Diagnosis of triple negative breast cancer based on radiomics signatures extracted from preoperative contrast-enhanced chest computed tomography

Assessment of CuFeSe2 ternary nanozymes for multimodal triple negative breast cancer theranostics

Triple negative breast cancer (TNBC) remains a challenge for clinical diagnosis and therapy due to its poor prognosis and high mortality rate. Hence, new methods to achieve TNBC imaging and imaging-guided TNBC therapy are urgently needed. Currently, the combination of chemotherapy with phototherapy/catalytic therapy has become a promising strategy for cancer treatment. Here, multifunctional CuFeSe2 ternary nanozymes (CuFeSe2-AMD3100-Gem nanosheets) were prepared as high-performance nanotheranostic agents for imaging-guided synergistic therapy of TNBC in vitro and in vivo. CuFeSe2-AMD3100-Gem nanosheets not only exhibited outstanding CXCR4-targeted capability and superior photothermal properties, but also produced exact chemical cytotoxicity through the loading of the chemotherapy drug Gemcitabine. Specifically, the CuFeSe2-AMD3100-Gem nanosheets simultaneously possessed peroxidase-like activities capable of converting endogenous H2O2 to hydroxyl radicals (•OH), which could be significantly enhanced under light irradiation. Furthermore, these nanosheets showed remarkable multimodal imaging ability for magnetic resonance imaging (MRI), computed tomography (CT) and infrared thermography in TNBC tumor-bearing mice (4T1 cells). More importantly, the in vitro and in vivo results verified the significant synergistic anticancer effect of the CuFeSe2-AMD3100-Gem nanosheets by combining photothermal therapy and enzyme catalytic therapy with chemotherapy. In conclusion, these advantages demonstrate the powerful potential of CuFeSe2 ternary nanozymes for imaging-guided synergistic photothermal/catalytic/chemical therapy for TNBC.

CT-based radiomics for predicting breast cancer radiotherapy side effects

Skin inflammation with the potential sequel of moist epitheliolysis and edema constitute the most frequent breast radiotherapy (RT) acute side effects. The aim of this study was to compare the predictive value of tissue-derived radiomics features to the total breast volume (TBV) for the moist cells epitheliolysis as a surrogate for skin inflammation, and edema. Radiomics features were extracted from computed tomography (CT) scans of 252 breast cancer patients from two volumes of interest: TBV and glandular tissue (GT). Machine learning classifiers were trained on radiomics and clinical features, which were evaluated for both side effects. The best radiomics model was a least absolute shrinkage and selection operator (LASSO) classifier, using TBV features, predicting moist cells epitheliolysis, achieving an area under the receiver operating characteristic (AUROC) of 0.74. This was comparable to TBV breast volume (AUROC of 0.75). Combined models of radiomics and clinical features did not improve performance. Exclusion of volume-correlated features slightly reduced the predictive performance (AUROC 0.71). We could demonstrate the general propensity of planning CT-based radiomics models to predict breast RT-dependent side effects. Mammary tissue was more predictive than glandular tissue. The radiomics features performance was influenced by their high correlation to TBV volume.

Prediction of the axillary lymph-node metastatic burden of breast cancer by 18F-FDG PET/CT-based radiomics

BACKGROUND

The axillary lymph-node metastatic burden is closely associated with treatment decisions and prognosis in breast cancer patients. This study aimed to explore the value of 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT)-based radiomics in combination with ultrasound and clinical pathological features for predicting axillary lymph-node metastatic burden in breast cancer.

METHODS

A retrospective analysis was conducted and involved 124 patients with pathologically confirmed early-stage breast cancer who had undergone 18F-FDG PET/CT examination. The ultrasound, PET/CT, and clinical pathological features of all patients were analysed, and radiomic features from PET images were extracted to establish a multi-parameter predictive model.

RESULTS

The ultrasound lymph-node positivity rate and PET lymph-node positivity rate in the high nodal burden group were significantly higher than those in the low nodal burden group (χ2 = 19.867, p < 0.001; χ2 = 33.025, p < 0.001). There was a statistically significant difference in the PET-based radiomics score (RS) for predicting axillary lymph-node burden between the high and low lymph-node burden groups. (-1.04 ± 0.41 vs. -1.47 ± 0.41, t = -4.775, p < 0.001). The ultrasound lymph-node positivity (US_LNM) (odds ratio [OR] = 3.264, 95% confidence interval [CI] = 1.022-10.423), PET lymph-node positivity (PET_LNM) (OR = 14.242, 95% CI = 2.960-68.524), and RS (OR = 5.244, 95% CI = 3.16-20.896) are all independent factors associated with high lymph-node burden (p < 0.05). The area under the curve (AUC) of the multi-parameter (MultiP) model was 0.895, which was superior to those of US_LNM, PET_LNM, and RS models (AUC = 0.703, 0.814, 0.773, respectively), with statistically significant differences (Z = 2.888, 3.208, 3.804, respectively; p = 0.004, 0.002, < 0.001, respectively). Decision curve analysis indicated that the MultiP model provided a higher net benefit for all patients.

