Predictive value of [18F]FDG PET for pathological response of breast cancer to neo-adjuvant chemotherapy

S.-J. Kim*, S.-k. Kim, E. S. Lee, J. Ro and S. h. Kang

Research Institute and Hospital, National Cancer Center, Goyang, Gyeonggi, Republic of Korea

* Correspondence to: Dr S.-J. Kim, Department of Nuclear Medicine and Medical Research Institute, Pusan National University Hospital, Busan, 1-10, Ami-dong, Seo-gu, Busan, Republic of Korea, 602-739. Tel: +82-51-240-7389; Fax: +82-51-254-3237; Email: growthkim{at}daum.net


    Abstract
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Background: The aim of this prospective study was to evaluate the predictive value of [18F]fluorodeoxyglucose positron emission tomography (FDG PET) for the pathological response of breast cancer after completion of neo-adjuvant chemotherapy.

Methods: Fifty patients with newly diagnosed, non-inflammatory, large or locally advanced breast cancer undergoing neo-adjuvant chemotherapy were eligible for this study. Clinical assessment was accomplished by comparing initial tumor size with preoperative tumor size. Pathological responses were classified into three groups: pathological non-response (pNR), pathological partial response (pPR) and pathological complete response (pCR). To determine the effect of reduction rate (RR) of peak standardized uptake values for tumor responses, logistic regression analyses were performed. To identify an optimal threshold value of RR for the prediction of pathological response, receiver operating characteristic analysis was performed.

Results: Eight per cent (four of 50) of the patients had pCR and 46% had pPR. Ten per cent of patients had clinical CR and 52% had clinical PR. In clinical response, the RRs (±SD) of CR (–83.4±12), PR (–81.8±22.7) and NR (–79.7±31.9) showed no statistical differences (P >0.05). However, for pathological responses, the RR of CR (–96.5±3.4) had a lower value than those of PR (–87.9±15.1) and NR (–56.2±29.6) (P=0.0006; CR versus PR, P <0.05; CR versus NR, P <0.05; PR versus NR, P <0.01). When –88% of RR was used as threshold value for differentiation between pCR and pPR, the area under the curve (AUC) was 0.788 [standard error (SE) 0.106; 95% confidence interval (CI) 0.589–0.920]. The sensitivity and specificity were 100% and 56.5%, respectively. When –79% of RR was used as threshold value for differentiation between pathological responders and non-responders, the AUC was 0.838 (SE 0.059; 95% CI 0.707–0.927). The sensitivity and specificity were 85.2% and 82.6%, respectively.

Conclusions: Despite some limitations, this study suggests a possible predictive value of FDG PET for the assessment of the pathological response of primary breast cancer after neo-adjuvant chemotherapy. However, these findings deserve further investigation on a larger number of patients, and more frequent and earlier PET scans in each patient need to be performed to allow a better validation of the differentiation between the responder and non-responder groups.

Key words: breast cancer, FDG PET, neo-adjuvant chemotherapy, tumor response


    Introduction
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Breast cancer continues to be a major public health problem in the USA and in other Western countries. The American Cancer Society estimates that, in 2003, 211 300 new cases of breast cancer will be diagnosed and are expected to account for 15% of all female cancer deaths [1Go].

Locally advanced breast cancer (LABC) occurs relatively infrequently, but it poses a significant clinical challenge. LABC refers to large breast tumors associated with either skin or chest-wall involvement, or with fixed axillary lymph nodes, or with disease spread to the ipsilateral internal mammary or supraclavicular nodes [2Go]. These patients exhibit a high incidence of loco-regional recurrence after surgical treatment and have a poor prognosis, with many eventually succumbing to the effects of distant metastases. After being introduced for the preoperative treatment of inflammatory breast cancer, neo-adjuvant chemotherapy is increasingly used in the treatment of large primary or locally advanced breast cancer. An important advantage of neo-adjuvant chemotherapy is that it increases the rate of breast-conserving surgery by preoperatively reducing tumor volume [3Go, 4Go].

