The New Era in Breast Cancer

Cady, Blake; Stone, Michael D.; Schuler, John G.; Thakur, Ravi; Wanner, Molly; Lavin, Philip T.

doi:10.1001/archsurg.1996.01430150079015

Cited by 337 publications

(27 citation statements)

References 28 publications

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“…The increased utilization of and improvement in screening mammography programs have led to an increase in the number of nonpalpable imaging-detected breast cancers [1, 2]. The standard technique for preoperative localization of nonpalpable lesions uses a needle-wire assembly (wire localization [WL]) under mammographic, stereotactic, sonographic, or MRI guidance.…”

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confidence: 99%

Imaging Factors That Influence Surgical Margins After Preoperative ¹²⁵I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Dryden

Dogan

Fox

et al. 2016

American Journal of Roentgenology

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Objective The objective of this study was to compare the potential Influence of imaging variables on surgical margins after preoperative radioactive seed localization (RSL) and wire localization (WL) techniques. Materials and Methods A total of 565 women with 660 breast lesions underwent RSL or WL between May 16, 2012, and May 30, 2013. Patient age, lesion type (mass, calcifications, mass with associated calcifications, other), lesion size, number of seeds or wires used, surgical margin status (close positive or negative margins), and reexcision and mastectomy rates were recorded. Results Of 660 lesions, 127 (19%) underwent RSL and 533 (81%) underwent WL pre-operatively. Mean lesion size was 1.8 cm in the RSL group and 1.8 cm in the WL group (p = 0.35). No difference in lesion type was identified in the RSL and WL groups (p = 0.63). RSL with a single seed was used in 105 of 127 (83%) RSLs compared with WL with a single wire in 349 of 533 (65%) WLs (p = 0.0003). The number of cases with a close positive margin was similar for RSLs (26/127, 20%) and WLs (104/533, 20%) (p = 0.81). There was no difference between the RSL group and the WL group in close positive margin status (20% each, p = 0.81), reexcision rates (20% vs 16%, respectively; p = 0.36), or mastectomy rates (6% each, p = 0.96). Lesions containing calcifications were more likely to require more than one wire (odds ratio [OR], 4.44; 95% CI, 2.8–7.0) or more than one seed (OR, 7.03; 95% CI, 1.6–30.0) when compared with masses alone (p < 0.0001). Increasing lesion size and the presence of calcifications were significant predictors of positive margins, whereas the use of more than one wire or seed was not (OR, 0.9; 95% CI, 0.5–1.5) (p = 0.75). Conclusion Close positive margin, reexcision, and mastectomy rates remained similar in the WL group and RSL group. The presence of calcifications and increasing lesion size increased the odds of a close positive margin in both the WL and RSL groups, whereas the use of one versus more than one seed or wire did not.

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confidence: 99%

Imaging Factors That Influence Surgical Margins After Preoperative ¹²⁵I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Dryden

Dogan

Fox

et al. 2016

American Journal of Roentgenology

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“…However, a much debated question is whether axillary lymph node dissection is indicated if the sole purpose is to gain prognostic information, especially in view of the decreasing incidence of lymph node metastases [12, 16]. When information on nodal status was excluded from the neural network model in the present study and the model was based solely on the histopathologic features of the primary tumor and age, it still predicted 5-year survival with a significantly higher accuracy than nodal status alone.…”

Section: Discussionmentioning

confidence: 99%

“…The decreasing incidence of lymph node and distant metastases at the time of diagnosis in patients with breast cancer [12]will reduce the value of the TNM staging system in survival prediction. In this context, the primary tumor characteristics may become the major determinants in making therapy decisions and judgements regarding prognosis [12].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, the primary tumor characteristics may become the major determinants in making therapy decisions and judgements regarding prognosis [12]. Attempts have been made to create models that based on the primary tumor features could predict lymph node status and identify those patients in whom an axillary nodal dissection would be indicated [13, 14, 15].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

Lundin

Lundin²,

Burke³

et al. 1999

Oncology

130

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In this study, we evaluated the accuracy of a neural network in predicting 5-, 10- and 15-year breast-cancer-specific survival. A series of 951 breast cancer patients was divided into a training set of 651 and a validation set of 300 patients. Eight variables were entered as input to the network: tumor size, axillary nodal status, histological type, mitotic count, nuclear pleomorphism, tubule formation, tumor necrosis and age. The area under the ROC curve (AUC) was used as a measure of accuracy of the prediction models in generating survival estimates for the patients in the independent validation set. The AUC values of the neural network models for 5-, 10- and 15-year breast-cancer-specific survival were 0.909, 0.886 and 0.883, respectively.The corresponding AUC values for logistic regression were 0.897, 0.862 and 0.858. Axillary lymph node status (N0 vs. N+) predicted 5-year survival with a specificity of 71% and a sensitivity of 77%. The sensitivity of the neural network model was 91% at this specificity level. The rate of false predictions at 5 years was 82/300 for nodal status and 40/300 for the neural network. When nodal status was excluded from the neural network model, the rate of false predictions increased only to 49/300 (AUC 0.877). An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction. The consistently high accuracy over time and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.

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“…An example of this became evident recently when data were analyzed for a study of trends in breast carcinoma size, stage and nodal status as a result of mammographic screening [4]. Cases from the New England Deaconess Hospital (NEDH), a largely tertiary teaching hospital in Boston, Massachusetts and Mount Auburn Hospital (MAH), a community teaching hospital in Cambridge, Massachusetts were compared.…”

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confidence: 99%

The surgical pathology report: Standardizing the “gold standard”

Rushing¹,

Joste²

1997

J. Surg. Oncol.

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The New Era in Breast Cancer

Cited by 337 publications

References 28 publications

Imaging Factors That Influence Surgical Margins After Preoperative ¹²⁵I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Imaging Factors That Influence Surgical Margins After Preoperative ¹²⁵I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

The surgical pathology report: Standardizing the “gold standard”

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The New Era in Breast Cancer

Cited by 337 publications

References 28 publications

Imaging Factors That Influence Surgical Margins After Preoperative 125I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Imaging Factors That Influence Surgical Margins After Preoperative 125I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Artificial Neural Networks Applied to Survival Prediction in Breast Cancer

The surgical pathology report: Standardizing the “gold standard”

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Product

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Imaging Factors That Influence Surgical Margins After Preoperative ¹²⁵I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization

Imaging Factors That Influence Surgical Margins After Preoperative ¹²⁵I Radioactive Seed Localization of Breast Lesions: Comparison With Wire Localization