Changes in Primary Breast Cancer Heterogeneity May Augment Midtreatment MR Imaging Assessment of Response to Neoadjuvant Chemotherapy

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100 radiology.rsna.org n Radiology: Volume 272: Number 1—July 2014

changes in Primary Breast cancer heterogeneity May augment Midtreatment Mr imaging assessment of response to neoadjuvant chemotherapy1

Jyoti Parikh, FRCRMariyah Selmi, BScGeoff Charles-Edwards, PhDJennifer Glendenning, FRCRBalaji Ganeshan, PhDHema Verma, FRCRJanine Mansi, MDMark Harries, PhD, MRCPAndrew Tutt, FRCRVicky Goh, MD, FRCR

Purpose: To evaluate whether changes in magnetic resonance (MR) imaging heterogeneity may aid assessment for pathologic complete response (pCR) to neoadjuvant chemotherapy (NACT) in primary breast cancer and to compare pCR with standard Response Evaluation Criteria in Solid Tu-mors response.

Materials and Methods:

Institutional review board approval, with waiver of in-formed consent, was obtained for this retrospective analysis of 36 consecutive female patients, with unilateral unifocal primary breast cancer larger than 2 cm in diam-eter who were receiving sequential anthracycline-taxane NACT between October 2008 and October 2012. T2- and T1-weighted dynamic contrast material–enhanced MR im-aging was performed before, at midtreatment (after three cycles), and after NACT. Changes in tumor entropy (irreg-ularity) and uniformity (gray-level distribution) were de-termined before and after MR image filtration (for differ-ent-sized features). Entropy and uniformity for pathologic complete responders and nonresponders were compared by using the Mann-Whitney U test and receiver operating characteristic analysis.

Results: With NACT, there was an increase in uniformity and a decrease in entropy on T2-weighted and contrast-en-hanced subtracted T1-weighted MR images for all filters (uniformity: 23.45% and 22.62%; entropy: 219.15% and 219.26%, respectively). There were eight complete path-ologic responders. An area under the curve of 0.84 for T2-weighted MR imaging entropy and uniformity (P = .004 and .003) and 0.66 for size (P = .183) for pCR was found, giving a sensitivity and specificity of 87.5% and 82.1% for entropy and 87.5% and 78.6% for uniformity compared with 50% and 82.1%, respectively, for tumor size change for association with pCR.

Conclusion: Tumors become more homogeneous with treatment. An increase in T2-weighted MR imaging uniformity and a decrease in T2-weighted MR imaging entropy following NACT may provide an earlier indication of pCR than tu-mor size change.

q RSNA, 2014

1 From the Departments of Radiology (J.P., H.V., V.G.), Clinical Oncology (J.G., A.T.), and Medical Oncology (J.M., M.H.), Guys and St Thomas’ Hospitals NHS Foundation Trust, Westminster Bridge Road, London SE1 7EH, England; Division of Imaging Sciences and Biomedical Engineering, King’s College, London, England (M.S., G.C., V.G.); and Institute of Nuclear Medicine, University College London, London, England (B.G.). Received March 20, 2013; revision requested April 23; final revision received September 22; accepted November 11; final version accepted January 20, 2014. Supported by the Department of Health via the National Institute for Health Research Comprehensive Biomedical Research Centre Award to Guy’s & St Thomas’ NHS Foundation Trust in partnership with King’s College London and King’s College Hospital NHS Foundation Trust; and King’s College London and UCL Comprehensive Cancer Imaging Centre, funded by the Cancer Research United Kingdom and Engineering and Physical Sciences Research Council in association with the Medical Research Council and Department of Health. Address correspondence to J.P. (e-mail: [email protected]).

q RSNA, 2014

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Radiology: Volume 272: Number 1—July 2014 n radiology.rsna.org 101

BREAST IMAGING: Midtreatment Breast Cancer Changes Augment MR Response Assessment Parikh et al

potentially facilitating a more individ-ualized assessment and therapy adap-tation through sequential NACT and surgery.

While morphologic MR imaging has a sensitivity of up to 100% in determining disease extent of inva-sive ductal carcinomas before NACT, it becomes less sensitive during and following NACT (21–24). There has been increasing interest into adjunctive approaches with the aim of permitting earlier imaging response evaluation. Assessment of changes in MR imaging tumor heterogeneity is one such ap-proach. Postprocessing algorithms that may quantify the distribution of pixel signal intensity, most commonly with the use of statistics-based or model-based methods, can be applied to stan-dard MR imaging (25,26). Previous MR imaging studies in the treatment setting have suggested that measures of MR imaging heterogeneity may aid response assessment during treatment for non-Hodgkin lymphoma (27) and that pretreatment features may be as-sociated with response in soft-tissue sarcomas (28) and colorectal liver me-tastases (29).

On-treatment monitoring is un-dertaken with clinical examination prior to each cycle, and objective re-sponse assessment, with imaging at baseline, at the midpoint of sequen-tial chemotherapy, and prior to de-finitive surgery. Magnetic resonance (MR) imaging is regarded as the ref-erence standard imaging modality for breast cancer and has been shown to have better diagnostic accuracy than clinical breast examination and con-ventional imaging (mammography and breast ultrasonography [US]) because of greater lesion-to-background en-hancement on dynamic contrast mate-rial–enhanced T1-weighted sequences (5–9). More important, on-treatment monitoring with the use of MR imag-ing correlates significantly better with pathologic response than clinical as-sessment and conventional imaging (10–18).