Along with early diagnosis, optimal therapy management is crucial to reduce mortality from breast cancer. Optimal tumor response to neo-adjuvant chemotherapy is a good prognostic factor that encourages continuation of neo-adjuvant therapy when the tumor is responding; however, the treatment should be altered if the tumor does not respond. Unfortunately, not every patient responds to neo-adjuvant chemotherapy, and it is of great importance to evaluate the response in each individual case.

Conventional imaging modalities have shown promise in monitoring the response of breast cancer to neo-adjuvant chemotherapy, including Doppler ultrasound and magnetic resonance imaging (MRI) [5Go–7Go]. However, these conventional imaging modalities of assessing tumor response rely on the documentation of morphological changes of the cancer. General restrictions of these imaging procedures include the limited accuracy and reproducibility in determining tumor size. Moreover, in patients with residual masses after neo-adjuvant chemotherapy, conventional imaging modalities do not distinguish viable tumor from fibrotic scar tissue. Currently, histopathological analysis is necessary to accurately assess response to neo-adjuvant chemotherapy.

Recent studies have shown the prognostic relevance of histopathological response among patients receiving neo-adjuvant chemotherapy [8Go, 9Go]. Pathological complete response (pCR) to neo-adjuvant chemotherapy is an important prognostic indicator for prolonged disease-free and overall survival [8Go–10Go]. Patients who achieve pCR may not require surgery for optimum local control. However, at present, surgical excision and histological examination of the resected specimen are the only way to reliably identify this group.

A significant number of studies have shown that [18F]fluorodeoxyglucose positron emission tomography (FDG PET) is able to detect, stage and monitor breast cancer with high sensitivity and specificity [11Go–14Go]. Assessment of treatment response should be possible earlier as compared with conventional imaging modalities, because changes in tumor metabolism precede a reduction of tumor size. Recently, two studies have demonstrated that an early reduction in uptake on serial FDG PET scans taken before and during chemotherapy can identify pathological responders [15Go, 16Go].

The aim of this prospective study was to evaluate the predictive value of FDG PET for pathological response in breast cancer patients after completion of neo-adjuvant chemotherapy. Regional FDG uptake after the completion of neo-adjuvant chemotherapy was compared with the pre-neo-adjuvant chemotherapy FDG PET scan in order to differentiate responding from non-responding patients, using histopathological results as the gold standard.


    Patients and methods
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Patients and treatment
Fifty patients with newly diagnosed, non-inflammatory, large or locally advanced breast cancer undergoing neo-adjuvant chemotherapy were eligible for this study. Patients were required to have a diagnosis of invasive breast carcinoma by core needle biopsy. Patients were excluded if they were pregnant, unwilling or unable to undergo serial FDG PET imaging studies, or were not considered surgical candidates. Neo-adjuvant chemotherapeutic regimens consisted of adriamycin 60 mg/m2 plus cyclophosphamide 600 mg/m2 (n=20), adriamycin 60 mg/m2 plus docetaxel (Taxotere) 75 mg/m2 (n=4), adriamycin 60 mg/m2 plus cyclophosphamide 600 mg/m2 plus docetaxel (Taxotere) 75 mg/m2 (n=3), paclitaxel (Taxol) 30 mg/m2 (n=1), docetaxel (Taxotere) 75 mg/m2 plus capecitabine (Xeloda) 1000 mg/m2 (n=21), or paclitaxel (Taxol) 30 mg/m2 plus carboplatin 450 mg/m2 (n=1). After completion of neo-adjuvant chemotherapy, all patients received appropriate surgery to remove the primary breast tumor and sample the axillary lymph nodes. After completion of neo-adjuvant chemotherapy, 33 patients underwent breast-conserving surgery and 17 patients underwent mastectomy. Patients who underwent breast-conserving treatment received additional radiotherapy. Postoperative chemotherapy was administered in all patients. The Joint Ethical Committee of the National Cancer Center Board approved this study protocol. A physician explained the details of the study to patients, and written informed consent was obtained from all patients.

