Optical Mammography in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy

Rationale and Objectives

We present an optical mammography study that aims to develop quantitative measures of pathologic response to neoadjuvant chemotherapy (NAC) in patients with breast cancer. Such quantitative measures are based on the concentrations of oxyhemoglobin ([HbO 2 ]), deoxyhemoglobin ([Hb]), total hemoglobin ([HbT]), and hemoglobin saturation (SO 2 ) in breast tissue at the tumor location and at sequential time points during chemotherapy.

Materials and Methods

Continuous-wave, spectrally resolved optical mammography was performed in transmission and parallel-plate geometry on 10 patients before treatment initiation and at each NAC administration (mean number of optical mammography sessions: 12, range: 7–18). Data on two patients were discarded for technical reasons. The patients were categorized as responders (R, >50% decrease in tumor size), or nonresponders (NR, <50% decrease in tumor size) based on imaging and histopathology results.

Results

At 50% completion of the NAC regimen (therapy midpoint), R (6/8) demonstrated significant decreases in SO 2 (−27% ± 4%) and [HbT] (−35 ± 4 µM) at the tumor location with respect to baseline values. By contrast, NR (2/8) showed nonsignificant changes in SO 2 and [HbT] at therapy midpoint. We introduce a cumulative response index as a quantitative measure of the individual patient’s response to therapy. At therapy midpoint, the SO 2 -based cumulative response index had a sensitivity of 100% and a specificity of 100% for the identification of R.

Conclusions

These results show that optical mammography is a promising tool to assess individual response to NAC at therapy midpoint to guide further decision making for neoadjuvant therapy.

Introduction

Neoadjuvant Chemotherapy (NAC)

NAC is administered to patients before surgery in an effort to reduce the primary tumor size, whereas adjuvant chemotherapy is administered following surgery in an effort to reduce the risk of residual disease and cancer recurrence. A patient’s response to NAC may be assessed by physical examination or breast imaging (clinical response), or by histology post surgery (pathologic response) . Assessing response to neoadjuvant treatment is crucial, as a pathologic complete response (pCR), defined as having no residual carcinoma in the resected breast tissue and in axillary lymph nodes, has been associated with improved survival . Strictly defined, pCR requires the absence of invasive tumor in the resected specimen, although some clinicians use the more restrictive requirement of no residual invasive or in situ disease . Because of the better outcome associated with pCR, finding tools that can define the individual clinical response during the course of therapy and accurately predicting pathologic response would be of great benefit. This is also true in patients with poor response to treatment, as early identification of this problem may allow the physician to alter the chemotherapy regimen to avoid disease progression and to identify a more effective chemotherapy option.

Imaging Modalities Under Investigation to Monitor Therapy Response

Imaging methods sensitive to functional tissue changes are being investigated for monitoring breast cancer patients’ response to NAC. Functional tumor changes are of particular interest because of the limitations of structural assessment of tumor response based on physical examination, ultrasound imaging, or mammography . Current imaging methods used to assess clinical response are via a decrease in the standard uptake value of 18-fluorodeoxyglucose by positron emission tomography-computed tomography (PET/CT) , or a decrease in tumor size by contrast-enhanced magnetic resonance imaging (MRI) . Both of these methods, however, are expensive and invasive, as PET/CT requires an injection of a radiopharmaceutical, and MRI requires an injection of a gadolinium-based contrast agent. Furthermore, the appropriate timing and frequency for assessing clinical response have not been established, and studies thus far have typically imaged at a single time point during therapy .