NACT regimens typically comprise six to eight cycles of sequential an-thracycline-taxane–based sequential chemotherapy, with the addition of human epidermal growth factor re-ceptor 2 (HER2) targeted therapy in HER2-positive disease. Pathologic re-sponse rates are consistently reported to be greatest in triple-negative and HER2-positive breast cancer or mo-lecularly defined basal-like and erbB2 subgroups; however, there is overlap between features predictive of subse-quent good response and those pre-dictive of progression (19,20). Con-sequently, a proportion of patients may experience therapy-associated side effects and cost to the health service but without benefit. Strategies that seek to improve on-treatment re-sponse evaluation, permitting rapid differentiation between responders and nonresponders, are attractive,

Neoadjuvant chemotherapy (NACT) is commonly recommended for large operable or locally ad-

vanced breast cancers, with the aim of facilitating more conservative sur-gery and conferring at least the same magnitude of survival as therapy given in the adjuvant setting (1,2). Although NACT has not been shown to offer a survival advantage compared with se-quential adjuvant chemotherapy (3,4), it permits in vivo assessment of tumor chemotherapy sensitivity. Further-more, the absence of residual invasive cancer within the breast or axilla at definitive surgery (pathologic com-plete response [pCR]) is an indepen-dent predictor of better disease-free survival.

Implication for Patient Care

n Additional assessment of tumor heterogeneity during treatment may augment size change and, in the future, may facilitate a change in treatment (eg, switch-ing to alternative therapies or proceeding to earlier surgery).

Advances in Knowledge

n During neoadjuvant chemo-therapy, breast tumors become more homogeneous, with an increase in uniformity and a decrease in entropy on T2-weighted and contrast-enhanced subtracted T1-weighted MR images for all filters (mean percentage change for unifor-mity: 23.45% and 22.62%; en-tropy: 219.15% and 219.26%, respectively).

n This effect is greater in patho-logic responders compared with nonresponders and may be seen following three cycles of chemo-therapy (on T2-weighted MR images, entropy was 2333.97 in responders vs 2172.39 in nonre-sponders; uniformity was 51.64 in responders vs 25.54 in nonresponders).

n Percentage change in entropy and uniformity on T2-weighted MR images during treatment is better for assessing pathologic complete response to treatment than size change at the same time at midtreatment, with an area under the curve of 0.84 for en-tropy and uniformity (P = .003 and P = .004, respectively) vs 0.66 (P = .183) for longest di-mension of the tumor.

Published online before print10.1148/radiol.14130569 Content code:

Radiology 2014; 272:100–112

Abbreviations:AUC = area under the ROC curveHER2 = human epidermal growth factor receptor 2LD = longest dimensionNACT = neoadjuvant chemotherapypCR = pathologic complete responsepPR = pathologic partial responserCR = radiologic complete responserPR = radiologic partial responseROC = receiver operating characteristic

Author contributions:Guarantors of integrity of entire study, J.P., M.S., G.C., V.G.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; approval of final version of submitted manuscript, all authors; literature research, J.P., M.S., J.G., V.G.; clinical studies, J.P., M.S., G.C., J.M., M.H., A.T.; experimental studies, M.S.; statistical analysis, J.P., M.S., G.C., B.G.; and manuscript editing, J.P., M.S., G.C., B.G., H.V., J.M., M.H., A.T., V.G.

Conflicts of interest are listed at the end of this article.



sequence was suboptimal (suboptimal delivery due to extravasated intravenous contrast agent and delayed imaging due to vomiting). In these patients, only the T2-weighted sequences were used in the analysis.

Neoadjuvant TreatmentNACT was administered as an an-thracycline-taxane regimen (a) either as fluorouracil (Accord Healthcare, Middlesex, United Kingdom) (500 mg/m2), epirubicin (Onco-Tain; Hospira UK, Warwickshire, United Kingdom) (100 mg/m2), and cyclophosphamide (Baxter Healthcare, Berkshire, United Kingdom) (500 mg/m2) given intra-venously as one cycle every 3 weeks for three cycles, followed by docetaxel (Taxceus; Medac, Wedel, Germany) (100 mg/m2) given intravenously every 3 weeks for three cycles, (b) or as epi-rubicin (90 mg/m2) and cyclophospha-mide (600 mg/m2) given intravenously as one cycle every 3 weeks for four cycles, followed by docetaxel (100 mg/m2) given intravenously as one cycle every 3 weeks for four cycles. From 2010 onward, patients who were HER2 receptor–positive individuals also re-ceived trastuzumab (Herceptin; Roche Products, Welwyn Garden City, United Kingdom) (8 mg/kg loading dose, 6 mg/kg maintenance dose, given intra-venously as one cycle every 3 weeks to-talling 18 cycles over a year) commenc-ing at first taxane cycle and continuing into the adjuvant setting. Patients re-ceived fluorouracil, epirubicin-cyclo-phosphamide followed by docetaxel (n = 7), epirubicin-cyclophosphamide fol-lowed by docetaxel (n = 29); of those receiving docetaxel, six patients re-ceived trastuzumab concomitant with the docetaxel component (n = 6).

MR ImagingDynamic contrast-enhanced MR imaging was performed by using a 1.5-T system (Avanto or Aera; Siemens Healthcare, Erlangen, Germany) with a dedicated 4- or 16-channel breast coil in the prone position. Standard diagnostic T2-weight-ed and dynamic contrast-enhanced T1-weighted sequences were per-formed (Table 1).

study. All other authors had control of the data and information submitted for publication.

PatientsInstitutional review board approval and waiver of informed consent were ob-tained for this retrospective analysis. Pa-tients with locally advanced breast cancer who underwent NACT between October 1, 2008, and September 30, 2012, were eligible and were identified from our institutional database. Patients were in-cluded who had unilateral focal solid masslike tumors larger than 2 cm in max-imum tumor LD without central necrosis and had undergone MR imaging at base-line, at midtreatment (after three cycles of chemotherapy), and after treatment prior to surgery. Patients were excluded if they had the following: (a) nonmasslike tumors; (b) cystic tumors; (c) contraindi-cation to contrast-enhanced MR imaging, including previous gadolinium-based con-trast agent hypersensitivity reaction or renal impairment with estimated glomer-ular filtration rate lower than 60 mL/min; (d) pregnancy and ferromagnetic pros-theses; or (e) suboptimal imaging results (eg, images with significant susceptibility artifact from the breast localization coil or motion artifact).

