|
Advertisement |
|
|
||
 |
|
|||||
|
|||||
|
 |
||||||
|
||||||
|
||||||
|
Originally published as JCO Early Release 10.1200/JCO.2006.08.8013 on July 30 2007 © 2007 American Society of Clinical Oncology. Doctor, What Are My Chances of Having a Positive Sentinel Node? A Validated Nomogram for Risk Estimation
From the Department of Surgery, Memorial Sloan-Kettering Cancer Center, New York, NY; the Department of Quantitative Health Sciences Cleveland Clinic, Cleveland, OH; and the Departmento de Cirurgia, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil Address reprint requests to Kimberly J. Van Zee, MS, MD, FACS, Department of Surgery, Memorial Sloan-Kettering Cancer Center, 1275 York Ave, MRI 1026, New York, NY 10021; e-mail: vanzeek{at}mskcc.org
Purpose Lymph node metastasis is a multifactorial event. Several variables have been described as predictors of lymph node metastasis in breast cancer. However, it is difficult to apply these data—usually expressed as odds ratios—to calculate the probability of sentinel lymph node (SLN) metastasis for a specific patient. We developed a user-friendly prediction model (nomogram) based on a large data set to assist in predicting the presence of SLN metastasis. Patients and Methods Clinical and pathologic features of 3,786 sequential SLN biopsy procedures were assessed with multivariable logistic regression to predict the presence of SLN metastasis in breast cancer. The model was subsequently applied to 1,545 sequential SLN biopsies. A nomogram was created from the logistic regression model. A computerized version of the nomogram was developed and is available on the Memorial Sloan-Kettering Cancer Center (New York, NY) Web site. Results Age, tumor size, tumor type, lymphovascular invasion, tumor location, multifocality, and estrogen and progesterone receptors were associated with SLN metastasis in multivariate analysis. The nomogram was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.754 when applied to the validation group. Conclusion Newly diagnosed breast cancer patients are increasingly interested in information about their disease. This nomogram is a useful tool that helps physicians and patients to accurately predict the likelihood of SLN metastasis.
Axillary lymph node status is the most influential prognostic factor in patients with invasive breast cancer.1-4 Axillary sentinel lymph node (SLN) biopsy is a standard of care for patients with early-stage breast cancer, replacing traditional axillary lymph node dissection when the SLN is negative.5-7 Lymph node metastasis (LNM) is a multifactorial event. Several variables have been described as predictors of LNM in breast cancer (Appendix Table A1, online only). Usually the results of multivariate analyses of predictors of LMN are expressed as odds ratios, which make it difficult to apply and calculate the probability of SLN metastases for a specific patient. Newly diagnosed breast cancer patients are increasingly interested in information about their disease. In 2004, eight of 10 Internet users searched online for information on at least one major health topic, which translates to about 100 million American adults who use the Internet to obtain health information.8 Breast cancer is one of the most common health topics researched online, and breast oncologists are likely to encounter more patients who have used the Internet in the context of their diagnosis.9-19 With an increased focus on consumer and patient use of information technologies (eg, Internet, multimedia home-care informatics, computerized educational programs) for medical information, tailoring this information appropriately to individuals’ cancers, literacy, and culture-specific needs should be a priority.20 The new web-oriented patient demands more precise answers for her questions. Therefore, even with many studies on prediction of LNM, it is a quite challenge to answer precisely (numerically) the simple question: Doctor, what are my chances of having a positive sentinel node? The goal of this study is to create, using a large data set, a validated nomogram to assist in predicting the presence of SLN metastasis in breast cancer, and to develop a user-friendly software program to allow easy calculation of the risk predicted by the nomogram.