CONCLUSION

A MultiP model based on PET-based radiomics was able to effectively predict axillary lymph-node metastatic burden in breast cancer.

TRIAL REGISTRATION

This study was registered with ClinicalTrials.gov (registration number: NCT05826197) on May 7, 2023.

The role of FDG PET/CT radiomics in the prediction of pathological response to neoadjuvant treatment in patients with esophageal cancer

Background

Attainment of a complete histopathological response following neoadjuvant therapy has been associated with favorable long-term survival outcomes in esophageal cancer patients. We investigated the ability of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) radiomic features to predict the pathological response to neoadjuvant treatment in patients with esophageal cancer.

Materials and methods

A retrospective review of medical records of patients with locally advanced resectable esophageal or esophagogastric junctional cancers. Included patients had a baseline FDG PET/CT scan and underwent Chemoradiotherapy for Oesophageal Cancer Followed by Surgery Study (CROSS) protocol followed by surgery. Four demographic variables and 107 PET radiomic features were extracted and analyzed using univariate and multivariate analyses to predict response to neoadjuvant therapy.

Results

Overall, 53 FDG-avid primary esophageal cancer lesions were segmented and radiomic features were extracted. Seventeen radiomic features and 2 non-radiomics variables were found to exhibit significant differences between neoadjuvant therapy responders and non-responders. An unsupervised hierarchical clustering analysis using these 19 variables classified patients in a manner significantly associated with response to neoadjuvant treatment (p < 0.01).

Conclusion

Our findings highlight the potential of FDG PET/CT radiomic features as a predictor for the response to neoadjuvant therapy in esophageal cancer patients. The combination of these radiomic features with select non-radiomic variables provides a model for stratifying patients based on their likelihood to respond to neoadjuvant treatment.

Utilizing grayscale ultrasound-based radiomics nomogram for preoperative identification of triple negative breast cancer

PURPOSE

This study aimed to develop a radiomics nomogram based on grayscale ultrasound (US) to distinguish triple-negative breast cancer (TNBC) from non-triple-negative breast cancer (NTNBC) prior to surgery.

METHODS

A retrospective analysis of 454 breast carcinoma patients confirmed by pathology was conducted, with 317 patients in the training dataset (59 with TNBC) and 137 patients in the validation dataset (27 with TNBC). Clinical information, conventional US features, and radiomics features were collected, and the Radscore model was constructed after feature selection. Independent risk factors were identified using univariate and multivariate logistic regression analysis. The nomogram model was assessed using the receiver operating characteristic (ROC) curve analysis, calibration curve, decision curve analysis (DCA), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).

RESULTS

Tumor shape, margin, and calcification were independent risk factors in the clinical prediction model. Additionally, 16 radiomics features were selected to construct the Radscore model out of a total of 474 extracted features. The radiomics nomogram model, which incorporated tumor shape, margin, calcification, and Radscore, achieved an AUC value of 0.837 in the training dataset and 0.813 in the validation dataset, outperforming both the Radscore and clinical models in terms of predictive performance. The significant improvement of NRI and IDI indicated that the Radscore may be useful biomarkers for TNBC.

CONCLUSION

The US-based radiomics nomogram showed satisfactory preoperative prediction of TNBC.

Radiologic imaging biomarkers in triple-negative breast cancer: a literature review about the role of artificial intelligence and the way forward

Breast cancer is one of the most common and deadly cancers in women. Triple-negative breast cancer (TNBC) accounts for approximately 10%-15% of breast cancer diagnoses and is an aggressive molecular breast cancer subtype associated with important challenges in its diagnosis, treatment, and prognostication. This poses an urgent need for developing more effective and personalized imaging biomarkers for TNBC. Towards this direction, artificial intelligence (AI) for radiologic imaging holds a prominent role, leveraging unique advantages of radiologic breast images, being used routinely for TNBC diagnosis, staging, and treatment planning, and offering high-resolution whole-tumour visualization, combined with the immense potential of AI to elucidate anatomical and functional properties of tumours that may not be easily perceived by the human eye. In this review, we synthesize the current state-of-the-art radiologic imaging applications of AI in assisting TNBC diagnosis, treatment, and prognostication. Our goal is to provide a comprehensive overview of radiomic and deep learning-based AI developments and their impact on advancing TNBC management over the last decade (2013-2024). For completeness of the review, we start with a brief introduction of AI, radiomics, and deep learning. Next, we focus on clinically relevant AI-based diagnostic, predictive, and prognostic models for radiologic breast images evaluated in TNBC. We conclude with opportunities and future directions for AI towards advancing diagnosis, treatment response predictions, and prognostic evaluations for TNBC.

