ESR Connect

    Research Presentation Session: Breast

    RPS 602 - The role of breast imaging in breast cancer treatment

    February 28, 16:30 - 17:30 CET

    • ACV - Research Stage 1
    • ECR 2024
    • 7 Lectures
    • 60 Minutes
    • 7 Speakers
    • 1 Comment

    Description

    7 min
    Comparison of radiomics-based machine-learning classifiers for pretreatment prediction of pathologic complete response to neoadjuvant therapy in breast cancer
    Xue Li, Beijing / China
      Author Block: X. Li, C. Li, L. Jiang, M. Chen; Beijing/CNPurpose: In recent years, machine learning (ML) classifiers have been used to establish high-performance predictive models for pathological complete response (pCR) in breast cancer after neoadjuvant therapy (NAT). However, few studies have compared the effectiveness of different ML classifiers. This study investigated the ability of radiomics models based on pre- and post-contrast first-phase T1WI to predict breast cancer pCR after NAT and compared the performance of different ML classifiers.Methods or Background: In this retrospective study, 300 patients from the Duke-Breast-Cancer-MRI dataset who underwent NAT were included, including pCR (n=76) and non-pCR (n=224) cases. These patients were randomly divided into training and validation groups at a ratio of 8:
    1. Radiomics features were extracted from pre- and post-contrast first-phase T1WI images of each patient. The radiomics model was built using features selected through the Spearman correlation analysis and the LASSO algorithm after normalisation. Seven ML classifiers were used to assess the predictive performance of the radiomics models.
      2. Results or Findings: Out of the seven classifiers used, the LightGBM classifier performed best in predicting breast cancer pCR, with an AUC of
    2. 813 in the validation group (accuracy 78.3%, sensitivity 46.7%, specificity 100.0%). During subgroup analysis, RF achieved the highest AUC in pCR prediction in luminal breast cancers (0.859, accuracy 85.9%, sensitivity 68.8%, and specificity 83.3%), and DT yielded the highest AUC in pCR prediction in triple negative (TN) breast cancers (0.909, accuracy 88.2%, sensitivity 81.8%, and specificity 100%).
      3. Conclusion: Overall, the LightGBM-based radiomics model demonstrated superior performance in predicting breast cancer pCR, while RF and DT displayed promising results in predicting pCR for luminal and TN breast cancers, respectively, during subgroup analysis.Limitations: Our study included different NAT treatment regimens, and subgroup analysis based on treatment regimens was not performed.Funding for this study: No funding was received for this study.Has your study been approved by an ethics committee? Not applicableEthics committee - additional information: Data obtained from the Cancer Imaging Archive (TCIA, https://www.cancerimagingarchive.net/) did not require ethical approval; informed consent was waived since the TCIA dataset de-identified patient information.
    7 min
    Ultrasound guided vacuum-assisted biopsy to assess pathological complete response to neoadjuvant therapy: a paradigm shift in breast cancer treatment
    Elisa D’Ascoli, Rome / Italy
      Author Block: E. D’Ascoli1, C. Depretto1, G. Della Pepa1, C. De Berardinis1, G. Irmici1, C. Cazzella2, D. Ballerini1, A. Bonanomi1, G. P. Scaperrotta1; 1Milan/IT, 2Bergamo/ITPurpose: The aim of this study was to evaluate the diagnostic performance of a pre-surgical mini-invasive ultrasound-guided biopsy to predict pathological complete response (pCR) in breast cancer (BC) patients after neoadjuvant therapy (NAT) in order to assess the possibility of omitting surgery in exceptional responders.Methods or Background: We enrolled patients with histologically confirmed TN, Her2+ and Luminal B cT1-cT2-cT3 cN0-cN1 monofocal BC who received NAT with complete radiological remission on imaging (ultrasound, mammography, MRI and CEM) or with breast residual tumor <1cm. Patients underwent ultrasound guided vacuum-assisted breast biopsy (VABB) and the results were compared with the final histological results obtained after surgery.Results or Findings: We enrolled 27 patients; 15/27 lesions were classified as TN, 6/27 Her2+ and 6/27 Luminal B. 22/27 cases showed complete radiological response; 5/27 had residual tumor <1cm. In two cases, VABB identified residual disease and post-surgical histological examination was negative. Overall, among the remaining 25 cases, in 22 (88%) there was concordance between the VABB result and the final histological examination. Among TN and Her2+ concordance was observed in 17/19 (