One hundred nine patients were identified; of this number, 73 were ex-cluded: 34 had nonmasslike, infiltrative, diffuse, or smaller than 2-cm tumors; 18 had multifocal disease; two had bilat-eral tumors; six had cystic tumors; two had images with susceptibility artifact from the titanium MR imaging localiza-tion coil (MReye localization coil; Cook Medical, Bloomington, Ind) precluding accurate tumor measurement; and eight had a complete radiologic response by midtreatment MR imaging so tumor het-erogeneity midtreatment could not be assessed. Three patients were also ex-cluded as there was no histopathologic information available (Fig 1). The final study population comprised 36 female patients (mean age, 49.8 years; range, 24–67 years) who underwent MR im-aging at these three times, totalling 108 MR imaging examinations. In two of these 36 patients, the pretreatment T1-weighted contrast-enhanced MR imaging

In previous mammography and US studies in which the researchers have assessed tumor texture in the breast, the investigators have focused on the detection and differentiation of ma-lignant and benign lesions. Texture analysis has been used to look at the differentiation of glandular and fatty breast tissue (30); to distinguish malig-nant masses from normal breast tissue (31,32), benign lesions (33), and inva-sive versus in situ disease; and to assess the relationship with estrogen receptor status (34).

Similarly, with MR imaging, texture analysis has been used to detect micro-calcification (35), to differentiate be-tween benign and malignant lesions (36–39), and to distinguish between invasive ductal and invasive lobular carcinomas (40). Model-based fractal methods ap-plied to MR imaging have shown that fractal dimension is related to estrogen receptor–progesterone receptor status (41), may help differentiate low- and high-grade tumors (42), and may pre-dict response to treatment (43). The researchers in a recent study (26) as-sessing texture features using software based on co-occurrence matrices have shown a difference between responders and nonresponders (defined by percent-age change in maximum tumor longest dimension [LD] after NACT), most evi-dent at 1–3 minutes after contrast agent administration; however, it should be noted that no correction was applied for multiple testing.

To our knowledge, researchers in no studies have assessed whether on-treatment changes after three cycles of NACT may be of clinical benefit.

The aim of our study was to eval-uate whether changes in MR imaging heterogeneity may aid assessment for pCR to NACT in primary breast cancer and to compare pCR with the standard Response Evaluation Criteria in Solid Tumors response.

Materials and Methods

One author (B.G.) is Scientific Direc-tor of TexRAD (University of Sussex, Falmer, Sussex, England), which pro-vided the software for analysis in this



region of interest; higher entropy and lower uniformity represent greater heterogeneity (29).

Images in all patients were fil-tered to highlight different-sized fea-tures by focusing on different pixel sizes ranging from 2 to 12 pixels (100–550 mm) for the five filter levels chosen (0.5, 1.0, 1.5, 2.0, 2.5). Filters 0.5–1.0 highlight fine-texture features (2 and 4 pixels), filters 1.5–2.0 highlight medium-texture features (6 and 10 pixels), and a filter of 2.5 highlights coarse-texture features (12 pixels) (Table 2). The effect of filtra-tion, by highlighting larger pixels, has been hypothesized to accentuate the contribution of the vasculature to tex-ture features.

Assessment of Changes in MR Imaging Morphologic AppearancesT2-weighted and T1-weighted unen-hanced and contrast-enhanced morpho-logic appearances of the tumors were scored qualitatively by using a three-point scale, where score 1 is heteroge-neous signal intensity; score 2, slightly heterogeneous and homogeneous signal intensity; and score 3, homogeneous signal intensity. Note was also made of areas of necrosis or fibrosis and chang-es, if any, during treatment.

Response AssessmentTumor size was recorded as the maxi-mum LD in the T2-weighted sequence and the T1-weighted contrast-enhanced subtracted second dynamic contrast-enhanced phase (3 minutes after the injection of contrast agent) for base-line, midtreatment, and posttreatment MR imaging. Where tumor size differed in these two sequences, the longer of the two measurements was used for data analysis. Changes in LD and re-sponse were assessed by using stan-dard Response Evaluation Criteria in Solid Tumors, version 1.1 criteria (44), as this is the usual practice at our in-stitution (ie, radiologic response after completion of treatment was defined as follows: radiologic complete response [rCR], 100% reduction, and radiologic partial response [rPR], 30% reduc-tion, in the maximum tumor LD on the

Imaging and Communications in Medi-cine image of the tumor at its maximum LD was processed using a propriety soft-ware program (TexRAD, University of Sussex). A region of interest was drawn around the whole visible tumor by two investigators in consensus (J.P., a breast radiologist, and M.S., a research student with . 1 year of experience in breast MR imaging), taking care to avoid the MR imaging localization marker, where present (Fig 2), on the section showing the maximum tumor LD. First-order en-tropy, or E, reflecting irregularity, and uniformity, or U, reflecting distribution of gray level, were derived as defined by the following equations:

[ ] [ ]21

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Image AnalysisImage interpretation and evaluation were performed at a workstation (MMWP; Siemens Healthcare) by a breast radiologist (J.P., with more than 8 years of experience in breast MR im-aging). Tumor size was recorded as the LD in the T2-weighted sequence and the T1-weighted contrast-enhanced subtracted second dynamic contrast-enhanced phase (3 minutes after the injection of contrast agent). We used standard Response Evaluation Criteria in Solid Tumors measurements, as this is the usual practice at our institution. Tumor volume measurement was not possible with the software used.