Get Radiology Tree app to read full this article<

TABLE 1

Chronological Summary of Published Studies of Optical Mammography on Patients Undergoing Neoadjuvant Breast Cancer Chemotherapy

Reference Year No. of Subjects No. of Sessions Duration (wk) Major Findings Jakubowski et al. 2004 1 8 10 After 1 wk: ↓[HbT] (−26%) (PR)

After 10 wk: ↓[HbT] (−56%), ↓[H 2 O](−67%), ↑SO 2 (PR)

Similar trend in control tissue (breast and abdomen):

↓[HbO 2 ], →[Hb], ↓Hgb (−16%) Choe et al. 2005 1 3 24 (Entire NAC) Between the fourth and seventh cycles:

↓[HbT] (−50%) (PR) Tromberg et al. 2005 1 6 1 After 1 wk:

↓TOI (−60%), ↓[HbT] (−30%), ↓[H 2 O] (−30%), ↑[lipid] (+20%) Cerussi et al. 2007 11 2 1 After 1 wk:

↓[Hb] (−27%), ↓[HbO 2 ] (−33%), ↓[H 2 O] (−11%) (responders)

No change (NR and healthy tissue) Zhou et al. 2007 1 6 1 After 1 wk:

↓T/N [Hb] (−31%), ↓T/N [HbO 2 ] (−27%), ↓T/N [HbT] (−28%), ↑T/N [lipid] (+16%), ↓T/N blood flow (−25%) (PR) Zhu et al. 2008 11 3 Entire NAC After the second cycle:

Lower BVI in complete responders vs partial/NR

At the end of therapy:

↓BVI (−71%) (pCR), ↓BVI (−54%) (PR), ↓BVI (−13%) (NR) Jiang et al. 2009 7 5–11 Entire NAC Within week 4:

↓[HbT] (−64%) (pCR)

↑[HbT] (+17%) (non-pCR) Soliman et al. 2010 10 5 Entire NAC After 4 wk:

↓[Hb] (−68%), ↓[HbO 2 ] (−59%), ↓[H 2 O] (−51%), ↓SP (−53%) (pCR, PR)

↓[Hb] (−18%), ↓[HbO 2 ] (−18%), ↓[H 2 O] (−15%), ↓SP (−13%) (NR) Cerussi et al. 2010 1 19 18 (Entire NAC) ↓T/N TOI ratio throughout NAC (−50% at the end of therapy) Roblyer et al. 2011 23 8 1 After 1 d:

↑[HbO 2 ] (+41%, +44%) (pCR, PR), ↓[HbO 2 ] (−22%) (NR)

After 1 wk:

↓[HbO 2 ] (−22%) (pCR), →[HbO 2 ] (PR), ↓[HbO 2 ] (−49%) (NR) Cerussi et al. 2011 34 3 Entire NAC At therapy midpoint:

↓T/N TOI: −47% (pCR), −20% (non-pCR) Pakalniskis et al. 2011 11 3 Entire NAC During therapy:

↓[HbT] (10%/mo) (pCR), →[HbT] (PR) Falou et al. 2012 15 5 Entire NAC After 1 wk:

↑[Hb] (17%), ↑[HbO 2 ] (8%), ↑[HbT] (10%), ↑[H 2 O] (11%) (responders)

↓[Hb] (−14%), ↓[HbO 2 ] (−18%), ↓[HbT] (−17%), ↓[H 2 O] (−29%) (NR) Ueda et al. 2012 41 1 Baseline only Before treatment:

Tumor [Hb], [HbO 2 ], [HbT] did not correlate with response

Tumor StO 2 was higher in pCR (78%) than in non-pCR (72%) Busch et al. 2013 30 2–4 Entire NAC Initial test of a statistical analysis of [HbT], SO 2 , µ s ′ images to extract a predictive parameter of NAC response Zhu et al. 2013 32 4 Entire NAC Before treatment:

Greater pretreatment [HbT] in responders than in NR

After the first cycle:

↓[HbT] (−12%) (CR); →[HbT] (PR, NR) Zhu et al. 2014 34 1 Baseline only Before treatment:

Tumor pretreatment [HbT] is the best predictor of NAC response. Jiang et al. 2014 19 3 Entire NAC Before treatment:

Greater pretreatment [HbT] in pCR than in non-pCR

By the end of the first cycle:

↓[HbT] (−43%) (pCR), ↑[HbT] (+20%) (PR) Schaafsma et al. 2015 22 4 Entire NAC After the first cycle (3 wk):

↓[HbO 2 ] (−14%), (pCR, PR); ↑[HbO 2 ] (+36%), (NR)

After the third cycle (9 wk, therapy midpoint):