This project was reviewed and approved by the Memorial Sloan-Kettering Cancer Center (MSKCC; New York, NY) institutional review board. Data were obtained from retrospective review of a database in which data were prospectively collected for each procedure. The patients were arbitrarily separated into two groups by date of SLN biopsy: the modeling group and the validation group. Our modeling study population consisted of the 4,608 breast cancer procedures of axillary SLN biopsy performed at MSKCC between September 12, 1996, and July 31, 2002. The inclusion criteria were patients with invasive breast carcinoma who had not undergone neoadjuvant treatment, and who underwent a successful SLN biopsy. A total of 3,786 SLN procedures were eligible for our study and were considered the modeling group. The validation study population consisted of 2,037 SLN biopsies performed at MSKCC between August 1, 2002 and May 1, 2004. The inclusion and exclusion criteria were identical to those in the modeling population, except for the date of SLN biopsy. There were 1,545 eligible procedures, and these were classified as the validation group. Detailed information for exclusions is listed in Appendix Table A2 (online only). Clinicopathologic characteristics of the modeling and validation groups are described in Table 1.
We utilized each of the following variables: age, sex, primary tumor size, laterality, palpability, histologic tumor type, histologic grade, nuclear grade, tumor location within the breast (quadrant), lymphovascular invasion (LVI), multifocality, estrogen receptor (ER) status, progesterone receptor (PR) status, HER-2/neu status, and type of surgery. The method of pathologic evaluation of SLNs has not changed after the first 78 cases of the sentinel node protocol (institutional review board protocol No. 96-049). Beginning with case 79, frozen section and immunohistochemical (IHC)-stained sections were routinely performed. IHC staining was done retrospectively in the first 78 cases. The technique of SLN biopsy and histopathologic analyses have been described elsewhere.21
Statistical Methods Logistic regressions, using interaction between the predictor and data set, tested whether a significant shift in the relationship between each predictor and SLN positivity occurred across data sets. A nomogram for predicting SLN metastases was developed based on the patients in the modeling group, and then validated with the patients in the validation group. In the modeling group (n = 3,786), logistic regression was used to analyze the association of each variable with the likelihood of SLN metastases. We used restricted cubic splines to relax the linearity assumption when fit was improved. This model was applied to the validation group (n = 1,545) by inserting all postoperative data into the model and calculating each individual patient's probability of having positive SLNs. The discrimination of the model was measured by using the area under the receiver operating characteristic (ROC) curve. The calibration of the model was assessed graphically. Patients were grouped into deciles based on their nomogram predictions. For each decile, the mean predicted probability was compared with the proportion of patients who actually had positive SLNs (actual probability). A bootstrapping analysis was also performed. First, we computed the apparent accuracy of the full model on the entire data set. Second, we computed the accuracy on a sample of the same size but with replacement, refitting these data. Third, we then calculated the accuracy of this second model, using the original data set. Optimism is the difference between the second and third accuracies. After repeating this process 200 times, the mean optimism was subtracted from the first model accuracy to arrive at a nearly unbiased estimate. A user-friendly software program was developed to facilitate the calculation of the probability of SLN metastases for each patient.
The overall frequency of SLN metastasis was 33%. The frequency of SLN metastasis by T stage and method of detection of metastases is demonstrated in Figure 1. The great majority of metastases were identified by frozen section of the SLN. The enhanced pathologic analysis identified additional metastases in 10% of patients, 4% by IHC only, and 6% by serial section. There was no statistical difference (Pearson  2, 3.357; P = .34) between the frequencies of method of detection of the modeling (Fig 1) and validation groups (Appendix Fig A1, online only).
There was a higher incidence of SLN metastases in the validation group (37.5% v 33.0%; P < .05). No significant shift in the relationship between each predictor and SLN positivity occurred across data sets (all P > .05; Table 2).
In multivariate analysis, variables that were associated with SLN metastasis in breast cancer were tumor type, LVI, tumor size, tumor location, age, multifocality, and ER and PR status (P < .05 for all variables). There was no difference in frequency of SLN metastases between ductal and lobular tumor types (P > .25). In Appendix Table A3, we list the results of the final logistic regression models for predicting SLN metastasis in breast cancer. We also created a model that does not include ER and PR because this information may not be available before the definitive surgery. Two nomograms based on these models were developed in the modeling group (n = 3,786) and appear in Figures 2A and 3A. The overall predictive accuracy of the models applied to the validation population, as measured by the area under ROC curve was 0.754 for both nomograms. For each model we built a calibration curve (Figs 2B and 3B) and a plot of the distribution of predicted probabilities (Figs A2 and A3, online only). The bootstrapping analysis demonstrated concordance indices of 0.753.
