Breast MRI radiomics for the pretreatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients

A non-invasive artificial intelligence model for identifying axillary pathological complete response to neoadjuvant chemotherapy in breast cancer: a secondary analysis to multicenter clinical trial

BACKGROUND

This study aims to develop a stacking model for accurately predicting axillary lymph node (ALN) response to neoadjuvant chemotherapy (NAC) using longitudinal MRI in breast cancer.

METHODS

We included patients with node-positive breast cancer who received NAC following surgery from January 2012 to June 2022. We collected MRIs before and after NAC, and extracted radiomics features from the tumour, peritumour, and ALN regions. The Mann-Whitney U test, least absolute shrinkage and selection operator, and Boruta algorithm were used to select features. We utilised machine learning techniques to develop three single-modality models and a stacking model for predicting ALN response to NAC.

RESULTS

This study consisted of a training cohort (n = 277), three external validation cohorts (n = 313, 164, and 318), and a prospective cohort (n = 81). Among the 1153 patients, 60.62% achieved ypN0. The stacking model achieved excellent AUCs of 0.926, 0.874, and 0.862 in the training, external validation, and prospective cohort, respectively. It also showed lower false-negative rates (FNRs) compared to radiologists, with rates of 14.40%, 20.85%, and 18.18% (radiologists: 40.80%, 50.49%, and 63.64%) in three cohorts. Additionally, there was a significant difference in disease-free survival between high-risk and low-risk groups (p < 0.05).

CONCLUSIONS

The stacking model can accurately predict ALN status after NAC in breast cancer, showing a lower false-negative rate than radiologists.

TRIAL REGISTRATION NUMBER

The clinical trial numbers were NCT03154749 and NCT04858529.

Molecular-subtype guided automatic invasive breast cancer grading using dynamic contrast-enhanced MRI

BACKGROUND AND OBJECTIVES

Histological grade and molecular subtype have presented valuable references in assigning personalized or precision medicine as the significant prognostic indicators representing biological behaviors of invasive breast cancer (IBC). To evaluate a two-stage deep learning framework for IBC grading that incorporates with molecular-subtype (MS) information using DCE-MRI.

METHODS

In Stage I, an innovative neural network called IOS2-DA is developed, which includes a dense atrous-spatial pyramid pooling block with a pooling layer (DA) and inception-octconved blocks with double kernel squeeze-and-excitations (IOS2). This method focuses on the imaging manifestation of IBC grades and performs preliminary prediction using a novel class F1-score loss function. In Stage II, a MS attention branch is introduced to fine-tune the integrated deep vectors from IOS2-DA via Kullback-Leibler divergence. The MS-guided information is weighted with preliminary results to obtain classification values, which are analyzed by ensemble learning for tumor grade prediction on three MRI post-contrast series. Objective assessment is quantitatively evaluated by receiver operating characteristic curve analysis. DeLong test is applied to measure statistical significance (P < 0.05).

RESULTS

The molecular-subtype guided IOS2-DA performs significantly better than the single IOS2-DA in terms of accuracy (0.927), precision (0.942), AUC (0.927, 95% CI: [0.908, 0.946]), and F1-score (0.930). The gradient-weighted class activation maps show that the feature representations extracted from IOS2-DA are consistent with tumor areas.

CONCLUSIONS

IOS2-DA elucidates its potential in non-invasive tumor grade prediction. With respect to the correlation between MS and histological grade, it exhibits remarkable clinical prospects in the application of relevant clinical biomarkers to enhance the diagnostic effectiveness of IBC grading. Therefore, DCE-MRI tends to be a feasible imaging modality for the thorough preoperative assessment of breast biological behavior and carcinoma prognosis.

Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review

Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.

A Radiomics Model Based on Synthetic MRI Acquisition for Predicting Neoadjuvant Systemic Treatment Response in Triple-Negative Breast Cancer

Purpose To determine if a radiomics model based on quantitative maps acquired with synthetic MRI (SyMRI) is useful for predicting neoadjuvant systemic therapy (NAST) response in triple-negative breast cancer (TNBC). Materials and Methods In this prospective study, 181 women diagnosed with stage I-III TNBC were scanned with a SyMRI sequence at baseline and at midtreatment (after four cycles of NAST), producing T1, T2, and proton density (PD) maps. Histopathologic analysis at surgery was used to determine pathologic complete response (pCR) or non-pCR status. From three-dimensional tumor contours drawn on the three maps, 310 histogram and textural features were extracted, resulting in 930 features per scan. Radiomic features were compared between pCR and non-pCR groups by using Wilcoxon rank sum test. To build a multivariable predictive model, logistic regression with elastic net regularization and cross-validation was performed for texture feature selection using 119 participants (median age, 52 years [range, 26-77 years]). An independent testing cohort of 62 participants (median age, 48 years [range, 23-74 years]) was used to evaluate and compare the models by area under the receiver operating characteristic curve (AUC). Results Univariable analysis identified 15 T1, 10 T2, and 12 PD radiomic features at midtreatment that predicted pCR with an AUC greater than 0.70 in both the training and testing cohorts. Multivariable radiomics models of maps acquired at midtreatment demonstrated superior performance over those acquired at baseline, achieving AUCs as high as 0.78 and 0.72 in the training and testing cohorts, respectively. Conclusion SyMRI-based radiomic features acquired at midtreatment are potentially useful for identifying early NAST responders in TNBC. Keywords: MR Imaging, Breast, Outcomes Analysis ClinicalTrials.gov registration no. NCT02276443 Supplemental material is available for this article. © RSNA, 2023 See also the commentary by Houser and Rapelyea in this issue.

