Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer

Application of machine learning in the analysis of multiparametric MRI data for the differentiation of treatment responses in breast cancer: retrospective study

OBJECTIVE

The objective of this study is to develop and validate a multiparametric MRI model employing machine learning to predict the effectiveness of treatment and the stage of breast cancer.

METHODS

The study encompassed 400 female patients diagnosed with breast cancer, with 200 individuals allocated to both the control and experimental groups, undergoing examinations in Shenzhen, China, during the period 2017-2023. This study pertains to retrospective research. Multiparametric MRI was employed to extract data concerning tumor size, blood flow, and metabolism.

RESULTS

The model achieved high accuracy, predicting treatment outcomes with an accuracy of 92%, sensitivity of 88%, and specificity of 95%. The model effectively classified breast cancer stages: stage I, 38% (P = 0.027); stage II, 72% (P = 0.014); stage III, 50% (P = 0.032); and stage IV, 45% (P = 0.041).

CONCLUSIONS

The developed model, utilizing multiparametric MRI and machine learning, exhibits high accuracy in predicting the effectiveness of treatment and breast cancer staging. These findings affirm the model's potential to enhance treatment strategies and personalize approaches for patients diagnosed with breast cancer. Our study presents an innovative approach to the diagnosis and treatment of breast cancer, integrating MRI data with machine learning algorithms. We demonstrate that the developed model exhibits high accuracy in predicting treatment efficacy and differentiating cancer stages. This underscores the importance of utilizing MRI and machine learning algorithms to enhance the diagnosis and individualization of treatment for this disease.

Machine learning and new insights for breast cancer diagnosis

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

MRI-Based Machine Learning Radiomics for Preoperative Assessment of Human Epidermal Growth Factor Receptor 2 Status in Urothelial Bladder Carcinoma

BACKGROUND

The human epidermal growth factor receptor 2 (HER2) has recently emerged as hotspot in targeted therapy for urothelial bladder cancer (UBC). The HER2 status is mainly identified by immunohistochemistry (IHC), preoperative and noninvasive methods for determining HER2 status in UBC remain in searching.

PURPOSES

To investigate whether radiomics features extracted from MRI using machine learning algorithms can noninvasively evaluate the HER2 status in UBC.

STUDY TYPE

Retrospective.

POPULATION

One hundred ninety-five patients (age: 68.7 ± 10.5 years) with 14.3% females from January 2019 to May 2023 were divided into training (N = 156) and validation (N = 39) cohorts, and 43 patients (age: 67.1 ± 13.1 years) with 13.9% females from June 2023 to January 2024 constituted the test cohort (N = 43).

FIELD STRENGTH/SEQUENCE

3 T, T2-weighted imaging (turbo spin-echo), diffusion-weighted imaging (breathing-free spin echo).

ASSESSMENT

The HER2 status were assessed by IHC. Radiomics features were extracted from MRI images. Pearson correlation coefficient and the least absolute shrinkage and selection operator (LASSO) were applied for feature selection, and six machine learning models were established with optimal features to identify the HER2 status in UBC.

STATISTICAL TESTS

Mann-Whitney U-test, chi-square test, LASSO algorithm, receiver operating characteristic analysis, and DeLong test.

RESULTS

Three thousand forty-five radiomics features were extracted from each lesion, and 22 features were retained for analysis. The Support Vector Machine model demonstrated the best performance, with an AUC of 0.929 (95% CI: 0.888-0.970) and accuracy of 0.859 in the training cohort, AUC of 0.886 (95% CI: 0.780-0.993) and accuracy of 0.846 in the validation cohort, and AUC of 0.712 (95% CI: 0.535-0.889) and accuracy of 0.744 in the test cohort.

DATA CONCLUSION

MRI-based radiomics features combining machine learning algorithm provide a promising approach to assess HER2 status in UBC noninvasively and preoperatively.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making.

Development and Validation of a Pathomics Model Using Machine Learning to Predict CXCL8 Expression and Prognosis in Head and Neck Cancer

OBJECTIVES

The necessity to develop a method for prognostication and to identify novel biomarkers for personalized medicine in patients with head and neck squamous cell carcinoma (HNSCC) cannot be overstated. Recently, pathomics, which relies on quantitative analysis of medical imaging, has come to the forefront. CXCL8, an essential inflammatory cytokine, has been shown to correlate with overall survival (OS). This study examined the relationship between CXCL8 mRNA expression and pathomics features and aimed to explore the biological underpinnings of CXCL8.

METHODS

Clinical information and transcripts per million mRNA sequencing data were obtained from The Cancer Genome Atlas (TCGA)-HNSCC dataset. We identified correlations between CXCL8 mRNA expression and patient survival rates using a Kaplan-Meier survival curve. A retrospective analysis of 313 samples diagnosed with HNSCC in the TCGA database was conducted. Pathomics features were extracted from hematoxylin and eosin-stained images, and then the minimum redundancy maximum relevance, with recursive feature elimination (mRMR-RFE) method was applied, followed by screening with the logistic regression algorithm.

RESULTS

Kaplan-Meier curves indicated that high expression of CXCL8 was significantly associated with decreased OS. The logistic regression pathomics model incorporated 16 radiomics features identified by the mRMR-RFE method in the training set and demonstrated strong performance in the testing set. Calibration plots showed that the probability of high gene expression predicted by the pathomics model was in good agreement with actual observations, suggesting the model's high clinical applicability.

CONCLUSION

The pathomics model of CXCL8 mRNA expression serves as an effective tool for predicting prognosis in patients with HNSCC and can aid in clinical decision-making. Elevated levels of CXCL8 expression may lead to reduced DNA damage and are associated with a pro-inflammatory tumor microenvironment, offering a potential therapeutic target.