    1. 5%) cases. VABB did not identify the presence of residual invasive disease in one case of Luminal B (16.7%) and in one case of TN (6.7%); in one TN case (6.7%) VABB was negative but definitive histological examination showed the presence of carcinoma in situ.
      2. Conclusion: pCR rates are rapidly improving, especially in TN and Her2+ BC patients. Reliably identifying patients with pCR could lead to deescalation of locoregional therapy after NAT, allowing surgery to be omitted in exceptional responders; this would result in a reduction of post-surgical complications and healthcare costs, and improvement in quality of life.Limitations: The main limitations are the single-centre nature of the study and the limited number of patients.Funding for this study: No funding was received for this study.Has your study been approved by an ethics committee? YesEthics committee - additional information: This study was approved by the Independent Ethics Committee at the IRCCS Istituto Nazionale dei Tumori, Milano.
    7 min
    Unenhanced MRI for assessment of response to neoadjuvant therapy in patient with locally advanced breast cancer: diagnostic value of DWI/ADC
    Marcella Pasculli, Rome / Italy
      Author Block: M. Pasculli, F. Galati, V. Rizzo, G. Moffa, R. Maroncelli, F. Cicciarelli, F. Pediconi, C. Catalano; Rome/ITPurpose: The aim of this study was to investigate the predictive value of DWI/ADC (DW-MRI) for the assessment of response to neoadjuvant therapy (NT) in patients with locally advanced breast cancer (LABC).Methods or Background: Patients with LABC candidate to NT, and who underwent pre-treatment breast MRI between March 2021 to March 2022, were retrospectively enrolled. MRI-based staging and DWI/ADC values (x10-3mm2/s) were analysed. According to post-surgical outcomes, patients were classified as complete responders (pCR) and non-complete responders (non-pCR). Pre-treatment ADC values were compared to the tumour's pathological outcome and post-treatment downstaging. The diagnostic accuracy of DWI-ADC in differentiating between pCR and non-pCR groups was calculated with receiver operating characteristic (ROC) analysis.Results or Findings: 36 patients were evaluated (pCR, n=20; non-pCR, n=16). Pre-treatment lesion ADC values were significantly different between the two groups (p=
    1. 034), while no association was found between pre-NT tumour size and pathological response. ADC values pre-teatment showed significant correlations with loco-regional downstaging after therapy (r=-0.537, p=0.022) and with tumour volume reduction (r=-0.480, p=0.044). ADC values could differentiate pCR from non-pCR patients, with a sensitivity of 75% and specificity of 70%.
      2. Conclusion: ADC values on pre-treatment MRI were strongly associated with the outcome in patients with LABC, both in terms of pathological response and loco-regional downstaging after NT, suggesting the use of Unenhanced DW-MRI as a potential predictive tool of response to therapy.Limitations: This was a single-centre study with a limited number of patients.Funding for this study: No funding was received.Has your study been approved by an ethics committee? Not applicableEthics committee - additional information: Informed consent was waived due to the retrospective nature of this study, as approved by our local Ethical Committee.
    7 min
    Performance of the node-RADS scoring system for standardised magnetic resonance imaging assessment of lymph nodes in breast cancer
    Roberto Maroncelli, Rome / Italy
      Author Block: R. Maroncelli, F. Pediconi, M. Pasculli, A. Marra, F. Cicciarelli, V. Rizzo, G. Moffa, F. Galati, C. Catalano; Rome/ITPurpose: The Node-RADS score was recently introduced to offer a standardised and comprehensive evaluation of lymph node invasion (LNI) based on a five-item Likert scale. We tested Node-RADS score diagnostic performance and assessed the applicability and feasibility of the score among readers.Methods or Background: A retrospective study was conducted on BC patients who underwent lymph node dissection between January 2020 and January
    1. All patients underwent breast contrast-enhanced magnetic resonance imaging. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for different Node-RADS cut-off values (>1, >2, >3, >4) in predicting LNI. Pathologic results were considered the gold standard. Additionally, the overall diagnostic performance was evaluated using ROC curves and the Area Under the Curve (AUC). Finally, Cohen’s K analysis was used to assess interreader agreement.
      2. Results or Findings: The final population includes 192 patients. By increasing the Node-RADS cut-off values, specificity and PPV rose from