Assessment of MR Imaging HeterogeneityAnalysis was performed for all three assessment times (baseline, midtreat-ment, and after treatment) for the T2-weighted MR images in 36 patients (108 T2-weighted MR examinations in total); in 34 of 36 patients, the T1-weighted dynamic contrast-enhanced subtracted MR images were analyzed due to sub-optimal enhancement in two patients (102 T1-weighted MR examinations in total). The T2-weighted and T1-weight-ed contrast-enhanced subtracted Digital

Figure 1

Figure 1: Flowchart of study population with exclusion criteria. ceMRI = contrast-enhanced MR imaging.



posttreatment MR image compared with the pretreatment MR image).

The pCR was defined as absence of any residual invasive cancer in the resected breast specimen and all sam-pled ipsilateral nodes. Histopathologic analysis was performed as per standard practice for our institution, a breast cancer tertiary referral center, by our four institutional breast histopatholo-gists, each with more than 7 years of experience.

Statistical AnalysisThe two-tailed Mann-Whitney U test was used to assess whether there was any relationship between the morpho-logic T2-weighted and T1-weighted contrast-enhanced appearances of the tumors for the baseline to midtreat-ment MR images and pretreatment to posttreatment MR images, respec-tively, and pathologic responders and nonresponders.

Percentage change in pretreat-ment to midtreatment MR imaging entropy and uniformity and pretreat-ment to posttreatment MR imag-ing entropy and uniformity between pathologic complete responders and nonresponders and radiologic com-plete responders/nonresponders were compared by using a two-tailed Mann-Whitney U test. The relationship between midtreatment changes in entropy and uniformity and midtreat-ment change in tumor size, as well as Response Evaluation Criteria in Solid Tumors after three cycles in assessing pCR and pathologic partial response (pPR), were evaluated and compared by using receiver operating character-istic (ROC) analysis. The point on the ROC curve farthest from the line of no discrimination was considered the optimum threshold for the association with complete response, and sensitiv-ity and specificity were determined. To correct for the multiple parameters assessed, a Bonferroni correction was applied, giving a P value of .003 or less as considered to represent a sig-nificant difference. Statistical analysis was performed by using software (SPSS Statistics, version 18; IBM, Ar-monk, NY).Ta

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Results

PatientsThirty-five patients had invasive ductal carcinomas of no special type, and one patient had invasive lobular carcinoma. Tumor characteristics of our study population are described in Table 3. Patients underwent breast-conserving surgery (n = 22) or mastectomy (n = 14). In one patient, mastectomy was performed because of patient choice; three patients were BRCA carriers and underwent bilateral mastectomies. Re-sidual disease, found in 28 patients,

Figure 2

Figure 2: (a–d) Axial T1-weighted contrast-enhanced dynamic subtracted MR images show texture analysis in a 31-year-old female patient with a 58-mm retroareolar tumor in the right breast before NACT. (a) Without fusion. Line shows LD. (b) Fused with a fine filter (0.5–1.0). (c) Fused with a medium filter (1.5–2.0). (d) Fused with a coarse filter (2.5). (e) Screenshot of texture analysis from software (TexRAD) provided by manufacturer. Texture feature spectrum, highlighting filtered texture quantifiers. Intensity = signal intensity.(Reprinted, with permission waived, from TexRAD.)

Table 2

Filter Sigma Values, Texture Type, and Corresponding Width of the Filter

Filter Sigma Value

Texture Type

Approximate Filter Width

No. of Pixels Size (µm)

No filter None 0 00.5 Fine 2 1001.0 Fine 4 2001.5 Medium 6 3002.0 Medium 10 4502.5 Coarse 12 550

was invasive carcinoma alone (n = 14), invasive carcinoma with in situ carci-noma (n = 12), or in situ carcinoma alone (n = 2).

Mean pretreatment MR tumor size was 44.9 mm (range, 22–105 mm). Mean midtreatment tumor size was 30.8 mm (range, 13–78 mm). Mean posttreatment MR tumor size was 19.1 mm (range, 0–70 mm), with a mean percentage change of 256.2% (range, 240.0% to 2100%) following NACT. Mean pathologic tumor size was 21.6 mm (range, 0–70 mm). The numbers of responders and nonresponders are



Changes in MR Imaging Morphologic Appearances after NACTNo correlation was demonstrated be-tween the morphologic changes in tu-mor signal intensity on T2-weighted and T1-weighted contrast-enhanced MR images and response for each of the response criteria assessed. One patient developed a slightly more cystic and/or necrotic tumor, as shown at T2-weighted MR imaging midtreatment, which then appeared more homoge-neous after treatment. No patients developed substantial areas of fibrosis during the time of treatment.

Assessment of Pathologic Response: Complete and Partial ResponseFor a pCR, the area under the ROC curve (AUC) for percentage change in T2-weighted MR imaging entropy and uniformity (filter levels of 1.5 and 2.0) after three chemotherapy cycles was an AUC of 0.84 for both entropy and uniformity. These AUCs were higher than tumor size change for assess-ing pCR: AUC of 0.66 (Table 7). For a pPR, the AUCs for T2-weighted MR imaging entropy, uniformity, and tumor size were similar and were 0.74, 0.78, and 0.76, respectively. The sensitivity and specificity, respectively, of tex-ture features for association with pCR and pPR are summarized in Table 8. On T2-weighted MR images, for pCR, sensitivity and specificity were 87.5% and 82.1% for entropy (threshold of 51.77) and 87.5% and 78.6% for uniformity (threshold of 25.67) com-pared with 50% and 82.1% for change in tumor size (threshold of 45.4), re-spectively. On T2-weighted MR images, for pPR, sensitivity and specificity were 60.0% and 72.7% for entropy (thresh-old of 37.62) and 60.0% and 81.8% for uniformity (threshold of 13.07) compared with 92.0% and 72.7% for change in tumor size (threshold of 12.13), respectively.