Using the Nomogram The nomogram consists of rows. The first row (POINTS) is the point assigment for each variable. For example, in Figure 2A, rows 2 to 10 represent the variables included in the model. For an individual patient, each variable is assigned a point value (uppermost scale, POINTS) based on the clinicopathologic characteristics. A vertical line is made between the appropriate variable value and the POINTS line. The assigned points for all nine variables are summed and the total is found in row 11 (TOTAL POINTS). Once the total is located, a vertical line is made between TOTAL POINTS and the final row, row 12 (Predicted Probability of +SLN). Below each nomogram is its corresponding calibration plot. The nomogram developed using the modeling group of patients (n = 3,786) was applied to the validation group (n = 1,545). A histogram of the calculated probabilities for the validation group is shown along the horizontal axis. These 1,545 patients are grouped in deciles of their predicted probabilities, and the actual incidence of additional SLN metastases was calculated for each decile. The vertical axis represents the actual observed incidence (Actual Probability), and the horizontal axis represents the probability calculated by the nomogram (Predicted Probability). For each decile of the validation group, a triangle is plotted to show actual probability. If the model were perfect, all triangles would lay on the dotted line with a slope of 1. The version of the nomogram in Figure 2 is for use when information on ER and PR status are available; that in Figure 3 is for those patients where ER and PR status are not available. To facilitate ease of use in clinical setting, in addition to the graphic nomograms, we have made an application (software) for personal computers, Palm (Palm Inc, Sunnyvale, CA), pocket personal computers, and Macintosh computers (Apple Inc, Cupertino, CA). These applications will be made available at our web site (www.mskcc.org/nomograms).
The frequency of axillary lymph node metastases (ALNM) by T stage (American Joint Committee on Cancer)3 varies among the studies of pre-SLN era. In the larger studies, the frequency is 10% in T1mic,22 9% to 13% in T1a,23-28 13% to 19% in T1b,23-29 26% to 29% in T1c,23,24,27,29 39% to 50% in T2 less or equal 3 cm,23,24,27,29 48% to 59% in T2 larger than 3 cm,27,29 and 71% to 80% in T3.24,27 The frequency of ALNM in each T stage in the SLN era appears higher than the corresponding frequency from the pre-SLN era. This is likely attributable to the use of enhanced histopathologic analysis in the SLN era, which allows detection of so-called occult metastases, not usually seen in the past with conventional pathologic analyses. Many studies have demonstrated an increase of 10% to 52% in the detection of SLN metastasis owing to the addition of serial sectioning and IHC analyses.30-35 Although the clinical relevance of these micrometastatic deposits is the subject of controversy, data from large studies with long follow-up periods suggest that occult metastases may be associated with a small but significant decrease in overall and/or disease-free survival.36-40 Maibenco et al,41 analyzing the Surveillance, Epidemiology, and End Results data, confirm the impact of micrometastases in long-term survival, but found no difference among patients with tumors smaller than 1 cm. However, other studies did not show any impact of lymph node micrometastases on survival.42,43 Large, ongoing prospective studies such as the National Surgical Adjuvant Breast and Bowel Project trial B32 (NSABP B-32), the American College of Surgeons Oncology Group trial Z0010 (ACOSOG-Z0010), and the International Breast Cancer Study Group trial 23-01 will better define the significance of micrometastatic lymph node disease. The inclusion of IHC-only positive SLNs may cause the nomogram to falsely predict for a positive node. Current AJCC staging criteria labels an IHC-only positive node as N0(i+). However, in our previous study, of