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

BACKGROUNDS

Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed.

METHODS

In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes.

RESULTS

The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response.

CONCLUSIONS

Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Update on DWI for Breast Cancer Diagnosis and Treatment Monitoring

DWI is a noncontrast MRI technique that measures the diffusion of water molecules within biologic tissue. DWI is increasingly incorporated into routine breast MRI examinations. Currently, the main applications of DWI are breast cancer detection and characterization, prognostication, and prediction of treatment response to neoadjuvant chemotherapy. In addition, DWI is promising as a noncontrast MRI alternative for breast cancer screening. Problems with suboptimal resolution and image quality have restricted the mainstream use of DWI for breast imaging, but these shortcomings are being addressed through several technologic advancements. In this review, we present an up-to-date assessment of the use of DWI for breast cancer imaging, including a summary of the clinical literature and recommendations for future use.

Development and validation of a combined nomogram for predicting perineural invasion status in rectal cancer via computed tomography-based radiomics

AIM

This study aimed to create and validate a clinic-radiomics nomogram based on computed tomography (CT) imaging for predicting preoperative perineural invasion (PNI) of rectal cancer (RC).

MATERIAL AND METHODS

This study enrolled 303 patients with RC who were divided into training (n = 242) and test datasets (n = 61) in an 8:2 ratio with all their clinical outcomes. A total of 3,296 radiomic features were extracted from CT images. Five machine learning (ML) models (logistic regression (LR)/K-nearest neighbor (KNN)/multilayer perceptron (MLP)/support vector machine (SVM)/light gradient boosting machine (LightGBM)) were developed using radiomic features derived from the arterial and venous phase images, and the model with the best diagnostic performance was selected. By combining the radiomics and clinical signatures, a fused nomogram model was constructed.

RESULTS

After using the Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) to remove redundant features, the MLP model proved to be the most efficient among the five ML models. The fusion nomogram based on MLP prediction probability further improves the ability to predict the PNI status. The area under the curve (AUC) of the training and test sets was 0.883 and 0.889, respectively, which were higher than those of the clinical (training set, AUC = 0.710; test set, AUC = 0.762) and radiomic models (training set, AUC = 0.840; test set, AUC = 0.834).

CONCLUSIONS

The clinical-radiomics combined nomogram model based on enhanced CT images efficiently predicted the PNI status of patients with RC.

Identification of Luminal A breast cancer by using deep learning analysis based on multi-modal images

Purpose

To evaluate the diagnostic performance of a deep learning model based on multi-modal images in identifying molecular subtype of breast cancer.

Materials and methods

A total of 158 breast cancer patients (170 lesions, median age, 50.8 ± 11.0 years), including 78 Luminal A subtype and 92 non-Luminal A subtype lesions, were retrospectively analyzed and divided into a training set (n = 100), test set (n = 45), and validation set (n = 25). Mammography (MG) and magnetic resonance imaging (MRI) images were used. Five single-mode models, i.e., MG, T2-weighted imaging (T2WI), diffusion weighting imaging (DWI), axial apparent dispersion coefficient (ADC), and dynamic contrast-enhanced MRI (DCE-MRI), were selected. The deep learning network ResNet50 was used as the basic feature extraction and classification network to construct the molecular subtype identification model. The receiver operating characteristic curve were used to evaluate the prediction efficiency of each model.

Results

The accuracy, sensitivity and specificity of a multi-modal tool for identifying Luminal A subtype were 0.711, 0.889, and 0.593, respectively, and the area under the curve (AUC) was 0.802 (95% CI, 0.657- 0.906); the accuracy, sensitivity, and AUC were higher than those of any single-modal model, but the specificity was slightly lower than that of DCE-MRI model. The AUC value of MG, T2WI, DWI, ADC, and DCE-MRI model was 0.593 (95%CI, 0.436-0.737), 0.700 (95%CI, 0.545-0.827), 0.564 (95%CI, 0.408-0.711), 0.679 (95%CI, 0.523-0.810), and 0.553 (95%CI, 0.398-0.702), respectively.

Conclusion

The combination of deep learning and multi-modal imaging is of great significance for diagnosing breast cancer subtypes and selecting personalized treatment plans for doctors.

Radiomics and artificial intelligence analysis by T2-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging to predict Breast Cancer Histological Outcome

OBJECTIVE

The objective of the study was to evaluate the accuracy of radiomics features obtained by MR images to predict Breast Cancer Histological Outcome.

METHODS

A total of 217 patients with malignant lesions were analysed underwent MRI examinations. Considering histological findings as the ground truth, four different types of findings were used in both univariate and multivariate analyses: (1) G1 + G2 vs G3 classification; (2) presence of human epidermal growth factor receptor 2 (HER2 + vs HER2 -); (3) presence of the hormone receptor (HR + vs HR -); and (4) presence of luminal subtypes of breast cancer.

RESULTS

The best accuracy for discriminating HER2 + versus HER2 - breast cancers was obtained considering nine predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 88% on validation set). The best accuracy for discriminating HR + versus HR - breast cancers was obtained considering nine predictors by T2-weighted subtraction images and a decision tree (accuracy of 90% on validation set). The best accuracy for discriminating G1 + G2 versus G3 breast cancers was obtained considering 16 predictors by early phase T1-weighted subtraction images in a linear regression model with an accuracy of 75%. The best accuracy for discriminating luminal versus non-luminal breast cancers was obtained considering 27 predictors by early phase T1-weighted subtraction images and a decision tree (accuracy of 94% on validation set).

CONCLUSIONS

The combination of radiomics analysis and artificial intelligence techniques could be used to support physician decision-making in prediction of Breast Cancer Histological Outcome.