A radiomic model to classify response to neoadjuvant chemotherapy in breast cancer

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 breast MRI were included. Response to NAC was assessed using the Miller-Payne system on the excised tumor. Tumor segmentation was carried out manually under the supervision of a consultant breast radiologist. Features were selected using least absolute shrinkage selection operator regression. A support vector machine 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 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.

Highly accurate response prediction in high-risk early breast cancer patients using a biophysical simulation platform

PURPOSE

Pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in early breast cancer (EBC) is largely dependent on breast cancer subtype, but no clinical-grade model exists to predict response and guide selection of treatment. A biophysical simulation of response to NAC has the potential to address this unmet need.

METHODS

We conducted a retrospective evaluation of a biophysical simulation model as a predictor of pCR. Patients who received standard NAC at the University of Chicago for EBC between January 1st, 2010 and March 31st, 2020 were included. Response was predicted using baseline breast MRI, clinicopathologic features, and treatment regimen by investigators who were blinded to patient outcomes.

RESULTS

A total of 144 tumors from 141 patients were included; 59 were triple-negative, 49 HER2-positive, and 36 hormone-receptor positive/HER2 negative. Lymph node disease was present in half of patients, and most were treated with an anthracycline-based regimen (58.3%). Sensitivity and specificity of the biophysical simulation for pCR were 88.0% (95% confidence interval [CI] 75.7 - 95.5) and 89.4% (95% CI 81.3 - 94.8), respectively, with robust results regardless of subtype. In patients with predicted pCR, 5-year event-free survival was 98%, versus 79% with predicted residual disease (log-rank p = 0.01, HR 4.57, 95% CI 1.36 - 15.34). At a median follow-up of 5.4 years, no patients with predicted pCR experienced disease recurrence.

CONCLUSION

A biophysical simulation model accurately predicts pCR and long-term outcomes from baseline MRI and clinical data, and is a promising tool to guide escalation/de-escalation of NAC.

Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers

PURPOSE

The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy.

EXPERIMENTAL DESIGN

We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187).

RESULTS

Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response.

CONCLUSIONS

Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.

SPIE Computer-Aided Diagnosis conference anniversary review

The SPIE Computer-Aided Diagnosis conference has been held for 16 consecutive years at the annual SPIE Medical Imaging symposium. The conference remains vibrant, with a core group of submitters as well as new submitters and attendees each year. Recent developments include a marked shift in submissions relating to the artificial intelligence revolution in medical image analysis. This review describes the topics and trends observed in research presented at the Computer-Aided Diagnosis conference as part of the 50th-anniversary celebration of SPIE Medical Imaging.

Prediction of pathological complete response using radiomics on MRI in patients with breast cancer undergoing neoadjuvant pharmacotherapy

PURPOSE

Neoadjuvant pharmacotherapy is essential for patients with breast cancer who wish to preserve the breast by shrinking the malignant tumor, allowing breast-conserving surgery. It may eliminate cancer cells completely, which is known as pathologic complete response (pCR). Patients with pCR have a lower risk of recurrence. The purpose of this study was to develop a method for predicting patients who achieve pCR by neoadjuvant pharmacotherapy using radiomic features in MR images.

METHODS

Fat-suppressed T2-weighted MR images of 64 cases were identified from the ISPY1 dataset. There were 26 cases of pCR and 38 cases of non-pCR. The image slice with the largest tumor diameter was selected from MR images, and the tumor region was manually segmented. A total of 371 radiomic features were calculated from the tumor region. We selected nine radiomic features using Lasso in this study. A support vector machine (SVM) with nine radiomic features was used for predicting patients with pCR.

RESULTS

The result of the ROC analysis showed that the area under the curve of SVM was 0.92 for distinguishing between pCR and non-pCR. Although the input data contain data that were misclassified by SVM, the survival curve classified into the pCR group was at a higher position than the non-pCR group. However, the log-rank test was [Formula: see text].