patients with an IHC-only positive sentinel node, 12% had additional hematoxylin and eosin (H&E) metastases (macrometastases) on completion axillary dissection, which would have been missed if the patient did not undergo completion axillary dissection.21 Since this information may be relevant, we chose to include both IHC and H&E positive sentinel nodes in the nomogram. We reviewed the published studies that address the predictors for ALNM in the pre-SLN and SLN eras (Appendix Table A1, online only). The great majority of these studies either collected the data retrospectively, did not mention how the data were collected, or had no central pathology review. In these studies, tumor size, age, LVI, and histologic subtype were the variables most commonly described as independently associated with ALNM. Only two studies, Ravdin at al44 and Gann et al,45 validated their statistical models in another data set. In SLN studies, the variables associated with SLN metastases were, as expected, quite similar. Our study confirms these results, with the additional findings of tumor location (fewer SLN metastases in patients with upper inner quadrant [UIQ] tumors), ER status, and PR status as independent predictor factors associated with SLN metastasis. The frequencies of SLN metastases in ER/PR-positive tumors were higher than those for ER/PR-negative tumors, corresponding to a small, but significant, increase of 2.0% for ER and 4.6% for PR. Although this observation seems counterintuitive, it actually is in agreement with the findings of other large studies. Ravdin et al44 studied data from 26,683 patients of the National Breast Cancer Tissue Resource. Gann et al45 analyzed data from 18,025 patients of the nationwide Patient Care Evaluation survey of the American College of Surgeons. Viale et al46 from Veronesi's group in Milan, studied the prediction of sentinel LNM for 4,351 patients, and they identified the same order of differences (2.8% for ER; 5.7% for PR). The lower frequency of SLN metastasis in UIQ location tumors observed in this study suggests the possibility of alternative routes of lymphatic flow, especially to the internal mammary chain. In an earlier comprehensive review of the literature on internal mammary nodes, we have addressed the importance of UIQ tumors in terms of survival and their relation to LNM.47 Two large data set studies highlight the importance and clinical implications of internal mammary sentinel nodes when identified on preoperative lymphoscintigraphy.48,49 Nevertheless, this issue is quite controversial, and it is beyond the scope of this study. We have developed a nomogram and a corresponding computer application that predict the likelihood of SLN metastases; in the validation test, they performed well (calibration plots in Figs 2 and 3). The application utilizes nine readily available clinical variables: age, tumor size, tumor type, LVI, multifocality, nuclear grade, tumor location, and ER and PR status. This allows quick and easy calculation. Our patients can now estimate their individual likelihood of having SLN metastases. It has been suggested that patients with a low risk of axillary lymph node involvement should be spared SLN biopsy.46 While SLN biopsy is an extremely safe procedure with low morbidity, there are some patients, typically elderly, in whom consideration is given to avoiding SLN biopsy because of comorbid conditions. In this small patient population, the nomogram could help to weigh the risks and benefits of SLN biopsy. This application could potentially help physicians to select precisely those patients at very low risk for SLN involvement who could be spared an SLN biopsy. Alternatively, it could encourage a physician to reconsider an initial decision to spare a patient an SLN biopsy in the event that a higher than expected risk is identified. Clearly staging the axilla is more accurate than any predictive model. However, in patients with significant comorbidities, and in whom avoidance of standard