A Comprehensive Model Based on Dynamic Contrast-Enhanced Magnetic Resonance Imaging Can Better Predict the Preoperative Histological Grade of Breast Cancer Than a Radiomics Model

Background

Histological grade is an important prognostic factor for patients with breast cancer and can affect clinical decision-making. From a clinical perspective, developing an efficient and non-invasive method for evaluating histological grading is desirable, facilitating improved clinical decision-making by physicians. This study aimed to develop an integrated model based on radiomics and clinical imaging features for preoperative prediction of histological grade invasive breast cancer.

Methods

In this retrospective study, we recruited 211 patients with invasive breast cancer and randomly assigned them to either a training group (n=147) or a validation group (n=64) with a 7:3 ratio. Patients were classified as having low-grade tumors, which included grade I and II tumors, or high-grade tumors, which included grade III tumors. Three models were constructed based on basic clinical features, radiomics features, and the sum of the two. To assess diagnostic performance of the radiomics models, we employed measures such as receiver operating characteristic (ROC) curve, decision curve analysis (DCA), accuracy, sensitivity, and specificity, and the predictive performance of the three models was compared using the DeLong test and net reclassification improvement (NRI).

Results

The area under the curve (AUC) of the clinical model, radiomics model, and comprehensive model was 0.682, 0.833, and 0.882 in the training set and 0.741, 0.751, and 0.836 in the validation set, respectively. NRI analysis confirmed that the combined model was better than the other two models in predicting the histological grade of breast cancer (NRI=21.4% in the testing cohort).

Conclusion

Compared with the other models, the comprehensive model based on the combination of basic clinical features and radiomics features exhibits more significant potential for predicting histological grade and can better assist clinicians in optimal decision-making.

Disulfidptosis-associated lncRNAs predict breast cancer subtypes

Disulfidptosis is a newly discovered mode of cell death. However, its relationship with breast cancer subtypes remains unclear. In this study, we aimed to construct a disulfidptosis-associated breast cancer subtype prediction model. We obtained 19 disulfidptosis-related genes from published articles and performed correlation analysis with lncRNAs differentially expressed in breast cancer. We then used the random forest algorithm to select important lncRNAs and establish a breast cancer subtype prediction model. We identified 132 lncRNAs significantly associated with disulfidptosis (FDR < 0.01, |R|> 0.15) and selected the first four important lncRNAs to build a prediction model (training set AUC = 0.992). The model accurately predicted breast cancer subtypes (test set AUC = 0.842). Among the key lncRNAs, LINC02188 had the highest expression in the Basal subtype, while LINC01488 and GATA3-AS1 had the lowest expression in Basal. In the Her2 subtype, LINC00511 had the highest expression level compared to other key lncRNAs. GATA3-AS1 had the highest expression in LumA and LumB subtypes, while LINC00511 had the lowest expression in these subtypes. In the Normal subtype, GATA3-AS1 had the highest expression level compared to other key lncRNAs. Our study also found that key lncRNAs were closely related to RNA methylation modification and angiogenesis (FDR < 0.05, |R|> 0.1), as well as immune infiltrating cells (P.adj < 0.01, |R|> 0.1). Our random forest model based on disulfidptosis-related lncRNAs can accurately predict breast cancer subtypes and provide a new direction for research on clinical therapeutic targets for breast cancer.

Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation

OBJECTIVE

The aim of the study is to demonstrate whether radiomics based on an automatic segmentation method is feasible for predicting molecular subtypes.

METHODS

This retrospective study included 516 patients with confirmed breast cancer. An automatic segmentation-3-dimensional UNet-based Convolutional Neural Networks, trained on our in-house data set-was applied to segment the regions of interest. A set of 1316 radiomics features per region of interest was extracted. Eighteen cross-combination radiomics methods-with 6 feature selection methods and 3 classifiers-were used for model selection. Model classification performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

The average dice similarity coefficient value of the automatic segmentation was 0.89. The radiomics models were predictive of 4 molecular subtypes with the best average: AUC = 0.8623, accuracy = 0.6596, sensitivity = 0.6383, and specificity = 0.8775. For luminal versus nonluminal subtypes, AUC = 0.8788 (95% confidence interval [CI], 0.8505-0.9071), accuracy = 0.7756, sensitivity = 0.7973, and specificity = 0.7466. For human epidermal growth factor receptor 2 (HER2)-enriched versus non-HER2-enriched subtypes, AUC = 0.8676 (95% CI, 0.8370-0.8982), accuracy = 0.7737, sensitivity = 0.8859, and specificity = 0.7283. For triple-negative breast cancer versus non-triple-negative breast cancer subtypes, AUC = 0.9335 (95% CI, 0.9027-0.9643), accuracy = 0.9110, sensitivity = 0.4444, and specificity = 0.9865.

CONCLUSIONS

Radiomics based on automatic segmentation of magnetic resonance imaging can predict breast cancer of 4 molecular subtypes noninvasively and is potentially applicable in large samples.

Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis

Purpose

This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy.

Materials and methods

This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software.

Results

A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up.

Conclusion

This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance.

Systematic review registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.

Ultrasound-based radiomics model for predicting molecular biomarkers in breast cancer

Background

Breast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression via extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC.

Objectives

To investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC.

Methods

A total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling.

Results

359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively.

Conclusion

Ultrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC.