    2. 4% to 100% and 76.7% to 100%, respectively for Reader 1 and 69.4% to 100% and 74.6% to 100% for Reader 2. Node-RADS >2 could be considered the best cut-off value due to its balanced performance. Node-RADS exhibited an AUC of 0.97 for Reader 1 and 0.93 for Reader 2. Node-RADS assigned on CE-MRI images independently predict LNI after adjusting for other variables in a multivariable regression analysis (p<0.001 for both Readers). An excellent interreader agreement was found (K=0.834).
      3. Conclusion: The current study establishes the groundwork for implementing Node-RADS as a method for assessing regional lymph nodes in BC patients. The Node-RADS score has demonstrated moderate-to-high overall accuracy in identifying LNI, providing the flexibility to establish different cut-off values based on specific clinical scenarios.Limitations: This study was based on a relatively limited cohort.Funding for this study: No funding was received for this study.Has your study been approved by an ethics committee? YesEthics committee - additional information: Informed consent was waived due to the retrospective nature of our study, and approved by the local Ethical Committee.
    7 min
    Incorporating breast cancer molecular subtype rather than axillary disease extent on baseline 18F-FDG PET/CT in axillary treatment strategies
    Thiemo van Nijnatten, Maastricht / Netherlands
      Author Block: F. v. Amstel1, C. De Mooij1, J. Simons2, C. Mitea1, C. Van Der Pol3, E. Luiten4, L. Koppert2, M. Smidt1, T. van Nijnatten1; 1Maastricht/NL, 2Rotterdam/NL, 3Leiderdorp/NL, 4Al Ain/AEPurpose: In clinically node-positive (cN+) patients treated with neoadjuvant systemic therapy (NST), axillary disease extent on baseline 18F-FDG PET/CT combined with pathologic axillary response to NST has been proposed to guide axillary treatment de-escalation. This study aimed to assess how breast cancer molecular subtype would affect such a treatment strategy.Methods or Background: Patients with cN+ breast cancer treated with NST in the RISAS trial (NCT02800317) who underwent 18F-FDG PET/CT at baseline were included (period 2017-2019). Baseline 18F-FDG PET/CT exams were centrally reviewed to differentiate between limited (1-3) and advanced axillary disease (≥4 hypermetabolic axillary lymph nodes). After NST, all patients underwent the RISAS-procedure followed by completion axillary lymph node dissection. Axillary pathologic complete response (pCR) rates were stratified by axillary disease extent on baseline 18F-FDG PET/CT, and subsequently by hormone receptor (HR)+/HER2-, HR+/HER2+, HR-/HER2+ and triple negative (TN) molecular subtypes.Results or Findings: A total of 185 patients were included,
    1. 7% with limited and 37.3% with advanced baseline axillary disease. Overall axillary pCR rate was 29.7% (7% for HR+/HER2-, 52.6% for HR+/HER2+, 75% for HR-/HER2+, and 34.1% for TN; p<0.001). Axillary pCR rates did not significantly differ between limited versus advanced baseline axillary disease within the molecular subtypes. Breast molecular subtype showed to be a significant predictor of axillary response.
      2. Conclusion: Axillary pCR rates between limited and advanced axillary disease on baseline 18F-FDG PET/CT were not significantly different within each of the breast molecular subtypes. Breast molecular subtype is important here since it showed to be a significant predictor of axillary response. Therefore, baseline axillary disease extent should be given a less important status while breast molecular subtype should be considered important to guide axillary treatment strategies in cN+ patients treated with NST.Limitations: No limitations were identifiedFunding for this study: Funding was received from the University Fund Limburg (SWOL; project
    2. 048) and Dutch Cancer Society (KWF – REFINE-trial; project 14055).
      3. Has your study been approved by an ethics committee? YesEthics committee - additional information: Due to the retrospective design of the study, the necessity to obtain written informed consent was waived by the local medical ethics committee.
    7 min
    MRI characteristics predicting recurrence/metastases in breast cancer patients receiving neoadjuvant chemotherapy
    Aisha Syed, Cardiff / United Kingdom
      Author Block: A. Syed, J. Bansal, M. Wallace; Cardiff/UKPurpose: The aim of this study was to evaluate MRI and tumour characteristics predicting recurrence or metastases in breast cancer patients post neoadjuvant chemotherapy ( NACT).Methods or Background: A retrospective evaluation of all breast MRIs for NACT monitoring between 2009 and 2018 was performed. All patients were followed up for at least five years. Factors including patients’ age, tumour size, receptor status, number of lymph nodes, and MRI characteristics were evaluated. SPSS was used for statistical evaluation, and p<