axillary staging is being considered, the predicted probability might help the clinician weigh the risks and benefits more appropriately. One could argue that pathologic information such as tumor size, LVI, and multifocality are available only after a surgical biopsy. Indeed, many patients have excisional biopsies before SLN biopsy, and for these women, all necessary predictive variables are available. For those who have only a core biopsy result available, it has been demonstrated that evaluation of tumor size and multifocality preoperatively with mammography, ultrasound, and/or magnetic resonance imaging is accurate and provides a good estimation of tumor size and the possibility of multifocality.50-60 With the quality of breast imaging available today, it is unlikely to identify large differences between imaging and pathological tumor sizes that would impact dramatically in the estimated probability of the nomogram. However, we agree that this is a limitation of the applicability of the nomogram. A practical solution is to enter values into the nomogram from either end of the range of estimates that seem reasonable for a given variable. For example, if the mammogram suggests a 4-cm spiculated mass, but the sonogram estimates a 2-cm hypoechoic mass, the user can put in both values, and obtain a range of estimates, depending on the final pathology size. Or, if the presence of LVI cannot be precisely assessed, a range of estimates can be calculated with and without LVI. One could point out that there are differences between the two data sets, in terms of variable distributions and shifts over time; however, these differences put more pressure on the prediction model to be accurate in the validation data set. If the prediction model is accurate, it will properly adjust the predictions to compensate. More important, the results of Table 2 reveal no evidence of a shift in the relationship between any of the predictors and SLN positivity across the development and validation data sets. Our model is imperfect. The area under the ROC curve was 0.754. This means that if we randomly select two women, one of whom has at least one positive SLN, and one of whom has negative SLNs, there is a 75.4% chance that the model will predict a higher probability for the positive woman. This is a scale that ranges from 0.5, which would be achieved by tossing a coin, to 1.0, which would require perfect ability to distinguish the woman with positive nodes from the one with negative nodes. Another example, for comparison: the area under ROC curve for film and digital mammography is, 0.61 and 0.82, respectively, for the detection of breast cancer.61 Therefore, our nomograms are as accurate for the prediction of a positive sentinel node as mammography is accurate for the detection of breast cancer. Nevertheless, this nomogram provides an accurate, accessible, multivariate predictive model that has been prospectively validated in a large independent data set. This model and its computer application represent a significant improvement over our intuition or theorization based on subjectivity of guesstimates. Finally, it provides the precise and deserved answer to our patient's question: Doctor, what are my chances of having a positive sentinel node?
The author(s) indicated no potential conflicts of interest.
Conception and design: José Luiz B. Bevilacqua, Kimberly J. Van Zee Financial support: Hiram S. Cody III Administrative support: Hiram S. Cody III Provision of study materials or patients: Jane V. Fey, Hiram S. Cody III, Kimberly J. Van Zee Collection and assembly of data: José Luiz B. Bevilacqua, Michael W. Kattan, Jane V. Fey Data analysis and interpretation: José Luiz B. Bevilacqua, Michael W. Kattan, Patrick I. Borgen, Kimberly J. Van Zee Manuscript writing: José Luiz B. Bevilacqua Final approval of manuscript: José Luiz B. Bevilacqua, Michael W. Kattan, Jane V. Fey, Hiram S. Cody III, Patrick I. Borgen, Kimberly J. Van Zee