Breast Cancer Surrogate Subtype Classification Using Pretreatment Multi-Phase Dynamic Contrast-Enhanced Magnetic Resonance Imaging Radiomics: A Retrospective Single-Center Study

This study aimed to explore the potential of multi-phase dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics for classifying breast cancer surrogate subtypes. This retrospective study analyzed 360 breast cancers from 319 patients who underwent pretreatment DCE-MRI between January 2015 and January 2019. The cohort consisted of 33 triple-negative, 26 human epidermal growth factor receptor 2 (HER2)-positive, 109 luminal A-like, 144 luminal B-like HER2-negative, and 48 luminal B-like HER2-positive lesions. A total of 1781 radiomic features were extracted from manually segmented breast cancers in each DCE-MRI sequence. The model was internally validated and selected using ten times repeated five-fold cross-validation on the primary cohort, with further evaluation using a validation cohort. The most successful models were logistic regression models applied to the third post-contrast subtraction images. These models exhibited the highest area under the curve (AUC) for discriminating between luminal A like vs. others (AUC: 0.78), luminal B-like HER2 negative vs. others (AUC: 0.57), luminal B-like HER2 positive vs. others (AUC: 0.60), HER2 positive vs. others (AUC: 0.81), and triple negative vs. others (AUC: 0.83). In conclusion, the radiomic features extracted from multi-phase DCE-MRI are promising for discriminating between breast cancer subtypes. The best-performing models relied on tissue changes observed during the mid-stage of the imaging process.

Assessment of androgen receptor expression in breast cancer patients using 18 F-FDG PET/CT radiomics and clinicopathological characteristics

OBJECTIVE

In the present study, we mainly aimed to predict the expression of androgen receptor (AR) in breast cancer (BC) patients by combing radiomic features and clinicopathological factors in a non-invasive machine learning way.

MATERIALS AND METHODS

A total of 48 BC patients, who were initially diagnosed by ¹⁸F-FDG PET/CT, were retrospectively enrolled in this study. LIFEx software was used to extract radiomic features based on PET and CT data. The most useful predictive features were selected by the LASSO (least absolute shrinkage and selection operator) regression and t-test. Radiomic signatures and clinicopathologic characteristics were incorporated to develop a prediction model using multivariable logistic regression analysis. The receiver operating characteristic (ROC) curve, Hosmer-Lemeshow (H-L) test, and decision curve analysis (DCA) were conducted to assess the predictive efficiency of the model.

RESULTS

In the univariate analysis, the metabolic tumor volume (MTV) was significantly correlated with the expression of AR in BC patients (p < 0.05). However, there only existed feeble correlations between estrogen receptor (ER), progesterone receptor (PR), and AR status (p = 0.127, p = 0.061, respectively). Based on the binary logistic regression method, MTV, SHAPE_Sphericity_CT (CT Sphericity from SHAPE), and GLCM_Contrast_CT (CT Contrast from grey-level co-occurrence matrix) were included in the prediction model for AR expression. Among them, GLCM_Contrast_CT was an independent predictor of AR status (OR = 9.00, p = 0.018). The area under the curve (AUC) of ROC in this model was 0.832. The p-value of the H-L test was beyond 0.05.

CONCLUSIONS

A prediction model combining radiomic features and clinicopathological characteristics could be a promising approach to predict the expression of AR and noninvasively screen the BC patients who could benefit from anti-AR regimens.

Characterization of Breast Tumors from MR Images Using Radiomics and Machine Learning Approaches

Determining histological subtypes, such as invasive ductal and invasive lobular carcinomas (IDCs and ILCs) and immunohistochemical markers, such as estrogen response (ER), progesterone response (PR), and the HER2 protein status is important in planning breast cancer treatment. MRI-based radiomic analysis is emerging as a non-invasive substitute for biopsy to determine these signatures. We explore the effectiveness of radiomics-based and CNN (convolutional neural network)-based classification models to this end. T1-weighted dynamic contrast-enhanced, contrast-subtracted T1, and T2-weighted MR images of 429 breast cancer tumors from 323 patients are used. Various combinations of input data and classification schemes are applied for ER⁺ vs. ER^-, PR⁺ vs. PR^-, HER2⁺ vs. HER2^-, and IDC vs. ILC classification tasks. The best results were obtained for the ER⁺ vs. ER^- and IDC vs. ILC classification tasks, with their respective AUCs reaching 0.78 and 0.73 on test data. The results with multi-contrast input data were generally better than the mono-contrast alone. The radiomics and CNN-based approaches generally exhibited comparable results. ER and IDC/ILC classification results were promising. PR and HER2 classifications need further investigation through a larger dataset. Better results by using multi-contrast data might indicate that multi-parametric quantitative MRI could be used to achieve more reliable classifiers.

Application of radiomics to meningiomas: A systematic review

BACKGROUND

Quantitative imaging analysis through radiomics is a powerful technology to non-invasively assess molecular correlates and guide clinical decision-making. There has been growing interest in image-based phenotyping for meningiomas given the complexities in management.

METHODS

We systematically reviewed meningioma radiomics analyses published in PubMed, Embase, and Web of Science until December 20, 2021. We compiled performance data and assessed publication quality using the radiomics quality score (RQS).

RESULTS

A total of 170 publications were grouped into 5 categories of radiomics applications to meningiomas: Tumor detection and segmentation (21%), classification across neurologic diseases (54%), grading (14%), feature correlation (3%), and prognostication (8%). A majority focused on technical model development (73%) versus clinical applications (27%), with increasing adoption of deep learning. Studies utilized either private institutional (50%) or public (49%) datasets, with only 68% using a validation dataset. For detection and segmentation, radiomic models had a mean accuracy of 93.1 ± 8.1% and a dice coefficient of 88.8 ± 7.9%. Meningioma classification had a mean accuracy of 95.2 ± 4.0%. Tumor grading had a mean area-under-the-curve (AUC) of 0.85 ± 0.08. Correlation with meningioma biological features had a mean AUC of 0.89 ± 0.07. Prognostication of the clinical course had a mean AUC of 0.83 ± 0.08. While clinical studies had a higher mean RQS compared to technical studies, quality was low overall with a mean RQS of 6.7 ± 5.9 (possible range -8 to 36).