FDG PET
FDG PET scans were obtained on a dedicated whole-body PET scanner (ADVANCE; GE Medical Systems, Milwaukee, WI, USA). The parameters of scanner included a radial spatial resolution of 5.2 mm full width half maximum, a tangential spatial resolution of 4.8 mm full width half maximum and a 15.3 cm axial field of view. All patients were instructed to fast for at least 8 h before the injection of [18F]FDG. Patients' serum glucose levels were measured (mean 102.8±16 mg/dl) before the intravenous administration of 370–555 MBq (Mega Becquerel) (10–15 mCi) [18F]FDG. Image acquisition for the whole body started at a mean time of 60 min after injection of [18F]FDG. All patients were studied in the supine position with both arms raised. The whole-body emission scan started from the proximal femur toward the neck for 5 min per frame. Data were collected in a 128 x 128 matrix. Segmented attenuation correction was done on the postinjection transmission image. The postinjection transmission corrected image was reconstructed by using an iterative ordered subsets maximization algorithm. Fifty patients underwent a total of 100 PET scans. Baseline scans were performed before core biopsy and neo-adjuvant chemotherapy, and a second scan was performed after completion of neo-adjuvant chemotherapy.

PET image analysis
Two nuclear physicians investigated the PET data. They were unaware of details regarding clinical and pathological tumor response and axillary lymph node status. Regions of interest (ROIs) were placed manually over all breast tumors in attenuation corrected images, and peak standardized uptake values (SUVp) within the ROIs were recorded. For each patient, subsequently obtained PET data were analyzed by applying the ROI identified in the pretreatment image to precisely the physical volume.

Assessment of neo-adjuvant chemotherapy response
Clinical assessment of treatment was accomplished by comparing initial tumor size with preoperative tumor size. This included physical examination in all patients plus, in the majority of cases, chest computed tomography. A clinical partial response (cPR) was defined, in accord with established definition, as a >50% decline in the product of the two greatest perpendicular tumor dimensions. Patients not achieving a 50% decline or an increase of tumor size up to 25% in the product of maximal tumor dimensions were considered to be clinical non-responders (cNR). Clinical progressive disease (cPD) was defined by an increase of tumor volume by >25% of the initial size. cPD was categorized into cNR for analysis. For the assessment of pathological response, all patients underwent lumpectomy (n=33) or mastectomy (n=17). All surgical resection specimens obtained after chemotherapy were evaluated by a pathologist in order to determine the degree of pathological tumor response of primary breast lesion. Histopathological tumor regression served as the gold standard for the evaluation of treatment response with PET. Response was classified into three groups: pathological non-response (pNR), pathological partial response (pPR) and pCR. pPR was deemed to have occurred when macro- and microscopic evidence of residual tumor tissue demonstrated features consistent with neo-adjuvant chemotherapy-induced damage. pCR was defined as the histological absence of invasive tumor cells.

Statistical analysis
Data are expressed as mean±SDs. To determine the effect of reduction rate (RR) of SUVp for the prediction of clinical and pathological tumor responses after neo-adjuvant chemotherapy, logistic regression analyses were performed. To identify an optimal threshold value of RR for the prediction of pathological response, receiver operating characteristic analysis was performed using MedCal Software (Mariakerke, Belgium). The area under curve (AUC) with 95% confidence interval (CI), standard error (SE), sensitivities and specificities were assessed. The differences of RR between cNR, cPR and cCR were assessed using ANOVA with Tukey's multiple comparison test. In addition, the differences in RR between pNR, pPR and pCR were assessed using SPSS for Windows. P <0.05 was considered statistically significant.


    Results
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Patient and tumor characteristics
Patient and tumor characteristics for the 50 patients are shown in Table 1. Fifty patients were enrolled in this study: 50 females (mean age 46.5±10.9 years; range 27–68). The tumor type was invasive ductal carcinoma in 47 patients, mucinous carcinoma in two patients and medullary carcinoma in one patient. The tumor size was a mean of 3.5 cm in greatest dimension (range 1.5–7.5). Twenty-six of 50 tumors were estrogen receptor positive and 13 were progesterone receptor positive by immunocytochemistry.