We thank Alexandra MacDonald for her expert editorial assistance and Ruy Bevilacqua, MD, PhD, for his insights and encouragement.
published online ahead of print at www.jco.org on July 30, 2007. Authors’ disclosures of potential conflicts of interest and author contributions are found at the end of this article.
1. Association of Directors of Anatomical and Surgical Pathology: Recommendations for the reporting of breast carcinoma: Association of Directors of Anatomic and Surgical Pathology. Hum Pathol 27:220-224, 1996[Medline] 2. Clark GM: Prognostic and predictive factors, in Harris JR, Lippman ME, Morrow M, et al (eds): Diseases of the Breast (ed 1). Philadelphia, PA, Lippincott, Williams & Wilkins, 1996, pp 461-485 3. TNM (2002) - Breast, in Sobin LH, Wittekind C, International Union Against Cancer (UICC) (eds): (ed 6). New York, NY, Wiley-Liss, 2002, pp 131-142 4. Early Breast Cancer Trialists Collaborative Group: Multi-agent chemotherapy for early breast cancer. Cochrane Database Syst Rev:CD000487, 2002 5. Carlson RW, McCormick B: Update: NCCN breast cancer Clinical Practice Guidelines. J Natl Compr Canc Netw 3:S7-S11, 2005 (suppl 1)[Medline] 6. Naik AM, Fey J, Gemignani M, et al: The risk of axillary relapse after sentinel lymph node biopsy for breast cancer is comparable with that of axillary lymph node dissection: A follow-up study of 4008 procedures. Ann Surg 240:462-468, 2004[CrossRef][Medline] 7. Veronesi U, Paganelli G, Viale G, et al: A randomized comparison of sentinel-node biopsy with routine axillary dissection in breast cancer. N Engl J Med 349:546-553, 2003 8. Fox S: Health Information Online. Washington, DC, Pew Internet & American Life Project, 2005. http://www.pewinternet.org 9. Lieberman MA, Goldstein BA: Self-help on-line: An outcome evaluation of breast cancer bulletin boards. J Health Psychol 10:855-862, 2005 10. Shaw BR, Hawkins R, Arora N, et al: An exploratory study of predictors of participation in a computer support group for women with breast cancer. Comput Inform Nurs 24:18-27, 2006[CrossRef][Medline] 11. Gustafson DH, McTavish FM, Stengle W, et al: Use and impact of eHealth system by low-income women with breast cancer. J Health Commun 10:195-218, 2005 (suppl 1)[CrossRef][Medline] 12. Gustafson DH, McTavish FM, Stengle W, et al: Reducing the digital divide for low-income women with breast cancer: A feasibility study of a population-based intervention. J Health Commun 10:173-193, 2005 (suppl 1)[CrossRef][Medline] 13. Sagrillo DP, Kunz S: Surfing the internet: Breast cancer and reconstruction resources. Plast Surg Nurs 25:213-215, 2005 14. Owen JE, Klapow JC, Roth DL, et al: Randomized pilot of a self-guided internet coping group for women with early-stage breast cancer. Ann Behav Med 30:54-64, 2005[CrossRef][Medline] 15. Nguyen KD, Hara B, Chlebowski RT: Utility of two cancer organization websites for a multiethnic, public hospital oncology population: Comparative cross-sectional survey. J Med Internet Res 7:e28, 2005[Medline] 16. Seale C: New directions for critical internet health studies: Representing cancer experience on the web. Sociol Health Illn 27:515-540, 2005[CrossRef][Medline] 17. Unruh HK, Bowen DJ, Meischke H, et al: Women's approaches to the use of new technology for cancer risk information. Women Health 40:59-78, 2004[CrossRef][Medline] 18. Teo NB, Paton P, Kettlewell S: Use of an interactive web-based questionnaire to evaluate a breast cancer website. Breast 14:153-156, 2005[CrossRef][Medline] 19. Helft PR: Breast cancer in the information age: A review of recent developments. Breast Dis 21:41-46, 2004[Medline] 20. Friedman DB, Hoffman-Goetz L, Arocha JF: Health literacy and the World Wide Web: Comparing the readability of leading incident cancers on the Internet. Med Inform Internet Med 31:67-87, 2006[CrossRef][Medline] 21. Van Zee KJ, Manasseh DM, Bevilacqua JL, et al: A nomogram for predicting the likelihood of additional nodal metastases in breast cancer patients with a positive sentinel node biopsy. Ann Surg Oncol 10:1140-1151, 2003[CrossRef][Medline] 