CONCLUSIONS

There has been global growth in meningioma radiomics, driven by data accessibility and novel computational methodology. Translatability toward complex tasks such as prognostication requires studies that improve quality, develop comprehensive patient datasets, and engage in prospective trials.

Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer

Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.

Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance

BACKGROUND

Recent advanced in radiomics analysis could help to identify breast cancer among benign mammary masses. The aim was to create a radiomics signature using breast DCE-MRI extracted features to classify tumors and to compare the performances with the BI-RADS classification.

MATERIAL AND METHODS

From September 2017 to December 2019 images, exams and records from consecutive patients with mammary masses on breast DCE-MRI and available histology from one center were retrospectively reviewed (79 patients, 97 masses). Exclusion criterion was malignant uncertainty. The tumors were split in a train-set (70%) and a test-set (30%). From 14 kinetics maps, 89 radiomics features were extracted, for a total of 1246 features per tumor. Feature selection was made using Boruta algorithm, to train a random forest algorithm on the train-set. BI-RADS classification was recorded from two radiologists.

RESULTS

Seventy-seven patients were analyzed with 94 tumors, (71 malignant, 23 benign). Over 1246 features, 17 were selected from eight kinetic maps. On the test-set, the model reaches an AUC = 0.94 95 CI [0.85-1.00] and a specificity of 33% 95 CI [10-70]. There were 43/94 (46%) lesions BI-RADS4 (4a = 12/94 (13%); 4b = 9/94 (10%); and 4c = 22/94 (23%)). The BI-RADS score reached an AUC = 0.84 95 CI [0.73-0.95] and a specificity of 17% 95 CI [3-56]. There was no significant difference between the ROC curves for the model or the BI-RADS score (p = 0.19).

CONCLUSION

A radiomics signature from features extracted using breast DCE-MRI can reach an AUC of 0.94 on a test-set and could provide as good results as BI-RADS to classify mammary masses.

A Two-Step Feature Selection Radiomic Approach to Predict Molecular Outcomes in Breast Cancer

Breast Cancer (BC) is the most common cancer among women worldwide and is characterized by intra- and inter-tumor heterogeneity that strongly contributes towards its poor prognosis. The Estrogen Receptor (ER), Progesterone Receptor (PR), Human Epidermal Growth Factor Receptor 2 (HER2), and Ki67 antigen are the most examined markers depicting BC heterogeneity and have been shown to have a strong impact on BC prognosis. Radiomics can noninvasively predict BC heterogeneity through the quantitative evaluation of medical images, such as Magnetic Resonance Imaging (MRI), which has become increasingly important in the detection and characterization of BC. However, the lack of comprehensive BC datasets in terms of molecular outcomes and MRI modalities, and the absence of a general methodology to build and compare feature selection approaches and predictive models, limit the routine use of radiomics in the BC clinical practice. In this work, a new radiomic approach based on a two-step feature selection process was proposed to build predictors for ER, PR, HER2, and Ki67 markers. An in-house dataset was used, containing 92 multiparametric MRIs of patients with histologically proven BC and all four relevant biomarkers available. Thousands of radiomic features were extracted from post-contrast and subtracted Dynamic Contrast-Enanched (DCE) MRI images, Apparent Diffusion Coefficient (ADC) maps, and T2-weighted (T2) images. The two-step feature selection approach was used to identify significant radiomic features properly and then to build the final prediction models. They showed remarkable results in terms of F1-score for all the biomarkers: 84%, 63%, 90%, and 72% for ER, HER2, Ki67, and PR, respectively. When possible, the models were validated on the TCGA/TCIA Breast Cancer dataset, returning promising results (F1-score = 88% for the ER+/ER- classification task). The developed approach efficiently characterized BC heterogeneity according to the examined molecular biomarkers.

Value of genomics- and radiomics-based machine learning models in the identification of breast cancer molecular subtypes: a systematic review and meta-analysis

Background

In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available.

Methods

We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611).

Results

Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (CI): 0.60-0.96], 0.78 (95% CI: 0.69-0.87), 0.89 (95% CI: 0.83-0.91), and 0.83 (95% CI: 0.81-0.86), respectively. The c-statistic values of ML based on the gene-transcriptomic analysis cohort for the identification of the previously described BRCA molecular subtypes were 0.96 (95% CI: 0.93-0.99), 0.96 (95% CI: 0.93-0.99), 0.98 (95% CI: 0.95-1.00), and 0.97 (95% CI: 0.96-0.98) respectively. Additionally, the sensitivity of the ML model based on radiomics for each molecular subtype ranged from 0.79 to 0.85, while the sensitivity of the ML model based on gene-transcriptomic was between 0.92 and 0.99.

Conclusions

Both radiomics and gene transcriptomics produced ideal effects on BRCA molecular subtype prediction. Compared with radiomics, gene transcriptomics yielded better prediction results, but radiomics was simpler and more convenient from a clinical point of view.

Development and validation of an ultrasound-based radiomics nomogram for predicting the luminal from non-luminal type in patients with breast carcinoma

INTRODUCTION

The molecular subtype plays a significant role in breast carcinoma (BC), which is the main indicator to guide treatment and is closely associated with prognosis. The aim of this study was to investigate the feasibility and efficacy of an ultrasound-based radiomics nomogram in preoperatively discriminating the luminal from non-luminal type in patients with BC.

METHODS

A total of 264 BC patients who underwent routine ultrasound examination were enrolled in this study, of which 184 patients belonged to the training set and 80 patients to the test set. Breast tumors were delineated manually on the ultrasound images and then radiomics features were extracted. In the training set, the T test and least absolute shrinkage and selection operator (LASSO) were used for selecting features, and the radiomics score (Rad-score) for each patient was calculated. Based on the clinical risk features, Rad-score, and combined clinical risk features and Rad-score, three models were established, respectively. The performances of the models were validated with receiver operator characteristic (ROC) curve and decision curve analysis.