↓[HbO 2 ] (−32%), (pCR, PR); ↑[HbO 2 ] (+10%), (NR) Gunther et al. 2015 22 2 2 After 2 wk:

↓[HbT] (−35%) (pCR), ↓[HbT] (−5%) (PR), ↑[HbT] (+5%) (NR)

Faster breath-hold washout rate of [Hb] in pCR vs non-pCR Tran et al. 2016 22 5 Entire NAC After 1 wk:

Combination of tumor ↓[HbT] and quantitative ultrasound parameters resulted in perfect markers for response. Tromberg et al. 2016 34 4 Entire NAC At therapy midpoint:

↓T/N TOI: −46% (pCR), −14% (non-pCR) Sajjadi et al. 2017 13 2–3 4 After 4 wk:

Different T/N breast compression dynamics of [HbT] and SO 2

in CR and PR vs NR This work 2017 8 7–18 16–23

(Entire NAC) At therapy midpoint:

↓[HbT] (−35%), ↓SO 2 (−27%) (pCR and PR)

→[HbT], →SO 2 (NR)

[Hb], concentration of deoxyhemoglobin; [HbO 2 ], concentration of oxyhemoglobin; [HbT], concentration of total hemoglobin; BVI, blood volume index ([HbT] × TV); CR, complete responder; NAC, neoadjuvant chemotherapy; NR, nonresponders; pCR, pathologic complete responder; PR, partial responder; SO 2 , hemoglobin saturation; SP, scattering power; T/N, tumor-to-normal ratio; TOI, tissue optical index ([Hb] × [H 2 O]/[lipid]); TV, tumor volume.

↓, ↑, and → indicate a decrease, increase, and no change, respectively.

Get Radiology Tree app to read full this article<

Research Plan for This Study

Get Radiology Tree app to read full this article<

Materials and Methods

Optical Imaging of Patients with Breast Cancer

Get Radiology Tree app to read full this article<

TABLE 2

Patient Details and Treatment Regimens

NACP No. Ref. No. Age (y) Pretreatment Cancer Size (cm) Cancer Stage Cancer Subtype Chemotherapy Agent Infusion Frequency ( n ) Treatment Duration (wk) Post-treatment Cancer Size (Hist.) (cm) NAC Response MRI X-ray Optical 1 164 38 9.4 1.2 IIIC ER+/HER2− Paclitaxel Weekly (12) 31 6.0 NR (PR2) Capecitabine Biweekly (5) 2 163 72 3.2 2.1 IIB TNBC Doxorubicin, cyclophosphamide Biweekly (4) 23 — R (pCR) Paclitaxel Weekly (12) 3 165 57 2.5 1.4 IIB ER+/HER2− Doxorubicin, cyclophosphamide Biweekly (4) 22 0.9 R (PR1) Paclitaxel Weekly (12) 4 166 54 2.9 1.3 IIIA HER2+ Carboplatin, docetaxel, trastuzumab, pertuzumab Every 3 wk (6) 16 2.8 NR (PR2) 5 167 46 4.4 3.7 IV HER2+ Carboplatin, docetaxel, trastuzumab, pertuzumab Every 3 wk (6) 17 — R (pCR) 6 168 47 6.0 4.4 IIB TNBC Doxorubicin, cyclophosphamide Biweekly (4) 21 0.4 R (PR1) Paclitaxel (with carboplatin every third week) Weekly (11) 7 169 44 7.0 3.5 IIIA ER+/HER2− Doxorubicin, cyclophosphamide Biweekly (4) 16 0.6 R (PR1) Paclitaxel Biweekly (4) 8 170 74 Inflam. 2.6 IIIB ER+/HER2− Doxorubicin, cyclophosphamide Every 3 wk (4) 20 1.7 R (PR1) Paclitaxel Weekly (8) 9 171 44 Inflam. 1.3 IIIB ER+/HER2− Doxorubicin, cyclophosphamide Biweekly (4) 20 11.3 NR (PR2) Paclitaxel Weekly (12) 10 173 56 4.5 1.9 IIB HER2+ Carboplatin, docetaxel, trastuzumab, pertuzumab Every 3 wk (6) 17 — R (pCR)

ER+/HER2−, positive for estrogen receptors and negative for human epidermal growth factor receptor 2; HER2+, positive for human epidermal growth factor receptor 2; MRI, magnetic resonance imaging; NAC, neoadjuvant chemotherapy; NACP, neoadjuvant chemotherapy patient; NR, nonresponder; pCR, pathologic complete response; PR1, partial response 1; PR2, partial response 2; R, responder; ROI, region of interest; TNBC, triple-negative breast cancer.