DiscussionOur data indicate that NACT decreases MR imaging heterogeneity during and after treatment that is adjunctive to standard morphologic assessment.

for both T2-weighted and T1-weight-ed dynamic contrast-enhanced sub-tracted MR images (Table 5). This pattern was stronger in responders com-pared with nonresponders (Fig 3). On T2-weighted MR images, in pathologic responders compared with pathologic nonresponders following three cycles of chemotherapy, entropy was −333.97 in responders versus −172.39 in nonre-sponders; uniformity was 51.64 in re-sponders versus 25.54 in nonresponders. Percentage change in entropy and uni-formity are summarized in Table 6. On T2-weighted and T1-weighted dy-namic contrast-enhanced MR images, mean percentage change for uniformity was 23.45% and 22.62%, and mean per-centage change for entropy was −19.15% and −19.26%, respectively. Changes in T2-weighted MR imaging entropy and uniformity at midtreatment after three cycles of NACT were better for assessing pCR than changes in entropy and unifor-mity at the end of treatment following six cycles of NACT for T2-weighted MR imaging but not for dynamic contrast-enhanced T1-weighted MR imaging. The only value at the midtreatment assessment time that showed a signifi-cant difference at P less than .003 was T2-weighted MR imaging entropy at a filter level of 1.5 (300-mm width). En-tropy and uniformity were better for assessing complete responders only at the midcycle time compared with size estimates. At the completion of NACT, size performed significantly better than either entropy or uniformity.

summarized in Table 4. Of the five pa-tients who were responders across all response criteria, four were estrogen-receptor negative and HER2-receptor negative and one was estrogen-recep-tor weakly positive (Allred score 2 of 8) and HER2-receptor positive.

Changes in MR Imaging Heterogeneity after NACTEntropy decreased and uniformity in-creased across all filters following NACT, confirming that tumors become more homogeneous during treatment

Table 3

Summary of Tumor Characteristics in 36 Patients

CharacteristicNo. of Patients

Side of tumor Right breast 19 (53) Left breast 17 (47)Location of tumor on MR image Upper outer quadrant, lateral 13 (36) Upper inner quadrant, medial 4 (11) Lower inner quadrant, medial 1 (3) Lower outer quadrant, lateral 4 (11) Retroareolar 11 (31) Posterior, on or close to the

chest wall3 (8)

Tumor type Invasive ductal arcinoma, no

special type35 (97)

Invasive lobular carcinoma 1 (3)Tumor grade 1 3 (8) 2 16 (44) 3 17 (47)Tumor receptor status* Estrogen receptor Strongly positive 16 (44) Weakly positive 5 (14) Negative 14 (39) HER2 receptor Positive 6 (17) Negative 29 (81)Tumor morphologic feature Well-defined mass 19 (53) Ill-defined mass 11 (31) Spiculate mass 6 (17)

Note.—Numbers in parentheses are percentages. Percentages were rounded.

* Receptor status was unknown in one patient.

Table 4

Response Categorization in 36 Patients Following Completion of NACT

Group

Response Criteria Assessed

pCR pPR rCR rPR

Responders 8 (22) 25 (69) 7 (19) 28 (78)Nonresponders 28 (78) 11 (31) 29 (81) 8 (22)

Note.—Data are numbers of patients. Numbers in parentheses are percentages. Percentages were rounded.



Tabl

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ity

Base

line

Afte

r 3 T

reat

men

t Cyc

les

Perc

enta

ge C

hang

eBa

selin

eAf

ter 3

Tre

atm

ent C

ycle

sPe

rcen

tage

Cha

nge

T2W

MR

imag

ing

No

filtr

atio

n8.

26 6

0.7

4 (6

.47,

9.5

4)7.

80 6

0.7

4 (5

.51,

9.5

9)2

5.29

6 8

.06

(221

.48,

11.

76)

0.00

46 6

0.0

030

(0.0

017,

0.0

142)

0.00

60 6

0.0

040

(0.0

015,

0.0

239)

46.9

3 6

67.

58

(2

50.2

7, 2

57.1

9)

0.5

4.01

6 0

.63

(2.1

0, 5

.27)

3.50

6 0

.72

(2.0

9, 5

.28)

212

.00

6 1

6.54

(244

.86,

30.

59)

0.38

6 0

.06

(0.2

7, 0

.61)

0.41

6 0

.08

(0.2

6, 0

.61)

10.9

7 6

19.

16

(2

21.0

4, 6

1.25

)

1.0

3.26

6 0

.71

(1.5

2, 4

.70)

2.42

6 0

.82

(1.0

8, 4

.21)

223

.93

6 2

4.74

(268

.37,

23.

12)

0.49

6 0

.08

(0.3

4, 0

.70)

0.58

6 0

.11

(0.4

0, 0

.79)

21.7

6 6

26.

87

(2

14.9

0, 9

8.73

)

1.5

2.45

6 0

.83

(0.8

5, 3

.93)

1.52

6 0

.84

(0.3

4, 3

.46)

233

.79

6 3

3.01

(288

.44,

31.

80)

0.61

6 0

.11

(0.4

3, 0

.83)

0.74

6 0

.12

(0.4

7, 0

.91)

23.5

9 6

26.

99

(2

13.8

9, 9

4.00

)

2.0

1.86

6 0

.91

(0.6

2, 3

.56)

1.03

6 0

.71

(0, 2

.91)

234

.52

6 4

1.13

(210

, 58.

78)

0.70

6 0

.13

(0.4

9, 0

.89)

0.82

6 0

.11

(0.5

5, 1

.00)

20.0

3 6

26.