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Table 1. Patient and tumor characteristics

 
Tumor response
Clinical assessment of primary tumor response was based on serial measurements of tumor dimensions, which for the purposes of this study were obtained by physical examination, mammography, ultrasound and computed tomography. One of the patients followed by physical examination had diffuse breast cancer involving whole breast and indeterminable tumor boundaries for which precise size estimates could not be obtained. For this patient, response was based on the subjective impression of the referring physician. The overall clinical (cCR and cPR) and pathological (pCR and pPR) response rates after completion of neo-adjuvant chemotherapy regimen were 62% and 54%, respectively (Table 2). Eight per cent (four of 50) of the patients had undergone pCR and 46% had pPR. Ten per cent of patients had cCR and 52% had cPR.


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Table 2. Tumor response: pathological versus clinical response

 
Pre-neo-adjuvant chemotherapy SUVp
The SUVp of primary breast lesions that demonstrated pCR (mean 11.5±2.3) and pPR (13±5.2) after neo-adjuvant chemotherapy were not different from those for lesions that failed to achieve a pathological response (9.5±6.5) (P=0.136; Figure 1). Also, The SUVp of primary breast lesions that demonstrated cCR (8.2±4.1) and cPR (12.8±6.6) after neo-adjuvant chemotherapy were not different from those for lesions that failed to demonstrate a clinical response (10.1±4.6) (P=0.218; Figure 1).



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Figure 1. Pre-neo-adjuvant chemotherapy peak standardized uptake values (SUVp) according to pathological and clinical tumor response.

 
SUVp RR compared with tumor response
The use of RR for the prediction of clinical and pathological responses of primary breast cancer was analyzed by logistic regression. For this analysis, the patients were grouped into clinical responders (cCR, cPR) and non-responders (cNR), and pathological responders (pCR, pPR) and non-responders (pNR). Table 3 shows that RR could predict the pathological response of primary breast cancer to neo-adjuvant chemotherapy ({chi}2 8.37; P=0.0038; odds ratio 0.933; 95% CI 0.891–0.978). However, RR was not associated with clinical response state of primary breast cancer ({chi}2 1.67; P=0.195; odds ratio 0.985; 95% CI 0.963–1.008). Figure 2 showed RR differences between responders and non-responders of clinical and pathological responses of primary breast cancer. Figure 3 demonstrated the ANOVA with Tukey multiple comparison tests of the differences between clinical and pathological CR, PR and NR. In clinical responses, the RRs of CR (–83.4±12), PR (–81.8±22.7) and NR (–79.7±31.9) showed no statistical differences (P >0.05). However, for pathological responses, the RR of CR (–96.5±3.4) had a lower value than those of PR (–87.9±15.1) and NR (–56.2±29.6) (P=0.0006; CR versus PR, P <0.05; CR versus NR, P <0.05; PR versus NR, P <0.01).


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Table 3. Peak standardized uptake values reduction rate for the prediction of pathological [odds ratio (OR) 0.933; 95% confidence interval (CI) 0.891–0.978] and clinical (OR 0.985; 95% CI 0.963–1.008) response

 


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Figure 2. Peak standardized uptake values reduction rates (SUVp) of clinical (c) and pathological (p) responders and non-responders. CR, complete response; PR, partial response; Nr, non-response.

 


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Figure 3. The differences of peak standardized uptake values (SUVp) reduction rate between clinical (c) and pathological (p) complete response (CR), partial response (PR) and non-response (NR).

 
Prediction of pathological response using FDG PET
Receiver operating curve analyses were performed to determine optimal differentiation cut-off values of RR for differentiation of pathological responders and non-responders after completion of neo-adjuvant chemotherapy. When –88% RR was used as the threshold value for differentiation between pCR and pPR, the AUC was 0.788 (SE 0.106; 95% CI 0.589–0.920; Figure 4A). The sensitivity and specificity were 100% and 56.5%, respectively. When –79% RR was used as the threshold value for differentiation between pathological responders and non-responders, the AUC was 0.838 (SE 0.059; 95% CI 0.707–0.927; Figure 4B). The sensitivity and specificity were 85.2% and 82.6%, respectively.