RESULTS

In all, 788 radiomics features per case were obtained from the ultrasound images. Through radiomics feature selection, 11 features were selected to constitute the Rad-score. The area under the ROC curve (AUC) of the Rad-score for predicting the luminal type was 0.828 in the training set and 0.786 in the test set. The nomogram comprising the Rad-score and US-reported tumor size showed AUCs of the training and test sets were 0.832 and 0.767, respectively, which were significantly higher than the AUCs of the clinical model in the training and test sets (0.691 and 0.526, respectively). However, there was no significant difference in predictive performance between the Rad-score and nomogram.

CONCLUSION

Both the Rad-score and nomogram can be applied as useful, noninvasive tools for preoperatively discriminating the luminal from non-luminal type in patients with BC. Furthermore, this study might provide a novel technique to evaluate molecular subtypes of BC.

Predicting hormone receptors and PAM50 subtypes of breast cancer from multi-scale lesion images of DCE-MRI with transfer learning technique

BACKGROUND

The recent development of artificial intelligence (AI) technologies coupled with medical imaging data has gained considerable attention, and offers a non-invasive approach for cancer diagnosis and prognosis. In this context, improved breast cancer (BC) molecular characteristics assessment models are foreseen to enable personalized strategies with better clinical outcomes compared to existing screening strategies. And it is a promising approach to developing models for hormone receptors (HR) and subtypes of BC patients from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.

METHODS

In this institutional review board-approved study, 174 BC patients with both DCE-MRI and RNA-seq data in the local database were analyzed. Slice images from tumor lesions and multi-scale peri-tumor regions were used as model inputs, and five representative pre-trained transfer learning (TF) networks, such as Inception-v3 and Xception, were employed to establish prediction models. A comprehensive analysis was performed using five-fold cross-validation to avoid overfitting, and accuracy (ACC) and area under the receiver operating characteristic curve (AUROC) to evaluate model performance.

RESULTS

Xception achieved the superior results when using solely tumor regions, with highest AUROCs of 0.844 (95% CI: [0.841, 0.847]) and 0.784 (95% CI: [0.781, 0.788]) for estrogen receptor (ER) and progesterone receptor (PR), respectively, and best ACC of 0.467 (95% CI: [0.462, 0.470]) for PAM50 subtypes. A significant improvement in the model performance was observed when images of the peri-tumor region were included, with optimal results achieved using images of the tumor and the 10 mm peri-tumor regions. Xception-based TF models performed most effectively in predicting ER and PR statuses, with the AUROCs were 0.942 (95% CI: [0.940, 0.944]) and 0.920 (95% CI: [0.917, 0.922]), respectively, whereas for PAM50 subtypes, the Inception-v3-based network yielded the highest ACC as 0.742 (95% CI: [0.738, 0.746]).

CONCLUSIONS

Transfer learning analysis based on DCE-MRI data of tumor and peri-tumor regions was helpful to the non-invasive assessment of molecular characteristics of BC.

Integration of ultrasound radiomics features and clinical factors: A nomogram model for identifying the Ki-67 status in patients with breast carcinoma

Objective

The aim of this study was to develop and validate an ultrasound-based radiomics nomogram model by integrating the clinical risk factors and radiomics score (Rad-Score) to predict the Ki-67 status in patients with breast carcinoma.

Methods

Ultrasound images of 284 patients (196 high Ki-67 expression and 88 low Ki-67 expression) were retrospectively analyzed, of which 198 patients belonged to the training set and 86 patients to the test set. The region of interest of tumor was delineated, and the radiomics features were extracted. Radiomics features underwent dimensionality reduction analysis by using the independent sample t test and least absolute shrinkage and selection operator (LASSO) algorithm. The support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB) and XGBoost (XGB) machine learning classifiers were trained to establish prediction model based on the selected features. The classifier with the highest AUC value was selected to convert the output of the results into the Rad-Score and was regarded as Rad-Score model. In addition, the logistic regression method was used to integrate Rad-Score and clinical risk factors to generate the nomogram model. The leave group out cross-validation (LGOCV) method was performed 200 times to verify the reliability and stability of the nomogram model.

Results

Six classifier models were established based on the 15 non-zero coefficient features. Among them, the LR classifier achieved the best performance in the test set, with the area under the receiver operating characteristic curve (AUC) value of 0.786, and was obtained as the Rad-Score model, while the XGB performed the worst (AUC, 0.615). In multivariate analysis, independent risk factor for high Ki-67 status was age (odds ratio [OR] = 0.97, p = 0.04). The nomogram model based on the age and Rad-Score had a slightly higher AUC than that of Rad-Score model (AUC, 0.808 vs. 0.798) in the test set, but no statistical difference (p = 0.144, DeLong test). The LGOCV yielded a median AUC of 0.793 in the test set.

Conclusions

This study proposed a convenient, clinically useful ultrasound radiomics nomogram model that can be used for the preoperative individualized prediction of the Ki-67 status in patients with BC.

Development and validation of a clinicoradiomic nomogram to assess the HER2 status of patients with invasive ductal carcinoma

Background

The determination of HER2 expression status contributes significantly to HER2-targeted therapy in breast carcinoma. However, an economical, efficient, and non-invasive assessment of HER2 is lacking. We aimed to develop a clinicoradiomic nomogram based on radiomics scores extracted from multiparametric MRI (mpMRI, including ADC-map, T2W1, DCE-T1WI) and clinical risk factors to assess HER2 status.

