scholarly journals A Radiomic Model To Classify Response To Neoadjuvant Chemotherapy in Breast Cancer

2021 ◽  
Author(s):  
Peter McAnena ◽  
Brian Moloney ◽  
Robert Browne ◽  
Niamh O’Halloran ◽  
Leon Walsh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Medical Image Analysis ◽  
Breast Mri ◽  
Poor Response ◽  
Support Vector ◽  
Lasso Regression ◽  
Excellent Response ◽  
Tumour Segmentation ◽  
Treatment Paradigm ◽  
Neo Adjuvant Chemotherapy

Abstract Background Medical image analysis has evolved to facilitate the development of methods for high-throughput extraction of quantitative features that can potentially contribute to the diagnostic and treatment paradigm of cancer. There is a need for further improvement in the accuracy of predictive markers of response to neo-adjuvant chemotherapy (NAC). The aim of this study was to develop a radiomic classifier to enhance current approaches to predicting the response to NAC breast cancer. Methods Data on patients treated for breast cancer with NAC prior to surgery who had a pre-NAC dynamic contrast enhanced (DCE) breast MRI were included. Response to NAC was assessed using the Miller-Payne system on the excised tumour. Tumour segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator (LASSO) regression. A support vector machine (SVM) learning model was used to classify response to NAC. Results 74 patients were included. Patients were classified as having a poor response to NAC (reduction in cellularity <90%, n=44) and an excellent response (>90% reduction in cellularity, n=30). 4 radiomics features (discretized kurtosis, NGDLM contrast, GLZLM_SZE and GLZLM_ZP) were identified as pertinent predictors of response to NAC. A SVM model using these features stratified patients into poor and excellent response groups producing an AUC of 0.75. Addition of estrogen receptor (ER) status improved the accuracy of the model with an AUC of 0.811. Conclusion This study identified a radiomic classifier incorporating 4 radiomics features to augment subtype based classification of response to NAC in breast cancer.

Breast MRI for monitoring response of primary breast cancer to neo-adjuvant chemotherapy

2002 ◽  
Vol 12 (7) ◽  
pp. 1711-1719 ◽  
Author(s):  
A. Rieber ◽  
H.-J. Brambs ◽  
A. Gabelmann ◽  
V. Heilmann ◽  
R. Kreienberg ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Primary Breast Cancer ◽  
Breast Mri ◽  
Neo Adjuvant Chemotherapy ◽  
Monitoring Response

scholarly journals Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 919
Author(s):  
Isaac Daimiel Naranjo ◽  
Peter Gibbs ◽  
Jeffrey S. Reiner ◽  
Roberto Lo Gullo ◽  
Caleb Sooknanan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Diagnostic Accuracy ◽  
Cancer Diagnosis ◽  
Breast Mri ◽  
Breast Cancer Diagnosis ◽  
Multiparametric Mri ◽  
Cancer Center ◽  
Support Vector ◽  
Model Based

The purpose of this multicenter retrospective study was to evaluate radiomics analysis coupled with machine learning (ML) of dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) radiomics models separately and combined as multiparametric MRI for improved breast cancer detection. Consecutive patients (Memorial Sloan Kettering Cancer Center, January 2018–March 2020; Medical University Vienna, from January 2011–August 2014) with a suspicious enhancing breast tumor on breast MRI categorized as BI-RADS 4 and who subsequently underwent image-guided biopsy were included. In 93 patients (mean age: 49 years ± 12 years; 100% women), there were 104 lesions (mean size: 22.8 mm; range: 7–99 mm), 46 malignant and 58 benign. Radiomics features were calculated. Subsequently, the five most significant features were fitted into multivariable modeling to produce a robust ML model for discriminating between benign and malignant lesions. A medium Gaussian support vector machine (SVM) model with five-fold cross validation was developed for each modality. A model based on DWI-extracted features achieved an AUC of 0.79 (95% CI: 0.70–0.88), whereas a model based on DCE-extracted features yielded an AUC of 0.83 (95% CI: 0.75–0.91). A multiparametric radiomics model combining DCE- and DWI-extracted features showed the best AUC (0.85; 95% CI: 0.77–0.92) and diagnostic accuracy (81.7%; 95% CI: 73.0–88.6). In conclusion, radiomics analysis coupled with ML of multiparametric MRI allows an improved evaluation of suspicious enhancing breast tumors recommended for biopsy on clinical breast MRI, facilitating accurate breast cancer diagnosis while reducing unnecessary benign breast biopsies.