11

(2

8.64

, 95.

35)

2.

51.

37 6

0.9

3 (0

.02,

3.4

2)0.

64 6

0.6

0 (0

, 2.4

7)2

5.34

6 2

06.0

6

(2

100,

114

2.37

)0.

78 6

0.1

4

(0

.50,

1.0

0)0.

89 6

0.0

9

(0

.63,

1.0

0)17

.44

6 2

4.63

(212

.90,

83.

89)

T1W

DCE

MR

imag

ing

No

filtr

atio

n8.

62 6

0.7

6 (7

.00,

9.8

7)8.

29 6

0.7

2 (6

.93,

9.7

3)2

3.68

6 5

.49

(215

.28,

10.

28)

0.00

36 6

0.0

022

(0.0

013,

0.0

104)

0.00

42 6

0.0

022

(0.0

013,

0.0

096)

29.8

3 6

44.

02

(2

47.5

2, 1

54.9

1)

0.5

4.91

6 0

.40

(4.0

6, 5

.63)

4.63

6 0

.46

(3.3

8, 5

.55)

25.

64 6

7.5

8

(2

25.8

7, 7

.08)

0.28

6 0

.026

(0.2

3, 0

.35)

0.29

6 0

.037

(0.2

3, 0

.42)

4.40

6 1

4.81

(220

.63,

48.

47)

1.

04.

22 6

0.6

3 (2

.41,

5.2

2)3.

54 6

0.7

8 (1

.48,

4.9

2)2

15.2

4 6

19.

02

(2

61.6

1, 3

1.02

)0.

37 6

0.0

7

(0

.28,

0.6

0)0.

44 6

0.0

8

(0

.30,

0.7

2)20

.16

6 2

4.00

(227

.92,

87.

60)

1.

53.

48 6

0.7

8 (1

.26,

4.7

2)2.

55 6

0.9

4 (0

.83,

4.2

4)2

25.7

8 6

24.

09

(2

72.4

5, 2

8.82

)0.

47 6

0.0

9

(0

.33,

0.7

8)0.

59 6

0.1

2

(0

.39,

0.8

4)26

.83

6 2

6.00

(223

.69,

69.

61)

2.

02.

82 6

0.9

2 (0

.64,

4.2

3)1.

78 6

0.9

9 (0

.10,

3.8

5)2

34.9

1 6

32.

45

(2

96.2

0, 3

9.82

)0.

57 6

0.1

2

(0

.40,

0.8

9)0.

71 6

0.1

4

(0

.45,

0.9

8)27

.80

6 2

4.98

(220

.14,

81.

30)

2.

52.

27 6

0.9

9 (0

.25,

3.9

6)1.

22 6

0.9

0 (0

.02,

3.2

0)2

30.3

3 6

80.

18

(2

98.7

6, 3

13.2

5)0.

65 6

0.1

4

(0

.45,

0.9

5)0.

80 6

0.1

4

0.

54, 1

.00)

26.7

1 6

25.

97

(2

22.4

2, 7

6.71

)

Note

.—Da

ta a

re m

eans

6 s

tand

ard

devi

atio

ns. N

umbe

rs in

par

enth

eses

are

95%

con

fiden

ce in

terv

als.

DCE

= d

ynam

ic c

ontra

st e

nhan

ced,

T1W

= T

1 w

eigh

ted,

T2W

= T

2 w

eigh

ted.



Intratumoral biological heteroge-neity may influence response to treat-ment, and assessment of changes in heterogeneity may provide additional information to that captured by stan-dard imaging assessment of size or en-hancement change. Anthracyclines and taxanes are cytotoxic chemotherapeutic agents that have different mechanisms of action: Anthracyclines damage DNA, while taxanes disrupt microtubules. However, they share commonality in affecting cycling tumor cells, resulting in cell kill. Previous histologic studies indicate that tumor cell morphology is altered as a consequence of chemo-therapy, with nuclear enlargement and vacuolation and cytoplasmic swelling. There may be a reduction in cell pro-liferation markers and an increase in apoptosis markers (49).

It has been postulated that re-sponse should be greater in tumors that are more heterogeneous because early cycles of chemotherapy treat areas of the tumor with a good blood supply and expose dormant or hypoxic areas for treatment with later cycles of che-motherapy (50).

With use of statistically based methods, the pixel signal intensity distribution may be quantified by the parameters entropy and uniformity. Entropy represents the disorder and randomness of gray levels: The higher the entropy, the greater the heteroge-neity within the tumor. Uniformity rep-resents the gray-level distribution and determines how close the values are to being uniform: The lower the unifor-mity, the greater the tumor heteroge-neity (51).

The performance of heterogeneity analysis was better for T2-weighted than T1-weighted subtracted dynamic contrast-enhanced MR imaging. This probably reflects the predominant un-derlying mechanism for signal intensity and mechanism of drug action. The sub-tracted T1-weighted MR images reflect the vascular leakage and distribution of contrast agent at 3 minutes after injec-tion in comparison with the T2-weighted MR images, where the signal intensity is derived predominantly from the tu-mor intracellular and extravascular

analyzed in the imaging component of the multicenter Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis, or the I-SPY-TRIAL, Ameri-can College of Radiology Imaging Net-work, ACRIN, 6657 (47), showed that measuring tumor LD at midtreatment (when switching from anthracycline-cyclophosphamide to taxane) gave an AUC of 0.68 for the 26% of patients in the study who showed a pCR. In our study, we found a similar AUC of 0.66 for assessing response to treatment by using tumor LD in the 22% of patients in our study who showed a pCR.