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Figure 4. Receiver operating characteristic curve analyses for the prediction of pathological response of breast cancer using FDG PET. (A) Differentiation between complete and partial pathological response. (B) Differentiation between pathological responders and non-responders.

 

    Discussion
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
Neo-adjuvant chemotherapy is increasingly used in breast cancer patients to decrease the tumor size in large cancers or, in cases of unfavorable anatomic location, to make these patients eligible for breast-conservation treatment [4Go, 17Go, 18Go].

pCR to neo-adjuvant chemotherapy is an important prognostic indicator for prolonged disease-free and overall survival [8Go–10Go]. Accurate and reliable identification of pCR before surgery offers the potential to avoid unnecessary operations with good prognosis, without jeopardized local control or long-term survival. Currently, postoperative pathological examination is the mainstay for confirmation of pCR.

This study demonstrated that pCRs showed much higher SUVp RR than the pPRs and pNRs. Also, using a threshold of –88% or more reduction of RR after completion of neo-adjuvant chemotherapy, FDG PET could differentiate between pCR and pPR with 100% sensitivity and 56.5% specificity. Using a cut-off value of –79% RR, we were able to accurately predict pathological responders with 85.2% sensitivity and 82.6%. However, RR could not predict the clinical response state after completion of neo-adjuvant chemotherapy in this study.

The common methods for monitoring tumor response to neo-adjuvant chemotherapy, physical examination and conventional imaging modalities, have limitations. In a study conducted by Feldman et al. [19Go], nearly 50% of patients with a cCR exhibited macroscopic residual disease at surgery, whereas 20% of patients with a cPR were found to have no macroscopic tumor at surgery. Maini et al. [20Go] reported that clinical evaluation had only 35% sensitivity for the prediction of tumor response and 67% specificity for the correct prediction of tumor absence after neo-adjuvant chemotherapy. Clinical examination is often unable to differentiate a residual mass representing fibrosis from a residual tumor [19Go]. Similar problems were found when ultrasound and mammography were used to evaluate tumor response. Ultrasound and mammography predominantly evaluate the tumor size or volume under therapy. This is apparently an uncertain parameter by which to distinguish responders from non-responders, as not all responders reveal a downsizing of the total tumor extent. One problem of mammography is that it provides not cross-sectional but projection images. Overlying breast parenchyma or implants affect the diagnostic accuracy in the follow-up situation. Furthermore, mammography depends primarily on the extent to which the tumor can be delineated from the adjacent breast tissue. According to Vinnicombe et al. [21Go], only three of eight patients with a mammographical CR had no residual disease at pathological examination, and among five patients with a CR at pathological examination, four patients had residual masses depicted at mammography. Ultrasound as an adjunct to mammography has major limitations for the evaluation of tumor response, since even in the case of CR, ultrasound cannot exclude residual tumor, mainly because it cannot be differentiated from fibrosis [22Go]. Evaluation of tumor response by MRI is possible due to decrease of the initial contrast enhancement and a loss of tumor diameter reflecting tumor response. However, diffuse enhancement patterns can occur due to radiotherapy, but these effects are transient. MR spectroscopy is not, to date, used routinely, and remains a topic of research [23Go].

Similar to our results, early monitoring of response using baseline and serial FDG PET scans is able to differentiate pathological responders and non-responders [15Go, 16Go]. Using a threshold of 20% or more reduction in FDG uptake after a single cycle of neo-adjuvant chemotherapy, Smith et al. [15Go] were able to accurately predict pathological responders with a sensitivity of 90% and specificity of 74%. Schelling et al. [16Go], using a 55% decrease in standard FDG uptake below the baseline as a threshold value, correctly identified pathological responders after the first cycle of neo-adjuvant chemotherapy with 88% accuracy, increasing to 91% after the second cycle. Wahl et al. [13Go] demonstrated that FDG uptake decreases in therapy responders before reduction of tumor size can be detected. Thus differentiation of clinical responders from non-responders using FDG PET could be possible [13Go]. However, in this study, RR of FDG PET was not able to predict the clinical response state of primary breast cancer after completion of neo-adjuvant chemotherapy.