Note: Ref. #: progressive patient number; age: age of the patient at the time of the baseline scan; pretreatment cancer size: maximum tumor dimension pretreatment (one column reports the dimension from MRI or full-field digital mammography; one column reports the size of the tumor ROI from optical mammography); inflam.: inflammatory breast cancer; cancer stage: initial clinical cancer stage; cancer subtype: TNBC, ER+/HER2−, and HER2+; chemotherapy agent: chemotherapy drug administered to the patient; infusion frequency (total number): how often infusions were performed (and total number of infusions); duration of treatment: how long the patients underwent treatment (including breaks in therapy schedule); post-treatment cancer size (hist.): maximum tumor dimension post treatment from histology after surgical resection; response level: individual patient’s response (R: responder showing either a pCR [no remaining tumor] or PR1 [tumor decreased by more than 50% in size]; NR: nonresponder showing PR2 [tumor decreased by less than 50% in size]).

Get Radiology Tree app to read full this article<

Laboratory Parameters and Response Categories

Get Radiology Tree app to read full this article<

Data Processing

Get Radiology Tree app to read full this article<

Statistical Analysis

Get Radiology Tree app to read full this article<

Results

Patient Measurements

Get Radiology Tree app to read full this article<

TABLE 3

Summary of the Means and Standard Errors of Relative Changes in [Hb], [HbO 2 ], [HbT], and SO 2 at the Tumor ROI From Baseline Over Five Binned Time Windows for Each Response Category

Group Percent Therapy Complete Response to Therapy at the Tumor ROI (% Change from Baseline) [Hb] [HbO 2 ] [HbT] SO 2 Responders ( n = 6) 10 (0, 20] −2 ± 8 −6 ± 7 −5 ± 7 −2 ± 1 30 (20, 40] −13 ± 7 −36 ± 6 −28 ± 6 −12 ± 3 50 (40, 60] −4 ± 5 −52 ± 5 −35 ± 4 −27 ± 4 70 (60, 80] −4 ± 6 −56 ± 5 −38 ± 4 −32 ± 4 90 (80, 100] 4 ± 7 −52 ± 4 −36 ± 4 −26 ± 4 Nonresponders ( n = 2) 10 (0, 20] −9 ± 7 −9 ± 7 −9 ± 7 0 ± 1 30 (20, 40] 1 ± 4 −5 ± 4 −3 ± 4 −2 ± 0 50 (40, 60] 1 ± 5 −3 ± 1 −2 ± 2 −1 ± 1 70 (60, 80] 0 ± 11 −15 ± 12 −10 ± 12 −6 ± 2 90 (80, 100] 23 ± 12 3 ± 15 10 ± 14 −8 ± 3

[Hb], concentration of deoxyhemoglobin; [HbO 2 ], concentration of oxyhemoglobin; [HbT], concentration of total hemoglobin; ROI, region of interest; SO 2 , hemoglobin saturation.

Beneath the response category, the number of patients in each group ( n ) is also provided.