22. Rivadeneira DE, Simmons RM, Christos PJ, et al: Predictive factors associated with axillary lymph node metastases in T1a and T1b breast carcinomas: Analysis in more than 900 patients. J Am Coll Surg 191:1-6, 2000[CrossRef][Medline] 23. Fehr MK, Kochli OR, Helfenstein U, et al: Multivariate analysis of clinico-pathologic predictors of axillary lymph node metastasis in invasive breast carcinoma [German]. Geburtshilfe Frauenheilkd 55:182-188, 1995[Medline] 24. Jackson JS, Olivotto IA, Wai MDE, et al: A decision analysis of the effect of avoiding axillary lymph node dissection in low risk women with invasive breast carcinoma. Cancer 88:1852-1862, 2000[CrossRef][Medline] 25. Maibenco DC, Weiss LK, Pawlish KS, et al: Axillary lymph node metastases associated with small invasive breast carcinomas. Cancer 85:1530-1536, 1999[CrossRef][Medline] 26. Mustafa IA, Cole B, Wanebo HJ, et al: The impact of histopathology on nodal metastases in minimal breast cancer. Arch Surg 132:384-390, 1997 27. Olivotto IA, Jackson JS, Mates D, et al: Prediction of axillary lymph node involvement of women with invasive breast carcinoma: A multivariate analysis. Cancer 83:948-955, 1998[CrossRef][Medline] 28. Port ER, Tan LK, Borgen PI, et al: Incidence of axillary lymph node metastases in T1a and T1b breast carcinoma. Ann Surg Oncol 5:23-27, 1998[CrossRef][Medline] 29. Voogd AC, Coebergh JW, Repelaer van Driel OJ, et al: The risk of nodal metastases in breast cancer patients with clinically negative lymph nodes: A population-based analysis. Breast Cancer Res Treat 62:63-69, 2000[CrossRef][Medline] 30. Cserni G: Metastases in axillary sentinel lymph nodes in breast cancer as detected by intensive histopathological work up. J Clin Pathol 52:922-924, 1999[Abstract] 31. Czerniecki BJ, Scheff AM, Callans LS, et al: Immunohistochemistry with pancytokeratins improves the sensitivity of sentinel lymph node biopsy in patients with breast carcinoma. Cancer 85:1098-1103, 1999[CrossRef][Medline] 32. Dowlatshahi K, Fan M, Bloom KJ, et al: Occult metastases in the sentinel lymph nodes of patients with early stage breast carcinoma: A preliminary study. Cancer 86:990-996, 1999[CrossRef][Medline] 33. Pendas S, Dauway E, Cox CE, et al: Sentinel node biopsy and cytokeratin staining for the accurate staging of 478 breast cancer patients. Am Surg 65:500-505, 1999[Medline] 34. Turner RR, Ollila DW, Krasne DL, et al: Histopathologic validation of the sentinel lymph node hypothesis for breast carcinoma. Ann Surg 226:271-276, 1997[CrossRef][Medline] 35. Weaver DL, Krag DN, Ashikaga T, et al: Pathologic analysis of sentinel and nonsentinel lymph nodes in breast carcinoma: A multicenter study. Cancer 88:1099-1107, 2000[CrossRef][Medline] 36. Dowlatshahi K, Fan M, Snider HC, et al: Lymph node micrometastases from breast carcinoma: Reviewing the dilemma. Cancer 80:1188-1197, 1997[CrossRef][Medline] 37. Cote RJ, Peterson HF, Chaiwun B, et al: Role of immunohistochemical detection of lymph-node metastases in management of breast cancer. International Breast Cancer Study Group. Lancet 354:896-900, 1999[CrossRef][Medline] 38. Kattan MW, Giri D, Panageas KS, et al: A tool for predicting breast carcinoma mortality in women who do not receive adjuvant therapy. Cancer 101:2509-2515, 2004[CrossRef][Medline] 39. Liberman L: Pathologic analysis of sentinel lymph nodes in breast carcinoma. Cancer 88:971-977, 2000[CrossRef][Medline] 40. Tan LK, Giri D, Pangeas K, et al: Occult micrometastases in axillary lymph nodes of breast cancer patients are significant: A retrospective study with long term follow up. Proc Am Soc Clin Oncol 21:37A, 2002 (abstr 146) 41. Maibenco DC, Dombi GW, Kau TY, et al: Impact of micrometastases on the long-term survival of women with T1 breast cancer. Breast Cancer Res Treat 88:S78, 2004 (suppl 1; abstr 2006) 42. de Mascarel I, MacGrogan G, Picot V, et al: Prognostic significance of immunohistochemically detected breast cancer node metastases in 218 patients. Br J Cancer 87:70-74, 