FDG PET is, however, not able to estimate important biological features of breast cancer such as differentiation, grading and proliferation rate [24Go, 25Go]. Avril et al. [24Go] found that one of the major limitations of PET is in the imaging of small breast cancer, owing to partial volume effects. However, this is not essential in neo-adjuvant chemotherapy, because T1 tumors are not treated according to these protocols. Another limitation is the tumor type, e.g. lobular carcinoma, which is also a problem for all other imaging modalities. In this study, the majority tumor type was invasive ductal carcinoma, and there was no case of lobular carcinoma.

Some potential limitations of this study should be considered. The number of patients included was relatively small. To obtain an objective cut-off value of RR, studies with larger patients numbers should be performed. Another limitation of this study is the lack of serial FDG PET scans taken during the neo-adjuvant chemotherapy courses instead of at completion of chemotherapy. More frequent and regular assessment of FDG uptake, perhaps on a monthly basis, may be more effective in identifying the accurate threshold values for the differentiation of pathological responders from non-responders.

Conclusions
The optimum role of FDG PET in predicting the response of breast cancers to neo-adjuvant chemotherapy is still not clearly defined. There is strong, prospective validated evidence that an early reduction in uptake on FDG PET can identify pathological responders and predict those unlikely to regress clinically. Despite some limitations, this study suggested a possible predictive role of FDG PET for assessment of pathological response of primary breast cancer after neo-adjuvant chemotherapy. However, these findings deserve further investigation on a larger number of patients, and more frequent and earlier PET scans in each patient need to be performed to allow a better validation of the differentiation between the responder and non-responder groups.

Received for publication January 12, 2004. Revision received April 27, 2004. Accepted for publication April 28, 2004.


    References
 Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 
1. Jemal A, Murray T, Samuels A et al. Cancer statistics, 2003. CA Cancer J Clin 2003; 53: 5–26.[Abstract/Free Full Text]

2. Singletary SE, Allred C, Ashley P et al. Revision of the American Joint Committee on Cancer Staging System for Breast Cancer. J Clin Oncol 2002; 20: 3628–3636.[Abstract/Free Full Text]

3. Scholl SM, Fourquet A, Asselain B et al. Neoadjuvant versus adjuvant chemotherapy in premenopausal patients with tumors considered too large for breast conserving surgery: Preliminary results of a randomized trial: S6. Eur J Cancer 1994; 5: 645–652.

4. Fisher B, Brown A, Mamounas E et al. Effect of preoperative chemotherapy on local-regional disease in women with operable breast cancer: Findings from National Surgical Adjuvant Breast and Bowel Project B-18. J Clin Oncol 1997; 15: 2483–2493.[Abstract]

5. Kedar RP, Cosgrove DS, Smith IE et al. Breast carcinoma: measurement of tumor response to primary medical therapy with color flow Doppler imaging. Radiology 1994; 190: 825–830.[Abstract]

6. Abraham DC, Jones RC, Jones SE et al. Evaluation of neoadjuvant chemotherapeutic response of locally advanced breast cancer by magnetic resonance imaging. Cancer 1996; 78: 91–100.[CrossRef][ISI][Medline]

7. Gilles R, Guinebretiere JM, Toussaint C et al. Locally advanced breast cancer: contrast-enhanced subtraction MR imaging of response to preoperative chemotherapy. Radiology 1994; 191: 633–638.[Abstract]

8. Machiavelli MR, Romero AO, Perez JE et al. Prognostic significance of pathological response of primary tumor and metastatic axillary lymph nodes after neoadjuvant chemotherapy for locally advanced breast carcinoma. Cancer J Sci Am 1998; 4: 125–131.[ISI][Medline]