Get Radiology Tree app to read full this article<

$Figure 4, Average change in blood volume, [HbT], and Hgb relative to the first infusion throughout chemotherapy for responders and nonresponders. All patients show a similar systemic decrease in Hgb during neoadjuvant chemotherapy, but blood volume fraction in breast tissue decreases in responders and increases in nonresponders. [HbT], concentration of total hemoglobin; Hgb, hemoglobin concentration in blood.$

Get Radiology Tree app to read full this article<

TABLE 4

Predictive Values of the Neoadjuvant Chemotherapy; Response Assessment Based on Changes From Baseline ( P Values) and CRI (Sensitivity and Specificity) From [HbT], SO 2 , [Hb], and [HbO 2 ] Measurements at the Tumor ROI for Each Time Bin

% Therapy Complete % Change From Baseline CRI [HbT] SO 2 [Hb] [HbO 2 ] [HbT] SO 2 [Hb] [HbO 2 ]P Value_P_ Value_P_ Value_P_ Value Sens/Spec Sens/Spec Sens/Spec Sens/Spec 10 (0, 20] 0.9 0.8 0.6 1 0.33/1 0.5/0.5 0.33/0.5 0.5/0.5 30 (20, 40] 0.06 0.06 0.2 0.06 0.67/0.5 0.83/1 0.67/0.5 0.83/1 50 (40, 60] 0.01 0.01 0.46 0.01 0.83/1 1/1 0.67/0 0.83/1 70 (60, 80] 0.05 0.002 0.6 0.02 0.83/1 1/1 0.67/0.5 1/1 90 (80, 100] 0.01 0.01 0.14 0.004 1/0.5 1/1 0.5/0.5 1/1

[Hb], concentration of deoxyhemoglobin; [HbO 2 ], concentration of oxyhemoglobin; [HbT], concentration of total hemoglobin; CRI, cumulative response index; ROI, region of interest; sens, sensitivity; spec, specificity; SO 2 , hemoglobin saturation.

Get Radiology Tree app to read full this article<

Cumulative Response Index (CRI)

Get Radiology Tree app to read full this article<

CRI(n)=∑ni=1diσ(di)∑ni=1∣∣di∣∣σ(di) CRI

(

)

∑

(

)

∑

(

)