Earlier identification of patients who will derive inadequate benefit from treatment has the potential to better tailor therapy to the individual and fa-cilitate evaluation of therapeutics within high-risk subsets identified by their in vivo response. This factor has implica-tions for patients with tumor subtypes, such as triple-negative breast cancers, which are known to have a significantly worse overall survival outcome, if a pCR is not achieved following NACT (48).

Our data suggest that changes in T2-weighted MR imaging entropy and uniformity midtreatment, after three cycles, demonstrate greater sensitiv-ity for assessing pCR than change in tumor size after three cycles of treat-ment, with a higher AUC at ROC analysis. Midtreatment changes in entropy and uniformity, however, are not significantly better than change in tumor size for assessing a pPR.

NACT or primary chemotherapy is the standard treatment for locally advanced or large operable breast cancers in which the aim is for down-staging before definitive surgery: Ac-curate response assessment to NACT is important. The reduction in tumor enhancement in dynamic contrast-enhanced T1 sequences during and following neoadjuvant treatment, re-sulting in underestimation of resid-ual disease, has been proposed as a possible reason for the relatively lower sensitivity of MR imaging in prediction of pCR when tumor size is used to measure response, as stan-dard (45,46). Data from 216 patients

Figure 3

Figure 3: Graph depicts overall change in entropy and uniformity following NACT for pCR with a medium filter (filter, 1.0) at T2-weighted MR imaging. The differences between the responders and nonresponders was significant (P = .007–.01 for both entropy and uniformity). A similar pattern was seen for all response criteria at T2-weighted MR imaging and was most significant at filters of 1.0, 1.5, and 2.0.



Tabl

e 6

Med

ian

Valu

es a

nd L

owes

t P V

alue

s fr

om M

ann-

Whi

tney

U T

est f

or P

erce

ntag

e Ch

ange

in E

ntro

py a

nd U

nifo

rmity

bef

ore

to M

idtr

eatm

ent a

nd b

efor

e to

aft

er N

ACT

for

Resp

onde

rs a

nd N

onre

spon

ders

NACT

Tim

e, M

R Fe

atur

e, a

nd S

eque

nce

Path

olog

ic R

espo

nse*

Filte

r for

Bes

t Pa

thol

ogic

Re

spon

se

Radi

olog

ical

Res

pons

e*Fi

lter f

or B

est

Radi

olog

ic R

espo

nse

pCR

Non-

pCR

rCR

Non-

rCR

Befo

re to

mid

treat

men

t NAC

T

Entro

py

T2

W M

R2

66.4

4 (2

79.1

6 to

220

.20)

230

.28

(288

.44

to 3

1.79

)1.

52.

80 (2

8.14

to 2

.96)

27.

31 (2

21.4

8 to

11.

76)

No fi

lter

P va

lue

.003

. . .

. . .

.058

. . .

, , ,

T1W

DCE

MR

278

.40

(298

.76

to 2

41.7

7)2

42.5

6 (2

98.5

3 to

231

3.25

)2.

52

47.4

6 (2

72.7

5 to

215

.98)

233

.83

(296

.20

to 3

9.82

)2.

0

P

valu

e.0

64. .

.. .

..3

43. .

.. .

.

Unifo

rmity

T2W

MR

44.1

3 (1

6.93

–94.

00)

10.4

6 (2

13.8

9 to

93.

01)

1.5

217

.83

(221

.73

to 5

8.69

)55

.25

(250

.27

to 2

57.1

9)No

filte

r

P

valu

e.0

04. .

. . .

..0

44. .

.. .

.

T1

W D

CE M

R58

.11

(227

.22

to 1

44.4

4)26

.46

(247

.52

to 1

54.9

1)No

Filt

er2

0.93

(213

.07

to 4

8.47

)1.

82 (2

20.6

3 to

33.

55)

0.5

P va

lue

.12

. . .

. . .

.206

. . .

. . .

Tu

mor

siz

e44

.43

(4.1

7–71

.43)

26.4

8 (0

–74.

0)NA

35.5

8 (4

.17–

46.6

7)26

.87

(0–7

4.0)

NA

P va

lue

.183

. . .

. . .

.764

. . .

. . .

Befo

re to

afte

r NAC

T

Entro

py

T2

W M

R2

98.5

2 (2

261.

37 to

218

.94)

228

.62

(219

2.13

to 2

2.93

)1.

02

305.

44 (2

1038

.6 to

217

.5)

251

.53

(296

4.3

to 3

3.46

)1.

5

P

valu

e.0

07. .

.. .

..0

32. .

.. .

.

T1

W D

CE M

R 2

15.7

9 (2

18.8

3 to

23.

57)

28.

18 (2

38.3

9 to

5.6

0)No

filte

r2

47.7

4 (2

100.

0 to

0.1

3)2

40.5

3 (2

91.3

9 to

72.

13)

1.5

P va

lue

.18

. . .

. . .

.366

. . .

. . .

Un

iform

ity

T2

W M

R38

.51

(16.

10–8

6.96

)9.

66 (2

25.3

1 to

74.

28)

1.0

33.7

4 (8

.38–

93.4

1)14

.26

(226

.96

to 9

5.82

)1.

5

P

valu

e.0

1. .

.. .

. .1

56. .

.. .

.

T1

W D

CE M

R15

0.90

(16.

67–2

00.2

7)59

.60

(230

.03

to 6

47.7

4)No

filte

r62

.97

(23.

34 to

119

.56)

42.4

8 (2

20.0

2 to

83.

14)

1.5

P va

lue

.297

. . .

. . .

.391

. . .

. . .

Tu

mor

Siz

e10

0.0

(46.

90–1

00.0

)51

.55

(240

.0 to

100

.0)

NA10

0.0

(100

.0–1

00.0

)50

.8 (2

40.0

to 8

2.90

)NA

P

valu

e.0

01. .

.. .

.,

.001

. . .

. . .