9. Honkoop AH, van Diest PJ, de Jong JS et al. Prognostic role of clinical, pathological and biological characteristics in patients with locally advanced breast cancer. Br J Cancer 1998; 77: 621–626.[ISI][Medline]

10. Eltahir A, Heys SD, Hutcheon AW et al. Treatment of large and locally advanced breast cancers using neoadjuvant chemotherapy. Am J Surg 1998; 175: 127–132.[CrossRef][ISI][Medline]

11. Adler LP, Crowe JP, Al-Kaisi NK, Sunshine JL. Evaluation of breast masses and axillary lymph nodes with F-18 2-deoxy-2-fluoro-D-glucose PET. Radiology 1993; 187: 743–750.[Abstract]

12. Nieweg OE, Kim EE, Wong WH et al. Positron emission tomography with fluorine-18-deoxyglucose in the detection and staging of breast cancer. Cancer 1993; 71: 3920–3925.[ISI][Medline]

13. Wahl RL, Zasadny K, Helvie M et al. Metabolic monitoring of breast cancer chemohormonotherapy using positron emission tomography: initial evaluation. J Clin Oncol 1993; 11: 2101–2111.[Abstract]

14. Jannson T, Westlin JE, Ahlstrom H et al. Positron emission tomography studies in patients with locally advanced and/or metastatic breast cancer: a method for early therapy evaluation. J Clin Oncol 1996; 88: 1204–1209.

15. Smith IC, Welch AE, Hutcheon AW et al. Positron emission tomography using F-18-fluorodeoxy-D-glucose to predict the pathologic response of breast cancer to primary chemotherapy. J Clin Oncol 2000; 18: 1676–1688.[Abstract/Free Full Text]

16. Schelling M, Avrill N, Nahrig J et al. Positron emission tomography using F-18 Fluorodeoxyglucose for monitoring primary chemotherapy in breast cancer. J Clin Oncol 2000; 18: 1689–1695.[Abstract/Free Full Text]

17. Powles TJ, Hickish TF, Markris A et al. Randomized trial of chemoendocrine therapy started before or after surgery for treatment of primary breast cancer. J Clin Oncol 1995; 13: 547–552.[Abstract]

18. Vlastos G, Mirza NQ, Lenert JT et al. The feasibility of minimally invasive surgery for stage IIA, IIB, and IIIA breast carcinoma patients after tumor downstaging with induction chemotherapy. Cancer 2000; 88: 1417–1424.[CrossRef][ISI][Medline]

19. Feldman LD, Hortobagyi GN, Buzdar AU et al. Pathological assessment of response to induction chemotherapy in breast cancer. Cancer Res 1986; 46: 2578–2581.[Abstract]

20. Maini CL, Tofani A, Sciuto R et al. Technetium-99m-MIBI scintigraphy in the assessment of neoadjuvant chemotherapy in breast carcinoma. J Nucl Med 1997; 38: 1546–1551.[Abstract]

21. Vinnicombe SJ, MacVicar AD, Guy RL et al. Primary breast cancer: mammographic changes after neoadjuvant chemotherapy, with pathologic correlation. Radiology 1996; 198: 333–340.[Abstract]

22. Chollet P, Charrier S, Brain E et al. Clinical and pathological response to primary chemotherapy in operable breast cancer. Eur J Cancer 1997; 33: 862–866.[CrossRef][Medline]

23. Jagnnathan NR, Singh M, Govindaraju V et al. Volume localized in vivo proton MR spectroscopy of breast carcinoma: variation of water-fat ratio in patients receiving chemotherapy. NMR Biomed 1998; 11: 414–422.[CrossRef][ISI][Medline]

24. Avril N, Rose CA, Schelling M et al. Breast imaging with positron emission tomography and fluorine-18 fluorodeoxyglucose: use and limitations. J Clin Oncol 2000; 18: 3495–3502.[Abstract/Free Full Text]

25. Avril N, Menzel M, Dose J et al. Glucose metabolism of breast cancer assessed by 18F-FDG PET: histologic and immunohistochemical tissue analysis. J Nucl Med 2001; 42: 9–16.[Abstract/Free Full Text]





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