2002[CrossRef][Medline] 43. Millis RR, Springall R, Lee AH, et al: Occult axillary lymph node metastases are of no prognostic significance in breast cancer. Br J Cancer 86:396-401, 2002[CrossRef][Medline] 44. Ravdin PM, De Laurentiis M, Vendely T, et al: Prediction of axillary lymph node status in breast cancer patients by use of prognostic indicators. J Natl Cancer Inst 86:1771-1775, 1994 45. Gann PH, Colilla SA, Gapstur SM, et al: Factors associated with axillary lymph node metastasis from breast carcinoma: Descriptive and predictive analyses. Cancer 86:1511-1519, 1999[CrossRef][Medline] 46. Viale G, Zurrida S, Maiorano E, et al: Predicting the status of axillary sentinel lymph nodes in 4351 patients with invasive breast carcinoma treated in a single institution. Cancer 103:492-500, 2005[CrossRef][Medline] 47. Bevilacqua JL, Gucciardo G, Cody HS, et al: A selection algorithm for internal mammary sentinel lymph node biopsy in breast cancer. Eur J Surg Oncol 28:603-614, 2002[CrossRef][Medline] 48. Estourgie SH, Tanis PJ, Nieweg OE, et al: Should the hunt for internal mammary chain sentinel nodes begin? An evaluation of 150 breast cancer patients. Ann Surg Oncol 10:935-941, 2003[CrossRef][Medline] 49. Galimberti V, Veronesi P, Arnone P, et al: Stage migration after biopsy of internal mammary chain lymph nodes in breast cancer patients. Ann Surg Oncol 9:924-928, 2002[Medline] 50. Davis PL, Staiger MJ, Harris KB, et al: Breast cancer measurements with magnetic resonance imaging, ultrasonography, and mammography. Breast Cancer Res Treat 37:1-9, 1996[CrossRef][Medline] 51. Fiorentino C, Berruti A, Bottini A, et al: Accuracy of mammography and echography versus clinical palpation in the assessment of response to primary chemotherapy in breast cancer patients with operable disease. Breast Cancer Res Treat 69:143-151, 2001[CrossRef][Medline] 52. Fischer U, Kopka L, Grabbe E: Breast carcinoma: Effect of preoperative contrast-enhanced MR imaging on the therapeutic approach. Radiology 213:881-888, 1999 53. Liberman L, Mason G, Morris EA, et al: Does size matter? Positive predictive value of MRI-detected breast lesions as a function of lesion size. Am J Roentgenol 186:426-430, 2006 54. Malur S, Wurdinger S, Moritz A, et al: Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography. Breast Cancer Res 3:55-60, 2001[CrossRef][Medline] 55. Menell JH, Morris EA, Dershaw DD, et al: Determination of the presence and extent of pure ductal carcinoma in situ by mammography and magnetic resonance imaging. Breast J 11:382-390, 2005[CrossRef][Medline] 56. Pavic D, Koomen MA, Kuzmiak CM, et al: The role of magnetic resonance imaging in diagnosis and management of breast cancer. Technol Cancer Res Treat 3:527-541, 2004[Medline] 57. Van Goethem M, Schelfout K, Dijckmans L, et al: MR mammography in the pre-operative staging of breast cancer in patients with dense breast tissue: Comparison with mammography and ultrasound. Eur Radiol 14:809-816, 2004[CrossRef][Medline] 58. Wright H, Listinsky J, Quinn C, et al: Increased ipsilateral whole breast vascularity as measured by contrast-enhanced magnetic resonance imaging in patients with breast cancer. Am J Surg 190:576-579, 2005[CrossRef][Medline] 59. Yang WT, Lam WW, Cheung H, et al: Sonographic, magnetic resonance imaging, and mammographic assessments of preoperative size of breast cancer. J Ultrasound Med 16:791-797, 1997[Abstract] 60. Zuiani C, Francescutti GE, Londero V, et al: Ductal carcinoma in situ: Is there a role for MRI? J Exp Clin Cancer Res 21:89-95, 2002[Medline] 61. Pisano ED, Gatsonis C, Hendrick E, et al: Diagnostic performance of digital versus film mammography for breast cancer screening. N Engl J Med 353:1773-1783, 2005 Submitted September 5, 2006; accepted May 17, 2007.
Related Articles
This article has been cited by other articles:
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|
|
|||||||||||
|
|||||||||||
|
|
Copyright © 2007 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
|
TOP
ABSTRACT
INTRODUCTION
