Medical Physics (Lancaster).

[57]  Ali Sadeghi-Naini,et al.  Evaluation of neoadjuvant chemotherapy response in women with locally advanced breast cancer using ultrasound elastography. , 2013, Translational oncology.

[58]  M J Yaffe,et al.  Whole‐specimen histopathology: a method to produce whole‐mount breast serial sections for 3‐D digital histopathology imaging , 2007, Histopathology.

[59]  M. van Glabbeke,et al.  New guidelines to evaluate the response to treatment in solid tumors , 2000, Journal of the National Cancer Institute.

[60]  M. Hatt,et al.  Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.

[61]  David Hsiang,et al.  Diffuse optical spectroscopy measurements of healing in breast tissue after core biopsy: case study. , 2009, Journal of biomedical optics.

[62]  P. Flamen,et al.  Heterogeneity of metabolic response to systemic therapy in metastatic breast cancer patients. , 2010, Clinical oncology (Royal College of Radiologists (Great Britain)).

[63]  M. Campone,et al.  FDG PET evaluation of early axillary lymph node response to neoadjuvant chemotherapy in stage II and III breast cancer patients , 2011, European Journal of Nuclear Medicine and Molecular Imaging.

[64]  Brandon Whitcher,et al.  DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6 , 2011, British Journal of Cancer.

[65]  B. Tromberg,et al.  Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy , 2007, Proceedings of the National Academy of Sciences.

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

[67]  Vicky Goh,et al.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[68]  Jason B. Nikas,et al.  Prognosis of Treatment Response (Pathological Complete Response) in Breast Cancer , 2012, Biomarker insights.

[69]  D. Mankoff,et al.  Monitoring the response of patients with locally advanced breast carcinoma to neoadjuvant chemotherapy using [technetium 99m]‐sestamibi scintimammography , 1999, Cancer.

[70]  X. Intes Time-Domain Optical Mammography SoftScan , 2005 .

[71]  Michael C. Kolios,et al.  Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy , 2013, Clinical Cancer Research.

[72]  Xinmai Yang,et al.  Real-time monitoring of high-intensity focused ultrasound ablations with photoacoustic technique: an in vitro study. , 2011, Medical physics.

[73]  Ewert Bengtsson,et al.  3D Texture Analysis in Renal Cell Carcinoma Tissue Image Grading , 2014, Comput. Math. Methods Medicine.

[74]  R. Sen,et al.  Histopathologic changes following neoadjuvant chemotherapy in locally advanced breast cancer. , 2013, Indian journal of cancer.

[75]  B. Tromberg,et al.  Sources of absorption and scattering contrast for near-infrared optical mammography. , 2001, Academic radiology.

[76]  F E Turkheimer,et al.  Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography , 2013, Physics in medicine and biology.