Get Radiology Tree app to read full this article<

Discussion

[HbT] Response to NAC at the Tumor ROI

Get Radiology Tree app to read full this article<

SO 2 Response to NAC at the Tumor ROI

Get Radiology Tree app to read full this article<

Limitations of the Study and Future Directions

Get Radiology Tree app to read full this article<

Conclusions

Get Radiology Tree app to read full this article<

Acknowledgments

Get Radiology Tree app to read full this article<

References

1. Mauri D., Pavlidis N., Ioannidis J.P.A.: Neoadjuvant versus adjuvant systemic treatment in breast cancer: a meta-analysis. J Natl Cancer Inst 2005; 97: pp. 188-194.
2. Rastogi P., Anderson S.J., Bear H.D., et. al.: Preoperative chemotherapy: updates of National Surgical Adjuvant Breast and Bowel Project Protocols B-18 and B-27. J Clin Oncol 2008; 26: pp. 778-785.
3. Von Minckwitz G., Untch M., Blohmer J.U., et. al.: Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol 2012; 30: pp. 1796-1804.
4. Guarneri V., Broglio K., Kau S., et. al.: Prognostic value of pathologic complete response after primary chemotherapy in relation to hormone receptor status and other factors. J Clin Oncol 2006; 24: pp. 1037-1044.
5. Gampenrieder S.P., Rinnerthaler G., Greil R.: Neoadjuvant chemotherapy and targeted therapy in breast cancer: past, present, and future. J Oncol 2013; 732047
6. Chagpar A.B., Middleton L.P., Sahin A.A., et. al.: Accuracy of physical examination, ultrasonography, and mammography in predicting residual pathologic tumor size in patients treated with neoadjuvant chemotherapy. Ann Surg 2006; 243: pp. 257-264.
7. Groheux D., Majdoub M., Sanna A., et. al.: Early metabolic response to neoadjuvant treatment: FDG PET/CT criteria according to breast cancer subtype. Radiology 2015; 277: pp. 358-371.
8. Loo C.E., Straver M.E., Rodenhuis S., et. al.: Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. J Clin Oncol 2011; 29: pp. 660-666.
9. Fantini S., Sassaroli A.: Near-infrared optical mammography for breast cancer detection with intrinsic contrast. Ann Biomed Eng 2012; 40: pp. 398-407.
10. Cerussi A., Shah N., Hsiang D., et. al.: In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. J Biomed Opt 2006; 11: pp. 44005.
11. Zhu Q., Kurtzma S.H., Hegde P., et. al.: Utilizing optical tomography with ultrasound localization to image heterogeneous hemoglobin distribution in large breast cancers. Neoplasia 2005; 7: pp. 263-270.
12. Cerussi A., Hsiang D., Shah N., et. al.: Predicting response to breast cancer neoadjuvant chemotherapy using diffuse optical spectroscopy. Proc Natl Acad Sci USA 2007; 104: pp. 4014-4019.
13. Zhu Q., Tannenbaum S., Hegde P., et. al.: Noninvasive monitoring of breast cancer during neoadjuvant chemotherapy using optical tomography with ultrasound localization. Neoplasia 2008; 10: pp. 1028-1040.
14. Roblyer D., Ueda S., Cerussi A., et. al.: Optical imaging of breast cancer oxyhemoglobin flare correlates with neoadjuvant chemotherapy response one day after starting treatment. Proc Natl Acad Sci USA 2011; 108: pp. 14626-14631.
15. Cerussi A.E., Tanamai V.W., Hsiang D., et. al.: Diffuse optical spectroscopic imaging correlates with final pathological response in breast cancer neoadjuvant chemotherapy. Philos Trans A Math Phys Eng Sci 2011; 369: pp. 4512-4530.
16. Zhu Q., DeFusco P.A., Ricci A., et. al.: Breast cancer: assessing response to neoadjuvant chemotherapy by using US-guided near-infrared tomography. Radiology 2013; 266: pp. 433-442.
17. Jakubowski D.B., Cerussi A.E., Bevilacqua F., et. al.: Monitoring neoadjuvant chemotherapy in breast cancer using quantitative diffuse optical spectroscopy: a case study. J Biomed Opt 2004; 9: pp. 230-238.
18. Wang J., Jiang S., Li Z., et. al.: In vivo quantitative imaging of normal and cancerous breast tissue using broadband diffuse optical tomography. Med Phys 2010; 37: pp. 3715-3724.
19. Flexman M.L., Kim H.K., Gunther J.E., et. al.: Optical biomarkers for breast cancer derived from dynamic diffuse optical tomography. J Biomed Opt 2013; 18: pp. 96012.
20. Jiang S., Pogue B.W., Carpenter C.M., et. al.: Evaluation of breast tumor response to neoadjuvant chemotherapy with tomographic diffuse optical spectroscopy: case studies of tumor region-of-interest changes. Radiology 2009; 252: pp. 551-560.