Targeted combination therapy with fulvestrant (FUL) for second-line (2L) treatment of hormone receptor-positive (HR+) advanced breast cancer (ABC).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e12527-e12527
Author(s):  
Ingrid A. Mayer ◽  
Ruth O'Regan ◽  
Noah Saul Kornblum ◽  
Kimberly L. Blackwell
Keyword(s):  
Breast Cancer ◽  
Targeted Therapy ◽  
Targeted Therapies ◽  
Mammalian Target Of Rapamycin ◽  
Progression Free Survival ◽  
Poor Response ◽  
First Line ◽  
Hormone Receptor Positive ◽  
Active Research ◽  
Neo Adjuvant Chemotherapy

e12527 Background: FUL is the recommended 2L treatment for patients whose HR+ ABC progressed after aromatase inhibitor (AI) therapy. In first line ABC adding targeted therapy, eg. cyclin-dependent kinase 4/6 inhibitors (CDK4/6i) palbociclib or ribociclib, or mammalian target of rapamycin inhibitor (mTORi) everolimus (EVE), to endocrine therapy (ET) has shown superior efficacy vs ET alone. The use of similar strategies to delay disease progression on ET in the 2L setting is an area of active research. Methods: PubMed and ClinicalTrials.gov were searched for trials investigating FUL + targeted therapies in 2L HR+ ABC. Search terms: (advanced OR metastatic) AND (breast cancer) AND (FUL OR faslodex) AND (2L OR relapse OR refractory OR resistant OR progression). Efficacy, adverse events (AEs) and quality of life were assessed. Results: 28 studies of FUL + targeted therapies in 2L ABC were found. Key randomized trials include 8 studies exploring FUL + CDK4/6i: palbociclib, ribociclib or abemaciclib. Palbociclib + FUL significantly improved progression-free survival (PFS) vs FUL in 2L HR+ ABC (p < 0.0001; PALOMA-3), AEs were manageable. Assessment of FUL + ribociclib (MONALEESA-3) or FUL + abemaciclib (MONARCH-2) in 2L HR+ ABC is ongoing. Ten studies are evaluating FUL + phosphatidylinositol 3-kinase (PI3K)/AKT/mTORi in 2L HR+ ABC. FUL + EVE significantly prolonged PFS vs FUL (p = 0.02; PrECOG 0102). Two trials evaluated buparlisib (pan-PI3K inhibitor [PI3Ki]) + FUL in HR+ ABC post-AI (BELLE-2) and post-mTORi (BELLE-3). In both trials, buparlisib + FUL improved PFS vs FUL in patients with PIK3CA-mutated tumors, FUL alone led to a poor response in this subgroup. However, pictilisib (pan-PI3Ki) + FUL did not improve PFS vs FUL even in the PIK3CA-mutated subgroup (FERGI). Ongoing phase 3 trials are exploring FUL + alpelisib (α-specific PI3Ki; SOLAR-1) or FUL + taselisib (β-sparing PI3Ki; SANDPIPER) in PIK3CA-mutant HR+ ABC. Pts who have progressed on AI, CDK4/6i or (neo)adjuvant chemotherapy are eligible for these studies. Data on FUL + other targeted therapies will also be discussed. Conclusions: Addition of targeted therapy to FUL demonstrates promising efficacy beyond first line.

First experience with MRI-guided vacuum aspirated breast biopsy

2020 ◽  
pp. 25-31
Author(s):  
M. L. Mazo ◽  
O. E. Jacobs ◽  
O. S. Puchkova ◽  
M. V. Feldsherov ◽  
E. V. Kondratyev
Keyword(s):  
Breast Cancer ◽  
Breast Augmentation ◽  
Breast Biopsy ◽  
Breast Mri ◽  
Breast Tumors ◽  
High Tech ◽  
X Ray ◽  
Vacuum Aspiration ◽  
Mri Guided ◽  
Aesthetic Breast

The rate of detection of breast cancer by MRI, while other methods of radiological diagnosis are not sufficiently informative, ranges from 5.2 to 26.3 per cent. Suspicious breast tumors of category BI-RADS 4, 5 show morphological image-guided biopsy verification, in particular MRI with contrast. Purpose. To show the possibilities and features of carrying out MRI-guided vacuum breast biopsy, including after aesthetic breast augmentation. Material and methods. A comprehensive X-ray, ultrasound and MRI examination of 54 women aged between 28 and 70 years with different breast tumors was conducted. Of these, five were detected only by breast MRI with contrast, and were morphologically verified by MRI-guided vacuum aspiration biopsy. Results. 14 of the 54 patients with breast mass were diagnosed with breast cancer and 26 were diagnosed with benign diseases. The effectiveness of comprehensive examination and low-invasive high-tech MRI-guided procedures in early refined screening for breast cancer, including after aesthetic breast augmentation, has been demonstrated. MRI-guided vacuum-assisted breast biopsy is a fast, safe and accurate diagnostic method of morphological verification of suspicious breast tumors that do not have X-ray and ultrasound.