CONCLUSIONS

We developed a method to predict patients with pCR by neoadjuvant pharmacotherapy using noninvasive MR images. The survival curve of patients classified as having pCR by the proposed method was higher than those classified as non-pCR. Since the proposed method predicts patients who achieve pCR by neoadjuvant pharmacotherapy, it enhances the value of preoperative image information.

MRI Radiomics for Assessment of Molecular Subtype, Pathological Complete Response, and Residual Cancer Burden in Breast Cancer Patients Treated With Neoadjuvant Chemotherapy

RATIONALE AND OBJECTIVES

There are limited data on pretreatment imaging features that can predict response to neoadjuvant chemotherapy (NAC). To extract volumetric pretreatment MRI radiomics features and assess corresponding associations with breast cancer molecular subtypes, pathological complete response (pCR), and residual cancer burden (RCB) in patients treated with NAC.

MATERIALS AND METHODS

In this IRB-approved study, clinical and pretreatment MRI data from patients with biopsy-proven breast cancer who received NAC between September 2009 and July 2016 were retrospectively analyzed. Tumors were manually identified and semi-automatically segmented on first postcontrast images. Morphological and three-dimensional textural features were computed, including unfiltered and filtered image data, with spatial scaling factors (SSF) of 2, 4, and 6 mm. Wilcoxon rank-sum tests and area under the receiver operating characteristic curve were used for statistical analysis.

RESULTS

Two hundred and fifty nine patients with unilateral breast cancer, including 73 (28.2%) HER2+, 112 (43.2%) luminal, and 74 (28.6%) triple negative breast cancers (TNBC), were included. There was a significant difference in the median volume (p = 0.008), median longest axial tumor diameter (p = 0.009), and median longest volumetric diameter (p = 0.01) among tumor subtypes. There was also a significant difference in minimum signal intensity and entropy among the tumor subtypes with SSF = 4 mm (p = 0.009 and p = 0.02 respectively) and SSF = 6 mm (p = 0.007 and p < 0.001 respectively). Additionally, sphericity (p = 0.04) in HER2+ tumors and entropy with SSF = 2, 4, 6 mm (p = 0.004, 0.02, 0.047 respectively) in luminal tumors were significantly associated with pCR. Multiple features demonstrated significant association (p < 0.05) with pCR in TNBC and with RCB in luminal tumors and TNBC, with standard deviation of intensity with SSF = 6 mm achieving the highest AUC (AUC = 0.734) for pCR in TNBC.

CONCLUSION

MRI radiomics features are associated with different molecular subtypes of breast cancer, pCR, and RCB. These features may be noninvasive imaging biomarkers to identify cancer subtype and predict response to NAC.

Radiomics in surgical oncology: applications and challenges

Surgery is a curative treatment option for many patients with malignant tumors. Increased attention has focused on the combination of surgery with chemotherapy, as multimodality treatment has been associated with promising results in certain cancer types. Despite these data, there remains clinical equipoise on optimal timing and patient selection for neoadjuvant or adjuvant strategies. Radiomics, an emerging field involving the extraction of advanced features from radiographic images, has the potential to revolutionize oncologic treatment and contribute to the advance of personalized therapy by helping predict tumor behavior and response to therapy. This review analyzes and summarizes studies that use radiomics with machine learning in patients who have received neoadjuvant and/or adjuvant chemotherapy to predict prognosis, recurrence, survival, and therapeutic response for various cancer types. While studies in both neoadjuvant and adjuvant settings demonstrate above average performance on ability to predict progression-free and overall survival, there remain many challenges and limitations to widespread implementation of this technology. The lack of standardization of common practices to analyze radiomics, limited data sharing, and absence of auto-segmentation have hindered the inclusion and rapid adoption of radiomics in prospective, clinical studies.

Value of 18F-FDG PET/CT for predicting axillary pathologic complete response following neoadjuvant systemic therapy in breast cancer patients: emphasis on breast cancer subtype

Background

Neoadjuvant systemic therapy (NST) is a widely accepted initial treatment modality that can lead to pathologic downstaging of the axillary disease burden in breast cancer patients. Axillary response as well as baseline ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) uptake on positron emission tomography with computed tomography (PET/CT) differ between breast cancer subtypes. The value of baseline ¹⁸F-FDG PET/CT in predicting axillary response to NST is not yet established, possibly since breast cancer subtype was not taken into account. The purpose of this study was to investigate the value of baseline ¹⁸F-FDG PET/CT in predicting axillary response to NST with a specific emphasis on subtype.

Methods

PET-parameters derived from the primary tumor as well as the most FDG-avid axillary lymph node were measured on baseline ¹⁸F-FDG PET/CT. Overall imaging findings were compared with the gold standard of histopathology of the axillary surgery specimen. Analyses for ER-positive/HER2-negative were performed separately from HER2-positive and TN patients. In addition, separate analyses for clinically node-positive patients were performed.