21. Jiang S., Pogue B.W., Kaufman P., et. al.: Predicting breast tumor response to neoadjuvant chemotherapy with diffuse optical spectroscopic tomography prior to treatment. Clin Cancer Res 2014; 20: pp. 6006-6015.
22. Choe R., Konecky S.D., Corlu A., et. al.: Differentiation of benign and malignant breast tumors by in-vivo three-dimensional parallel-plate diffuse optical tomography. J Biomed Opt 2009; 14: pp. 24020.
23. Grosenick D., Wabnitz H., Moesta K.T., et. al.: Time-domain scanning optical mammography: II. Optical properties and tissue parameters of 87 carcinomas. Phys Med Biol 2005; 50: pp. 2451-2468.
24. Taroni P., Bassi A., Comelli D., et. al.: Diffuse optical spectroscopy of breast tissue extended to 1100 nm. J Biomed Opt 2012; 14: pp. 54030.
25. Fang Q., Selb J., Carp S.A., et. al.: Combined optical and X-ray tomosynthesis breast imaging. Radiology 2010; 258: pp. 89-97.
26. Soliman H., Gunasekara A., Rycroft M., et. al.: Functional imaging using diffuse optical spectroscopy of neoadjuvant chemotherapy response in women with locally advanced breast cancer. Clin Cancer Res 2010; 16: pp. 2605-2614.
27. Falou O., Soliman H., Sadeghi-Naini A., et. al.: Diffuse optical spectroscopy evaluation of treatment response in women with locally advanced breast cancer receiving neoadjuvant chemotherapy. Transl Oncol 2012; 5: pp. 238-246.
28. Schaafsma B.E., van de Giessen M., Charehbili A., et. al.: Optical mammography using diffuse optical spectroscopy for monitoring tumor response to neoadjuvant chemotherapy in women with locally advanced breast cancer. Clin Cancer Res 2015; 21: pp. 577-584.
29. Rinneberg H., Grosenick D., Moesta K.T., et. al.: Detection and characterization of breast tumours by time-domain scanning optical mammography. Opto-Electron Rev 2008; 16: pp. 147-162.
30. Choe R., Corlu A., Lee K., et. al.: Diffuse optical tomography of breast cancer during neoadjuvant chemotherapy: a case study with comparison to MRI. Med Phys 2005; 32: pp. 1128-1139.
31. Tromberg B.J., Cerussi A., Shah N., et. al.: Imaging in breast cancer: diffuse optics in breast cancer: detecting tumors in pre-menopausal women and monitoring neoadjuvant chemotherapy. Breast Cancer Res 2005; 7: pp. 279-285.
32. Zhou C., Choe R., Shah N., et. al.: Diffuse optical monitoring of blood flow and oxygenation in human breast cancer during early stages of neoadjuvant chemotherapy. J Biomed Opt 2007; 12: pp. 51903.
33. Tran W.T., Childs C., Chin L., et. al.: Multiparametric monitoring of chemotherapy treatment response in locally advanced breast cancer using quantitative ultrasound and diffuse optical spectroscopy. Oncotarget 2016; 7: pp. 19762-19780.
34. Pakalniskis M.G., Wells W.A., Schwab M.C., et. al.: Tumor angiogenesis change estimated by using diffuse optical spectroscopic tomography: demonstrated correlation in women undergoing neoadjuvant chemotherapy for invasive breast cancer?. Radiology 2011; 259: pp. 365-374.
35. Busch D.R., Choe R., Rosen M.A., et. al.: Optical malignancy parameters for monitoring progression of breast cancer neoadjuvant chemotherapy. Biomed Opt Express 2013; 4: pp. 105-121.
36. Gunther J.E., Lim E., Kim H.K., et. al.: Combined dynamic and static optical tomography for prediction of treatment outcome in breast cancer patients. Proc SPIE 2015; 9538: 953811
37. Tromberg B.J., Zhang Z., Leproux A., et. al.: Predicting responses to neoadjuvant chemotherapy in breast cancer: ACRIN 6691 trial of diffuse optical spectroscopic imaging. Cancer Res 2016; 76: pp. 5933-5944.
38. Ueda S., Roblyer D., Cerussi A., et. al.: Baseline tumor oxygen saturation correlates with a pathologic complete response in breast cancer patients undergoing neoadjuvant chemotherapy. Cancer Res 2012; 72: pp. 4318-4328.
39. Zhu Q., Wang L., Tannenbaum S., et. al.: Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria. Breast Cancer Res 2014; 16: pp. 456.
40. Sajjadi A.Y., Isakoff S.J., Deng B., et. al.: Normalization of compression-induced hemodynamics in patients responding to neoadjuvant chemotherapy monitored by dynamic tomographic optical breast imaging (DTOBI). Biomed Opt Express 2017; 8: pp. 555-569.