Note

.—DC

E =

dyn

amic

con

trast

enh

ance

d, N

A =

not

ava

ilabl

e, T

1W =

T1

wei

ghte

d, T

2 =

T2

wei

ghte

d.

* Ex

cept

whe

re o

ther

wis

e in

dica

ted,

dat

a ar

e m

edia

ns. N

umbe

rs in

par

enth

eses

are

rang

es.



tumors, the majority of which were in-vasive ductal carcinomas. Future work should include a more representative clinical spectrum of morphologic and histopathologic subtypes to validate our findings. Assessment of response was made after three cycles of NACT, and earlier assessment, for example, after a single cycle of NACT could also be helpful. We limited analysis to the maximum axial diameter to minimize any potential effect of the localization marker. Potentially, this may not be en-tirely representative of a very heteroge-neous tumor. We used a dichotomous separation to define response; however, scoring systems incorporating degrees of partial response such as Residual Cancer Burden, which is independently prognostic across all breast cancer phe-notypes, may provide a more appropri-ate end point for evaluating treatment texture change (53). We also did not in-clude in our analysis eight patients who had an rCR to NACT after only three cycles, as there was no tumor to assess on their midtreatment MR images, and this factor may have introduced bias into our results.

In conclusion, midtreatment chang-es in breast cancer entropy and uni-formity in patients receiving NACT for breast cancer may better aid assess-ment for a pCR than tumor size change and may potentially provide additional information to standard MR imaging assessment at no additional burden to the patient.

Disclosures of Conflicts of Interest: J.P. No rel-evant conflicts of interest to disclose. M.S. No relevant conflicts of interest to disclose. G.C. No relevant conflicts of interest to disclose. J.G. No relevant conflicts of interest to disclose. B.G. Fi-nancial activities related to the present article: is the scientific director of TexRAD (University of Sussex, Falmer, Sussex, England) which pro-vided the software for analysis in this study. Financial activities not related to the present article: is not paid for board membership by TexRAD Ltd, is employed by TexRAD Ltd/Uni-versity College London, is not paid for patents pending (patent for TexRAD technique is owned by University of Sussex, United Kingdom) by TexRAD Ltd/University of Sussex and has not re-ceived any royalties; receives funds for travel for conferences and meetings if related to company work from TexRAD Ltd; holds shares in TexRAD Ltd. Other relationships: none to disclose. H.V. No relevant conflicts of interest to disclose. J.M.

Table 7

AUC for ROC Curves Comparing Percentage Change in Entropy and Uniformity after Three Cycles with Percentage Change in Tumor Size for Each Response

Feature

Response Assessment Criteria

pCR pPR rCR rPR

Tumor size AUC 0.66 0.76 0.54 0.76 95% CI 0.44, 0.87 0.56, 0.95 0.32, 0.75 0.56, 0.95Entropy AUC 0.84 0.74 0.62 0.70 95% CI 0.70, 0.99 0.56, 0.91 0.40, 0.84 0.52, 0.87Uniformity AUC 0.84 0.78 0.61 0.74 95% CI 0.71, 0.97 0.62, 0.093 0.40, 0.82 0.58, 0.90

Note.—CI = confidence interval.

Table 8

Sensitivity and Specificity for Tumor Size and Texture in Assessing Response

Feature

Response Assessment Criteria

pCR pPR rCR rPR

Tumor size Sensitivity 50.0 92.0 71.4 96.4 Specificity 82.1 72.7 58.6 87.5Entropy Sensitivity 87.5 60.0 57.1 78.6 Specificity 82.1 72.7 79.3 62.5Uniformity Sensitivity 87.5 60.0 57.1 78.6 Specificity 78.6 81.8 69.0 62.5

Note.—Data are percentages. Percentages were rounded.

extracellular space, which composes the bulk to any tumor. Although the performance of texture analysis was better for T2-weighted MR imaging, contrast-enhanced T1-weighted MR imaging would still need to be per-formed for diagnostic purposes in as-sessing tumor extent. There could be a potential role for texture analysis in augmenting T2-weighted MR imaging response assessment in patients who cannot receive gadolinium-based con-trast agents because of allergy or renal function, for example.

In our study we found that midtreat-ment changes in T2-weighted entropy and uniformity at medium-texture fea-tures after three cycles of chemother-apy correlated better with response than after completion of chemotherapy, suggesting that changes in tumor tex-ture occur earlier on in treatment than tumor size changes. More important, for a pCR, changes in texture features after three cycles were better for as-sessing response in comparison with tumor size. In our study, tumor size change had a sensitivity of 50.0% and specificity of 82.1%—a trend similar to the findings of a meta-analysis (52) of 25 studies involving 1212 patients look-ing at the accuracy of tumor size at MR imaging in prediction of pCR to neoad-juvant treatment; That meta-analysis showed pooled weighted estimates of sensitivity of 63% and specificity of 91%.

Our findings suggest that midtreat-ment changes in T2-weighted MR im-aging heterogeneity (entropy and uni-formity) are adjunctive to morphologic assessment and may be better than size change alone in assessing for a pCR.

There were several limitations to our study. This study was a retrospec-tive analysis of a small study popula-tion; however, this study was intended as a proof-of-principle study. We con-fined the study population to unifo-cal, unilateral, solid-looking masslike



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No relevant conflicts of interest to disclose. M.H. No relevant conflicts of interest to disclose. A.T. No relevant conflicts of interest to disclose. V.G. Financial activities related to the present arti-cle: TexRAD provided software under a research contract between the universities (Sussex and King’s College London), but since July 2013, the contract has been completed. Financial activities not related to the present article: none to dis-close. Other relationships: none to disclose.

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Changes in Primary Breast Cancer Heterogeneity May Augment Midtreatment MR Imaging Assessment of Response to Neoadjuvant Chemotherapy