Results

Sixty-six patients with 69 primary tumors were included in this study. Thirty-three axillae contained ER-positive/HER2-negative, 16 HER2-positive, and 20 TN breast cancer. No significant difference in PET-parameters between patients with axillary residual disease and axillary pathologic complete response were found for ER-positive/HER2-negative breast cancer. In the combined HER2-positive/TN subgroup, the SUV_max was significantly lower in patients without residual axillary disease in both the entire cohort and in patients with clinically node-positive disease. In this combined subgroup, a cut-off of 4.89 SUV_max measured on the most FDG-avid axillary lymph node could predict residual axillary disease with a sensitivity, specificity, PPV, and NPV of 90%, 69%, 53%, and 95%, respectively.

Conclusions

Predicting axillary response following NST with baseline ¹⁸F-FDG PET/CT can be performed when focusing on breast cancer subtypes. The easily computed PET-parameter SUV_max can predict axillary response in HER2-positive and TN breast cancer. This study adds to the accumulating evidence that studies investigating the value of ¹⁸F-FDG PET/CT in breast cancer should always take subtypes into account.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-021-00861-z.

Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review

The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.

Ultrasound-based deep learning radiomics in the assessment of pathological complete response to neoadjuvant chemotherapy in locally advanced breast cancer

PURPOSE

The aim of the study was to develop and validate a deep learning radiomic nomogram (DLRN) for preoperatively assessing breast cancer pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) based on the pre- and post-treatment ultrasound.

METHODS

Patients with locally advanced breast cancer (LABC) proved by biopsy who proceeded to undergo preoperative NAC were enrolled from hospital #1 (training cohort, 356 cases) and hospital #2 (independent external validation cohort, 236 cases). Deep learning and handcrafted radiomic features reflecting the phenotypes of the pre-treatment (radiomic signature [RS] 1) and post-treatment tumour (RS2) were extracted. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used for feature selection and RS construction. A DLRN was then developed based on the RSs and independent clinicopathological risk factors. The performance of the model was assessed with regard to calibration, discrimination and clinical usefulness.

RESULTS

The DLRN predicted the pCR status with accuracy, yielded an area under the receiver operator characteristic curve of 0.94 (95% confidence interval, 0.91-0.97) in the validation cohort, with good calibration. The DLRN outperformed the clinical model and single RS within both cohorts (P < 0.05, as per the DeLong test) and performed better than two experts' prediction of pCR (both P < 0.01 for comparison of total accuracy). Besides, prediction within the hormone receptor-positive/human epidermal growth factor receptor 2 (HER2)-negative, HER2+ and triple-negative subgroups also achieved good discrimination performance, with an AUC of 0.90, 0.95 and 0.93, respectively, in the external validation cohort. Decision curve analysis confirmed that the model was clinically useful.

CONCLUSION

A deep learning-based radiomic nomogram had good predictive value for pCR in LABC, which could provide valuable information for individual treatment.

Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging

Purpose

To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images.

Methods

Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well‐established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single‐feature‐based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple‐step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant).

Results

The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80–0.96).

Conclusions

The proposed multi‐marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging

Purpose: This study aims to develop and compare human-engineered radiomics methodologies that use multiparametric magnetic resonance imaging (mpMRI) to diagnose breast cancer. Approach: The dataset comprises clinical multiparametric MR images of 852 unique lesions from 612 patients. Each MR study included a dynamic contrast-enhanced (DCE)-MRI sequence and a T2-weighted (T2w) MRI sequence, and a subset of 389 lesions were also imaged with a diffusion-weighted imaging (DWI) sequence. Lesions were automatically segmented using the fuzzy C-means algorithm. Radiomic features were extracted from each MRI sequence. Two approaches, feature fusion and classifier fusion, to utilizing multiparametric information were investigated. A support vector machine classifier was trained for each method to differentiate between benign and malignant lesions. Area under the receiver operating characteristic curve (AUC) was used to evaluate and compare diagnostic performance. Analyses were first performed on the entire dataset and then on the subset that was imaged using the three-sequence protocol. Results: When using the full dataset, the single-parametric classifiers yielded the following AUCs and 95% confidence intervals:AUC DCE = 0.84 [0.82, 0.87],AUC T 2 w = 0.83 [0.80, 0.86], andAUC DWI = 0.69 [0.62, 0.75]. The two multiparametric classifiers both yielded AUCs of 0.87 [0.84, 0.89] and significantly outperformed all single-parametric methods classifiers. When using the three-sequence subset, the mpMRI classifiers' performances significantly decreased. Conclusions: The proposed mpMRI radiomics methods can improve the performance of computer-aided diagnostics for breast cancer and handle missing sequences in the imaging protocol.

Application and Analysis of Biomedical Imaging Technology in Early Diagnosis of Breast Cancer

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.