Methods

We retrospectively collected 214 patients with pathologically confirmed invasive ductal carcinoma between January 2018 to March 2021 from Fudan University Shanghai Cancer Center, and randomly divided this cohort into training set (n = 128, 42 HER2-positive and 86 HER2-negative cases) and validation set (n = 86, 28 HER2-positive and 58 HER2-negative cases) at a ratio of 6:4. The original and transformed pretherapy mpMRI images were treated by semi-automated segmentation and manual modification on the DeepWise scientific research platform v1.6 (http://keyan.deepwise.com/), then radiomics feature extraction was implemented with PyRadiomics library. Recursive feature elimination (RFE) based on logistic regression (LR) and LASSO regression were adpoted to identify optimal features before modeling. LR, Linear Discriminant Analysis (LDA), support vector machine (SVM), random forest (RF), naive Bayesian (NB) and XGBoost (XGB) algorithms were used to construct the radiomics signatures. Independent clinical predictors were identified through univariate logistic analysis (age, tumor location, ki-67 index, histological grade, and lymph node metastasis). Then, the radiomics signature with the best diagnostic performance (Rad score) was further combined with significant clinical risk factors to develop a clinicoradiomic model (nomogram) using multivariate logistic regression. The discriminative power of the constructed models were evaluated by AUC, DeLong test, calibration curve, and decision curve analysis (DCA).

Results

70 (32.71%) of the enrolled 214 cases were HER2-positive, while 144 (67.29%) were HER2-negative. Eleven best radiomics features were retained to develop 6 radiomcis classifiers in which RF classifier showed the highest AUC of 0.887 (95%CI: 0.827–0.947) in the training set and acheived the AUC of 0.840 (95%CI: 0.758–0.922) in the validation set. A nomogram that incorporated the Rad score with two selected clinical factors (Ki-67 index and histological grade) was constructed and yielded better discrimination compared with Rad score (p = 0.374, Delong test), with an AUC of 0.945 (95%CI: 0.904–0.987) in the training set and 0.868 (95%CI: 0.789–0.948; p = 0.123) in the validation set. Moreover, calibration with the p-value of 0.732 using Hosmer–Lemeshow test demonstrated good agreement, and the DCA verified the benefits of the nomogram.

Conclusion

Post largescale validation, the clinicoradiomic nomogram may have the potential to be used as a non-invasive tool for determination of HER2 expression status in clinical HER2-targeted therapy prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09967-6.

Intra- and Peritumoral Radiomics Model Based on Early DCE-MRI for Preoperative Prediction of Molecular Subtypes in Invasive Ductal Breast Carcinoma: A Multitask Machine Learning Study

Purpose

The aim of this study is to investigate radiomics features extracted from the optimal peritumoral region and the intratumoral area on the early phase of dynamic contrast-enhanced MRI (DCE-MRI) for predicting molecular subtypes of invasive ductal breast carcinoma (IDBC).

Methods

A total of 422 IDBC patients with immunohistochemical and fluorescence in situ hybridization results from two hospitals (Center 1: 327 cases, Center 2: 95 cases) who underwent preoperative DCE-MRI were retrospectively enrolled. After image preprocessing, radiomic features were extracted from the intratumoral area and four peritumoral regions on DCE-MRI from two centers, and selected the optimal peritumoral region. Based on the intratumoral, peritumoral radiomics features, and clinical–radiological characteristics, five radiomics models were constructed through support vector machine (SVM) in multiple classification tasks related to molecular subtypes and visualized by nomogram. The performance of radiomics models was evaluated by receiver operating characteristic curves, confusion matrix, calibration curves, and decision curve analysis.

Results

A 6-mm peritumoral size was defined the optimal peritumoral region in classification tasks of hormone receptor (HR)-positive vs others, triple-negative breast cancer (TNBC) vs others, and HR-positive vs human epidermal growth factor receptor 2 (HER2)-enriched vs TNBC, and 8 mm was applied in HER2-enriched vs others. The combined clinical–radiological and radiomics models in three binary classification tasks (HR-positive vs others, HER2-enriched vs others, TNBC vs others) obtained optimal performance with AUCs of 0.838, 0.848, and 0.930 in the training cohort, respectively; 0.827, 0.813, and 0.879 in the internal test cohort, respectively; and 0.791, 0.707, and 0.852 in the external test cohort, respectively.

Conclusion

Radiomics features in the intratumoral and peritumoral regions of IDBC on DCE-MRI had a potential to predict the HR-positive, HER2-enriched, and TNBC molecular subtypes preoperatively.

Prediction Breast Molecular Typing of Invasive Ductal Carcinoma Based on Dynamic Contrast Enhancement Magnetic Resonance Imaging Radiomics Characteristics: A Feasibility Study

Objective

To investigate the feasibility of radiomics in predicting molecular subtype of breast invasive ductal carcinoma (IDC) based on dynamic contrast enhancement magnetic resonance imaging (DCE-MRI).

Methods

A total of 303 cases with pathologically confirmed IDC from January 2018 to March 2021 were enrolled in this study, including 223 cases from Fudan University Shanghai Cancer Center (training/test set) and 80 cases from Shaoxing Central Hospital (validation set). All the cases were classified as HR+/Luminal, HER2-enriched, and TNBC according to immunohistochemistry. DCE-MRI original images were treated by semi-automated segmentation to initially extract original and wavelet-transformed radiomic features. The extended logistic regression with least absolute shrinkage and selection operator (LASSO) penalty was applied to identify the optimal radiomic features, which were then used to establish predictive models combined with significant clinical risk factors. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis were adopted to evaluate the effectiveness and clinical benefit of the models established.

