Multifocality and Multicentrality in Breast Cancer: Comparison of the Efficiency of Mammography, Contrast-Enhanced Spectral Mammography, and Magnetic Resonance Imaging in a Group of Patients with Primarily Operable Breast Cancer

Steinhof-Radwańska, Katarzyna; Lorek, Andrzej; Holecki, Michał; Barczyk-Gutkowska, Anna; Grażyńska, Anna; Szczudło-Chraścina, Joanna; Bożek, Oskar; Habas, Justyna; Szyluk, Karol; Niemiec, Paweł; Gisterek, Iwona

doi:10.3390/curroncol28050341

Cited by 22 publications

(22 citation statements)

References 50 publications

(57 reference statements)

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“…When CEM or MRI changed the Mock‐MDT's surgical plan, a more extensive excision was usually proposed. The change in the mastectomy rate in this study was similar to the 10.7%–14.3% increase noted by Kim et al ., 14 and much less than the 28.5% increase found in the study by Steinhof‐Radwanska et al 10 . where MRI was only offered to women meeting the EUSOMA recommendations.…”

Section: Discussionsupporting

confidence: 86%

Interpreting contrast imaging to plan breast surgery

Taylor

Hobbs

Ronald

et al. 2023

ANZ Journal of Surgery

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BackgroundContrast enhanced mammography (CEM) and magnetic resonance imaging (MRI) are more accurate than conventional imaging (CI) for breast cancer staging. How adding CEM and MRI to CI might change the surgical plan is understudied.MethodsSurgical plans (breast conserving surgery (BCS), wider BCS, BCS with diagnostic excision (>1BCS), mastectomy) were devised by mock‐MDT (radiologist, surgeon and pathology reports) according to disease extent on CI, CI + CEM and CI + MRI. Differences in the mock‐MDT's surgical plans following the addition of CEM or MRI were investigated. Using pre‐defined criteria, the appropriateness of the modified plans was assessed by comparing estimated disease extent on imaging with final pathology. Surgery performed was recorded from patient records.ResultsContrast imaging modified mock‐MDT plans for 20 of 61(32.8%) breasts. The addition of CEM changed the plan in 16/20 (80%) and MRI in 17/20 breasts (85%). Identical changes were proposed by both CEM and MRI in 13/20 (65%) breasts. The modified surgical plan based on CI + CEM was possibly appropriate for 6/16 (37.5%), and CI + MRI in 9/17, (52.9%) breasts. The surgery performed was concordant with the mock‐MDT plan for all 10 patients where the plans could be compared (BCS 1, >1 BCS 2 and mastectomy 7).ConclusionAdding CEM or MRI to CI changed mock‐MDT plans in up to one third of women, but not all were appropriate. Changing surgical plans following addition of contrast imaging to CI without biopsy confirmation could lead to over or under‐treatment.

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Section: Discussionsupporting

confidence: 86%

Interpreting contrast imaging to plan breast surgery

Taylor

Hobbs

Ronald

et al. 2023

ANZ Journal of Surgery

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“…Author details 1 Radiology Department, Women's Imaging Unit, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt. 2 Pathology Department, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt. 3…”

Section: Author Contributionsmentioning

confidence: 99%

“…Tissue superimposition may mimic or mask an underlying lesion. This is especially evident in premenopausal women who have dense breasts [2].Recent advances in AI hold a great promise not only for detection of breast cancer, but for risk assessment, diagnosis, prognosis and monitoring response to therapy [3]. AI can be used as an independent preliminary reader of mammographic images, with near human reader accuracy.…”

Section: Introductionmentioning

confidence: 99%

Does artificial intelligence aid in the detection of different types of breast cancer?

Raafat

Mansour

Kamel

et al. 2022

Egypt J Radiol Nucl Med

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Background On mammography many cancers may be missed even in retrospect either due to the breast density, the small size of the tumor or the subtle signs of cancer that are imperceptible. We aimed to compare the sensitivity of artificial intelligence (AI) to that of digital mammography in the detection of different types of breast cancer. Also, the sensitivity of AI in picking up the different breast cancer morphologies namely mass, pathological calcifications, asymmetry, and distortion was assessed. Tissue biopsy and pathology were used as the standard reference. The study included 123 female patients with 134 proved carcinoma. All patients underwent digital mammogram (DM) examination scanned with artificial intelligence algorithm. Results AI achieved higher sensitivity than mammography in detecting malignant breast lesions. The sensitivity of AI was 96.6%, and false negative rate was 3.4%, while mammography sensitivity was 87.3% and false negative rate 12.7%. Our study showed AI performed better than mammography in detecting ductal carcinoma in situ and invasive lobular carcinoma with sensitivity (100% and 96.6%) vs (88.9% and 82.2%) respectively. AI was more sensitive to detect cancers presented with suspicious mass 95.2% vs 75%, suspicious calcifications 100% vs 86.5% and asymmetry and distortion 100% vs 84.6%, than mammography. Conclusions AI showed potential values to overcome mammographic limitations in the detection of breast cancer even those with challenging morphology as invasive lobular carcinoma, ductal carcinoma in situ, tubular carcinoma and micropapillary carcinoma.

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“…This may indicate that the uncertainty in the LVI model’s predictions was still too large for the LVI model to be of benefit when predicting N status. Nevertheless, the reliability and/or distribution of data, such as multifocality, may change in the preoperative setting [40], which could change the prerequisites for predicting LVI status. Given the growing evidence on the significance of LVI status as a predictor of axillary N status [7, 11, 22, 41], further evaluation of the presented LVI model is warranted.…”

Section: Discussionmentioning

confidence: 99%

Noninvasive Staging of Lymph Node Status (NILS) in Breast Cancer using Machine Learning: External Validation and Further Model Development

Hjärtström

Dihge

Bendahl

et al. 2023

Preprint

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Background:Most patients diagnosed with breast cancer present with node–negative disease. Sentinel lymph node biopsy (SLNB) is routinely used to stage the axilla, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal (N) status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of N metastasis; however, it is challenging to assess preoperatively.Objective:To externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based register cohort (n=18 633) while developing a new N MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC), 2014–2017, comprising only routinely and preoperatively available documented clinicopathological variables. Furthermore, we aimed to develop and validate an LVI MLP to predict missing values of LVI status to increase the preoperative feasibility of the original NILS model.Methods:Three non-overlapping cohorts were used for model development and validation. Four N MLPs and one LVI MLP were developed using 11–12 routinely available predictors. Three N models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth N model was developed for 80% of NKBC cases (n=14 906) and validated in the remaining 20% (n=3727). Three alternatives for imputing missing values of the LVI status were compared using the LVI model. The discriminatory capacity was evaluated by validation area under the receiver operating characteristics curve (AUC) in three of the N models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.Results:External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI: 0.690–0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI: 0.694–0.799) with good calibration but did not improve the discriminatory performance of the N models. The new N model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688–0.729) with excellent calibration in the hold-out internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNB.Conclusions:The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model′s discriminatory performance. A new N model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images.

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Multifocality and Multicentrality in Breast Cancer: Comparison of the Efficiency of Mammography, Contrast-Enhanced Spectral Mammography, and Magnetic Resonance Imaging in a Group of Patients with Primarily Operable Breast Cancer

Cited by 22 publications

References 50 publications

Interpreting contrast imaging to plan breast surgery

Interpreting contrast imaging to plan breast surgery

Does artificial intelligence aid in the detection of different types of breast cancer?

Noninvasive Staging of Lymph Node Status (NILS) in Breast Cancer using Machine Learning: External Validation and Further Model Development

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