Susceptibility artifact at breast MRI due to retained fragment of localization hook‐wire hiding breast cancer

2021 ◽  
Author(s):  
Almir Galvão Vieira Bitencourt ◽  
Vinicius Cardona Felipe ◽  
Mauricio Doi ◽  
Luciana Graziano
Keyword(s):  
Breast Cancer ◽  
Breast Mri ◽  
Susceptibility Artifact ◽  
Hook Wire

scholarly journals Prospective study of hair recovery after (neo)adjuvant chemotherapy with scalp cooling in Japanese breast cancer patients

2021 ◽  
Author(s):  
Shozo Ohsumi ◽  
Sachiko Kiyoto ◽  
Mina Takahashi ◽  
Seiki Takashima ◽  
Kenjiro Aogi ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Adjuvant Chemotherapy ◽  
Prospective Study ◽  
Cancer Patients ◽  
Scalp Cooling ◽  
Breast Cancer Patients ◽  
Chemotherapy Infusion ◽  
Group A ◽  
Neo Adjuvant Chemotherapy ◽  
Group B

Abstract Purpose Scalp cooling during chemotherapy infusion to mitigate alopecia for breast cancer patients is becoming widespread; however, studies regarding hair recovery after chemotherapy with scalp cooling are limited. We conducted a prospective study of hair recovery after chemotherapy with scalp cooling. Patients and methods One hundred and seventeen Japanese female breast cancer patients who completed planned (neo)adjuvant chemotherapy using the Paxman Scalp Cooling System for alopecia prevention were evaluated for alopecia prevention in our prospective study. We evaluated their hair recovery 1, 4, 7, 10, and 13 months after chemotherapy. Primary outcomes were grades of alopecia judged by two investigators (objective grades) and patients’ answers to the questionnaire regarding the use of a wig or hat (subjective grades). Results Of 117 patients, 75 completed scalp cooling during the planned chemotherapy cycles (Group A), but 42 discontinued it mostly after the first cycle (Group B). Objective and subjective grades were significantly better in Group A than in Group B throughout 1 year, and at 4 and 7 months after chemotherapy. When we restricted patients to those with objective Grade 3 (hair loss of > 50%) at 1 month, Group A exhibited slightly faster hair recovery based on the objective grades than Group B. There was less persistent alopecia in Group A than in Group B. Conclusions Scalp cooling during chemotherapy infusion for Japanese breast cancer patients increased the rate of hair recovery and had preventive effects against persistent alopecia.

scholarly journals Diffusion tensor imaging on 3-T MRI breast: diagnostic performance in comparison to diffusion-weighted imaging

2021 ◽  
Vol 52 (1) ◽  
Author(s):  
Dalia Abdelhady ◽  
Amany Abdelbary ◽  
Ahmed H. Afifi ◽  
Alaa-eldin Abdelhamid ◽  
Hebatallah H. M. Hassan
Keyword(s):  
Breast Cancer ◽  
Diffusion Tensor Imaging ◽  
Sensitivity And Specificity ◽  
Diffusion Weighted Imaging ◽  
Diffusion Tensor ◽  
Breast Mri ◽  
Breast Lesions ◽  
Dce Mri ◽  
Additional Tool ◽  
Diffusion Weighted

Abstract Background Breast cancer is the most prevalent cancer among females. Dynamic contrast-enhanced MRI (DCE-MRI) breast is highly sensitive (90%) in the detection of breast cancer. Despite its high sensitivity in detecting breast cancer, its specificity (72%) is moderate. Owing to 3-T breast MRI which has the advantage of a higher signal to noise ratio and shorter scanning time rather than the 1.5-T MRI, the adding of new techniques as diffusion tensor imaging (DTI) to breast MRI became more feasible. Diffusion-weighted imaging (DWI) which tracks the diffusion of the tissue water molecule as well as providing data about the integrity of the cell membrane has been used as a valuable additional tool of DCE-MRI to increase its specificity. Based on DWI, more details about the microstructure could be detected using diffusion tensor imaging. The DTI applies diffusion in many directions so apparent diffusion coefficient (ADC) will vary according to the measured direction raising its sensitivity to microstructure elements and cellular density. This study aimed to investigate the diagnostic accuracy of DTI in the assessment of breast lesions in comparison to DWI. Results By analyzing the data of the 50 cases (31 malignant cases and 19 benign cases), the sensitivity and specificity of DWI in differentiation between benign and malignant lesions were about 90% and 63% respectively with PPV 90% and NPV 62%, while the DTI showed lower sensitivity and specificity about 81% and 51.7%, respectively, with PPV 78.9% and NPV 54.8% (P-value ≤ 0.05). Conclusion While the DWI is still the most established diffusion parameter, DTI may be helpful in the further characterization of tumor microstructure and differentiation between benign and malignant breast lesions.

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