Results

Of the 223 cases from Fudan University Shanghai Cancer Center, HR+/Luminal cancers were diagnosed in 116 cases (52.02%), HER2-enriched in 71 cases (31.84%), and TNBC in 36 cases (16.14%). Based on the training set, 788 radiomic features were extracted in total and 8 optimal features were further identified, including 2 first-order features, 1 gray-level run length matrix (GLRLM), 4 gray-level co-occurrence matrices (GLCM), and 1 3D shape feature. Three multi-class classification models were constructed by extended logistic regression: clinical model (age, menopause, tumor location, Ki-67, histological grade, and lymph node metastasis), radiomic model, and combined model. The macro-average areas under the ROC curve (macro-AUC) for the three models were 0.71, 0.81, and 0.84 in the training set, 0.73, 0.81, and 0.84 in the test set, and 0.76, 0.82, and 0.83 in the validation set, respectively.

Conclusion

The DCE-MRI-based radiomic features are significant biomarkers for distinguishing molecular subtypes of breast cancer noninvasively. Notably, the classification performance could be improved with the fusion analysis of multi-modal features.

Potential of the Non-Contrast-Enhanced Chest CT Radiomics to Distinguish Molecular Subtypes of Breast Cancer: A Retrospective Study

Objectives

The molecular subtype plays an important role in breast cancer, which is the main reference to guide treatment and is closely related to prognosis. The objective of this study was to explore the potential of the non-contrast-enhanced chest CT-based radiomics to predict breast cancer molecular subtypes non-invasively.

Methods

A total of 300 breast cancer patients (153 luminal types and 147 non-luminal types) who underwent routine chest CT examination were included in the study, of which 220 cases belonged to the training set and 80 cases to the time-independent test set. Identification of the molecular subtypes is based on immunohistochemical staining of postoperative tissue samples. The region of interest (ROI) of breast masses was delineated on the continuous slices of CT images. Forty-two models to predict the luminal type of breast cancer were established by the combination of six feature screening methods and seven machine learning classifiers; 5-fold cross-validation (cv) was used for internal validation. Finally, the optimal model was selected for external validation on the independent test set. In addition, we also took advantage of SHapley Additive exPlanations (SHAP) values to make explanations of the machine learning model.

Results

During internal validation, the area under the curve (AUC) values for different models ranged from 0.599 to 0.842, and the accuracy ranged from 0.540 to 0.775. Eventually, the LASSO_SVM combination was selected as the final model, which included 9 radiomics features. The AUC, accuracy, sensitivity, and specificity of the model to distinguish luminal from the non-luminal type were 0.842 [95% CI: 0.728−0.957], 0.773, 0.818, and 0.773 in the training set and 0.757 [95% CI: 0.640–0.866], 0.713, 0.767, and 0.676 in the test set.

Conclusion

The radiomics based on chest CT may provide a new idea for the identification of breast cancer molecular subtypes.

Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques

Objectives

The performance of multiparametric MRI-based radiomics models for predicting H3 K27M mutant status in diffuse midline glioma (DMG) has not been thoroughly evaluated. The optimal combination of multiparametric MRI and machine learning techniques remains undetermined. We compared the performance of various radiomics models across different MRI sequences and different machine learning techniques.

Methods

A total of 102 patients with pathologically confirmed DMG were retrospectively enrolled (27 with H3 K27M-mutant and 75 with H3 K27M wild-type). Radiomics features were extracted from eight sequences, and 18 feature sets were conducted by independent combination. There were three feature matrix normalization algorithms, two dimensionality-reduction methods, four feature selectors, and seven classifiers, consisting of 168 machine learning pipelines. Radiomics models were established across different feature sets and machine learning pipelines. The performance of models was evaluated using receiver operating characteristic curves with area under the curve (AUC) and compared with DeLong’s test.

Results

The multiparametric MRI-based radiomics models could accurately predict the H3 K27M mutant status in DMG (highest AUC: 0.807–0.969, for different sequences or sequence combinations). However, the results varied significantly between different machine learning techniques. When suitable machine learning techniques were used, the conventional MRI-based radiomics models shared similar performance to advanced MRI-based models (highest AUC: 0.875–0.915 vs. 0.807–0.926; DeLong’s test, p > 0.05). Most models had a better performance when generated with a combination of MRI sequences. The optimal model in the present study used a combination of all sequences (AUC = 0.969).

Conclusions

The multiparametric MRI-based radiomics models could be useful for predicting H3 K27M mutant status in DMG, but the performance varied across different sequences and machine learning techniques.

Gonadotropin-releasing hormone receptor inhibits triple-negative breast cancer proliferation and metastasis

BACKGROUND

Gonadotropin-releasing hormone receptor (GnRHR) is expressed in several malignant tumors and inhibits the proliferation and metastasis of cancer cells, but its role in triple-negative breast cancers (TNBCs) is unclear. This study investigated the biological effects of GnRHR and their influence on TNBC prognosis.

METHODS

The GSE21653 database was used to obtain information about GnRHR expression and clinicopathological factors in patients with TNBC. GnRHR was activated in cultured MDA-MB-231 and MDA-MB-468 cells by leuprolide acetate and antagonized by elagolix sodium. Cell proliferation was assessed by the cell counting kit-8 and colony formation assays. Cell metastasis was detected by the wound healing assay and Transwell assay. Apoptosis and the cell cycle were investigated by flow cytometry. GnRHR protein expression was determined by western blotting.

RESULTS

GnRHR mRNA expression was significantly higher in patients with TNBC than in hormone receptor+/human epidermal growth factor receptor (HER)2- and HER2+ patients with breast cancer. Patients with high GnRHR expression had significantly better disease-free survival than those with lower expression. Activated GnRHR significantly inhibited cell proliferation and metastasis, increased apoptosis, and enhanced GnRHR protein expression levels.

CONCLUSION

GnRHR inhibits TNBC proliferation and metastasis, suggesting it could be targeted for TNBC treatment.