    1. 05 was considered a significant result. Binomial logistic regression was used to evaluate factors, controlling for other variables. The median age of patients was 45 years (range 25 to 73).
      2. Results or Findings: Out of 135 patients, 114 had adequate data for evaluation in this study. Thirty-three (
    2. 9%) patients showed local recurrence or metastases. The median time to an event from the date of diagnosis was 35 months (range 0-144 months). Compared to a non-mass enhancement, a mass-like enhancement was statistically associated with fewer events (p=0.011). The factors most significantly associated with an event were triple negative (TN) status, a higher number of lymph nodes on baseline MRI, and post-surgery (ypN). No significant association was found between T stage, tumour grade or MR response pattern (concentric versus crumbling).
      3. Conclusion: Of all breast cancer patients receiving NACT,
    3. 9% showed an adverse event at a median of 35 months. Factors predicting an event in breast cancer patients receiving NACT were TN receptor status, non-mass enhancement on MRI, and higher lymph node status.
      4. Limitations: This was a small retrospective study.Funding for this study: No funding was received for this study.Has your study been approved by an ethics committee? Not applicableEthics committee - additional information: This was a retrospective study of imaging, therefore no ethics approval was required.
    7 min
    Baseline MRI BI-RADS and breast oedema score features as predictors for axillary lymph node response to neoadjuvant chemotherapy in breast cancer
    Caroline Malhaire, Paris / France
    Author Block: C. Malhaire1, O. Umay1, F. Frouin2; 1Paris/FR, 2Orsay/FR
    Purpose: The aim of this study was to assess the association between pre-treatment breast MRI features and axillary lymph node residual disease in women treated for node-positive breast cancer by neoadjuvant chemotherapy (NAC).
    Methods or Background: In this single-centre, retrospective study, women with node-positive breast cancer who underwent NAC and pre-treatment breast MRI between 2016 and 2021 were included. MRIs were evaluated using the standardised BI-RADS and T2-weighted Breast Oedema Score. Univariate analysis and multivariate logistic regression analysis were conducted to evaluate clinicopathological and MRI variables association with lymph node residual disease. A prediction model was developed from the logistic regression analysis and evaluated on a randomly split train and test set (7:3 ratio).
    Results or Findings: Of 142 breast cancers, 59% achieved post-NAC nodal response, varying by subtype: luminal 24%, HER2+ 69%, and TN 75%. Factors associated with nodal response were TN and HER2+ subtypes, high Ki67, and tumour-infiltrating lymphocytes. Univariate analysis identified MRI features like anterior third location, indicating the depth of the tumour within the breast, and irregular shape as significant for residual axillary disease. In multivariate analysis, the anterior location and the absence of intratumoural T2 hyperintensity remained significant. Adding MRI features to anatomopathological variables enhanced nodal residual disease prediction models.
    Conclusion: Luminal subtypes, low Ki67 levels, anterior third location, and lack of intratumoural T2 hyperintensity are independently associated with axillary residual disease and provide additional predictive value to predict lymph node residual disease after NAC
    Limitations: Study limitations include a single-centre design, a retrospective nature, and a limited sample size for histomolecular subtype subgroup analysis.
    Funding for this study: No funding was received.
    Has your study been approved by an ethics committee? Yes
    Ethics committee - additional information: This study was approved by the relevant Institutional Reveiw Board.

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      CME Information

      This session is accredited with 1 CME credit.

      Moderators

      • Nuala Healy

        Dublin / Ireland

      Speakers

      • Xue Li

        Beijing / China

      • Elisa D’Ascoli

        Rome / Italy

      • Marcella Pasculli

        Rome / Italy

      • Roberto Maroncelli

        Rome / Italy

      • Thiemo Van Nijnatten

        Maastricht / Netherlands

      • Aisha Syed

        Cardiff / United Kingdom

      • Caroline Malhaire

        Paris / France