41. Cerussi A.E., Tanamai V.W., Mehta R.S., et. al.: Frequent optical imaging during breast cancer neoadjuvant chemotherapy reveals dynamic tumor physiology in an individual patient. Acad Radiol 2010; 17: pp. 1031-1039.
42. Anderson P.G., Kainerstorfer J.M., Sassaroli A., et. al.: Broadband optical mammography: chromophore concentration and hemoglobin saturation contrast in breast cancer. PLoS ONE 2015; 10: pp. e0117322.
43. Ogston K.N., Miller I.D., Payne S., et. al.: A new histological grading system to assess response of breast cancers to primary chemotherapy: prognostic significance and survival. Breast 2003; 12: pp. 320-327.
44. Rodenhuis S., Mandjes I.A., Wesseling J., et. al.: A simple system for grading the response of breast cancer to neoadjuvant chemotherapy. Ann Oncol 2010; 21: pp. 481-487.
45. Contini D., Martelli F., Zaccanti G.: Photon migration through a turbid slab described by a model based on diffusion approximation. I. Theory. Appl Opt 1997; 36: pp. 4587.
46. Anderson P.G., Sassaroli A., Kainerstorfer J.M., et. al.: Broadband optical mammography: breast tissue thickness compensation algorithm. Proc SPIE 2015; 9319: pp. 93190L.
47. Madsen K., Nielsen H., Tingleff O.: Methods for Non-Linear Least Squares Problems.2004.
48. Prahl S.: Optical absorption of hemoglobin.2002.Oregon Med. Laser CenterPortland, OR Available at: http://omlc.org/spectra/hemoglobin/summary.html Accessed May 17, 2017
49. Arridge S.R., Lionheart W.R.B.: Nonuniqueness in diffusion-based optical tomography. Opt Lett 1998; 23: pp. 882.
50. Groopman J.E., Itri L.M.: Chemotherapy-induced anemia in adults: incidence and treatment. J Natl Cancer Inst 1999; 91: pp. 1616-1634.
51. O’Sullivan T.D., Leproux A., Chen J.-H., et. al.: Optical imaging correlates with magnetic resonance imaging breast density and reveals composition changes during neoadjuvant chemotherapy. Breast Cancer Res 2013; 15: pp. R14.
52. Carraresi S., Shatir T.S., Martelli F., et. al.: Accuracy of a perturbation model to predict the effect of scattering and absorbing inhomogeneities on photon migration. Appl Opt 2001; 40: pp. 4622-4632.
53. Duch J., Fuster D., Muñoz M., et. al.: 18F-FDG PET/CT for early prediction of response to neoadjuvant chemotherapy in breast cancer. Eur J Nucl Med Mol Imaging 2009; 36: pp. 1551-1557.
54. Delille J., Slanetz P., Yeh E., et. al.: Invasive ductal breast carcinoma response to neoadjuvant chemotherapy: noninvasive monitoring with functional MR imaging—pilot study 1. Radiology 2003; 228: pp. 63-69.
55. Pickles M.D., Lowry M., Manton D.J., et. al.: Role of dynamic contrast enhanced MRI in monitoring early response of locally advanced breast cancer to neoadjuvant chemotherapy. Breast Cancer Res Treat 2005; 91: pp. 1-10.
56. Hayes C., Padhani A.R., Leach M.O.: Assessing changes in tumour vascular function using dynamic contrast-enhanced magnetic resonance imaging. NMR Biomed 2002; 15: pp. 154-163.
57. Mankoff D.A., Dunnwald L.K., Gralow J.R., et. al.: Changes in blood flow and metabolism in locally advanced breast cancer treated with neoadjuvant chemotherapy. J Nucl Med 2003; 44: pp. 1806-1814.
58. von Minckwitz G., Blohmer J.U., Costa S.D., et. al.: Response-guided neoadjuvant chemotherapy for breast cancer. J Clin Oncol 2013; 31: pp. 3623-3630.

Optical Mammography in Patients with Breast Cancer Undergoing Neoadjuvant Chemotherapy

Rationale and Objectives

Materials and Methods

Results

Conclusions

Introduction

Neoadjuvant Chemotherapy (NAC)

Imaging Modalities Under Investigation to Monitor Therapy Response

Research Plan for This Study

Materials and Methods

Optical Imaging of Patients with Breast Cancer

Laboratory Parameters and Response Categories

Data Processing

Statistical Analysis

Results

Patient Measurements

Cumulative Response Index (CRI)

Discussion

[HbT] Response to NAC at the Tumor ROI

SO 2 Response to NAC at the Tumor ROI

Limitations of the Study and Future Directions

Conclusions

Acknowledgments

References

Further Reading

A Diagnostic Accuracy Meta-analysis of CT and MRI for the Evaluation of Small Bowel Crohn Disease

Andragogic Approaches to Continuing Medical Education

Applying Quantitative CT Image Feature Analysis to Predict Response of Ovarian Cancer Patients to Chemotherapy