Construction of a comprehensive predictive model for axillary lymph node metastasis in breast cancer: a retrospective study

PURPOSE

The accurate assessment of axillary lymph node metastasis (LNM) in early-stage breast cancer (BC) is of great importance. This study aimed to construct an integrated model based on clinicopathology, ultrasound, PET/CT, and PET radiomics for predicting axillary LNM in early stage of BC.

MATERIALS AND METHODS

124 BC patients who underwent 18 F-fluorodeoxyglucose (18 F-FDG) PET/CT and whose diagnosis were confirmed by surgical pathology were retrospectively analyzed and included in this study. Ultrasound, PET and clinicopathological features of all patients were analyzed, and PET radiomics features were extracted to establish an ultrasound model (clinicopathology and ultrasound; model 1), a PET model (clinicopathology, ultrasound, and PET; model 2), and a comprehensive model (clinicopathology, ultrasound, PET, and radiomics; model 3), and the diagnostic efficacy of each model was evaluated and compared.

RESULTS

The T stage, US_BIRADS, US_LNM, and PET_LNM in the positive axillary LNM group was significantly higher than that of in the negative LNM group (P = 0.013, P = 0.049, P < 0.001, P < 0.001, respectively). Radiomics score for predicting LNM (RS_LNM) for the negative LNM and positive LNM were statistically significant difference (-1.090 ± 0.448 vs. -0.693 ± 0.344, t = -4.720, P < 0.001), and the AUC was 0.767 (95% CI: 0.674-0.861). The ROC curves showed that model 3 outperformed model 1 for the sensitivity (model 3 vs. model 1, 82.86% vs. 48.57%), and outperformed model 2 for the specificity (model 3 vs. model 2, 82.02% vs. 68.54%) in the prediction of LNM. The AUC of mode 1, model 2 and model 3 was 0.687, 0.826 and 0.874, and the Delong test showed the AUC of model 3 was significantly higher than that of model 1 and model 2 (P < 0.05). Decision curve analysis showed that model 3 resulted in a higher degree of net benefit for all the patients than model 1 and model 2.

CONCLUSION

The use of a comprehensive model based on clinicopathology, ultrasound, PET/CT, and PET radiomics can effectively improve the diagnostic efficacy of axillary LNM in BC.

TRIAL REGISTRATION

This study was registered at ClinicalTrials Gov (number NCT05826197) on 7th, May 2023.

Comparison of radiomics models and dual-energy material decomposition to decipher abdominal lymphoma in contrast-enhanced CT

PURPOSE

The radiologists' workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes.

METHODS

Retrospectively, 72 patients [m, 47; age, 63.5 (27-87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test.

RESULTS

About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435-0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000-1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model.

CONCLUSIONS

Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment.

Integrating Preoperative Computed Tomography and Clinical Factors for Lymph Node Metastasis Prediction in Esophageal Squamous Cell Carcinoma by Feature-Wise Attentional Graph Neural Network

PURPOSE

This study aimed to propose a regional lymph node (LN) metastasis prediction model for patients with esophageal squamous cell carcinoma (ESCC) that can learn and adaptively integrate preoperative computed tomography (CT) image features and nonimaging clinical parameters.

METHODS AND MATERIALS

Contrast-enhanced CT scans taken 2 weeks before surgery and 20 clinical factors, including general, pathologic, hematological, and diagnostic information, were collected from 357 patients with ESCC between October 2013 and November 2018. There were 999 regional LNs (857 negative, 142 positive) with pathologically confirmed status after surgery. All LNs were randomly divided into a training set (n = 738) and a validation set (n = 261) for testing. The feature-wise attentional graph neural network (FAGNN) was composed of (1) deep image feature extraction by the encoder of 3-dimensional UNet and high-level nonimaging factor representation by the clinical parameter encoder; (2) a feature-wise attention module for feature embedding with learnable adaptive weights; and (3) a graph attention layer to integrate the embedded features for final LN level metastasis prediction.

RESULTS

Among the 4 models we constructed, FAGNN using both CT and clinical parameters as input is the model with the best performance, and the area under the curve (AUC) reaches 0.872, which is better than manual CT diagnosis method, multivariable model using CT only (AUC = 0.797), multivariable model with combined CT and clinical parameters (AUC = 0.846), and our FAGNN using CT only (AUC = 0.853).

CONCLUSIONS

Our adaptive integration model improved the metastatic LN prediction performance based on CT and clinical parameters. Our model has the potential to foster effective fusion of multisourced parameters and to support early prognosis and personalized surgery or radiation therapy planning in patients with ESCC.

FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study

OBJECTIVES

To evaluate if radiomics with machine learning can differentiate between F-18-fluorodeoxyglucose (FDG)-avid breast cancer metastatic lymphadenopathy and FDG-avid COVID-19 mRNA vaccine-related axillary lymphadenopathy.

MATERIALS AND METHODS

We retrospectively analyzed FDG-positive, pathology-proven, metastatic axillary lymph nodes in 53 breast cancer patients who had PET/CT for follow-up or staging, and FDG-positive axillary lymph nodes in 46 patients who were vaccinated with the COVID-19 mRNA vaccine. Radiomics features (110 features classified into 7 groups) were extracted from all segmented lymph nodes. Analysis was performed on PET, CT, and combined PET/CT inputs. Lymph nodes were randomly assigned to a training (n = 132) and validation cohort (n = 33) by 5-fold cross-validation. K-nearest neighbors (KNN) and random forest (RF) machine learning models were used. Performance was evaluated using an area under the receiver-operator characteristic curve (AUC-ROC) score.

RESULTS

Axillary lymph nodes from breast cancer patients (n = 85) and COVID-19-vaccinated individuals (n = 80) were analyzed. Analysis of first-order features showed statistically significant differences (p < 0.05) in all combined PET/CT features, most PET features, and half of the CT features. The KNN model showed the best performance score for combined PET/CT and PET input with 0.98 (± 0.03) and 0.88 (± 0.07) validation AUC, and 96% (± 4%) and 85% (± 9%) validation accuracy, respectively. The RF model showed the best result for CT input with 0.96 (± 0.04) validation AUC and 90% (± 6%) validation accuracy.

CONCLUSION

Radiomics features can differentiate between FDG-avid breast cancer metastatic and FDG-avid COVID-19 vaccine-related axillary lymphadenopathy. Such a model may have a role in differentiating benign nodes from malignant ones.

KEY POINTS

• Patients who were vaccinated with the COVID-19 mRNA vaccine have shown FDG-avid reactive axillary lymph nodes in PET-CT scans. • We evaluated if radiomics and machine learning can distinguish between FDG-avid metastatic axillary lymphadenopathy in breast cancer patients and FDG-avid reactive axillary lymph nodes. • Combined PET and CT radiomics data showed good test AUC (0.98) for distinguishing between metastatic axillary lymphadenopathy and post-COVID-19 vaccine-associated axillary lymphadenopathy. Therefore, the use of radiomics may have a role in differentiating between benign from malignant FDG-avid nodes.

A Simultaneous Multiparametric 18F-FDG PET/MRI Radiomics Model for the Diagnosis of Triple Negative Breast Cancer

Simple Summary

In this study, we aimed to build a machine-learning predictive model for the identification of triple negative breast cancer, the most aggressive subtype, using quantitative parameters and radiomics features extracted from tumor lesions on hybrid PET/MRI. The good performance of the model supports the hypothesis that hybrid PET/MRI can provide quantitative data able to non-invasively detect tumor biological characteristics using artificial intelligence software and further encourages the conduction of additional studies for this purpose.

Abstract

Purpose: To investigate whether a machine learning (ML)-based radiomics model applied to ¹⁸F-FDG PET/MRI is effective in molecular subtyping of breast cancer (BC) and specifically in discriminating triple negative (TN) from other molecular subtypes of BC. Methods: Eighty-six patients with 98 BC lesions (Luminal A = 10, Luminal B = 51, HER2+ = 12, TN = 25) were included and underwent simultaneous ¹⁸F-FDG PET/MRI of the breast. A 3D segmentation of BC lesion was performed on T2w, DCE, DWI and PET images. Quantitative diffusion and metabolic parameters were calculated and radiomics features extracted. Data were selected using the LASSO regression and used by a fine gaussian support vector machine (SVM) classifier with a 5-fold cross validation for identification of TNBC lesions. Results: Eight radiomics models were built based on different combinations of quantitative parameters and/or radiomic features. The best performance (AUROC 0.887, accuracy 82.8%, sensitivity 79.7%, specificity 86%, PPV 85.3%, NPV 80.8%) was found for the model combining first order, neighborhood gray level dependence matrix and size zone matrix-based radiomics features extracted from ADC and PET images. Conclusion: A ML-based radiomics model applied to ¹⁸F-FDG PET/MRI is able to non-invasively discriminate TNBC lesions from other BC molecular subtypes with high accuracy. In a future perspective, a “virtual biopsy” might be performed with radiomics signatures.

Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer

Background: To develop and validate a radiomics model based on ¹⁸F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative ¹⁸F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative ¹⁸F-FDG PET/CT radiomic 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.

Radiomics in Triple Negative Breast Cancer: New Horizons in an Aggressive Subtype of the Disease

In the last decade, the analysis of the medical images has evolved significantly, applications and tools capable to extract quantitative characteristics of the images beyond the discrimination capacity of the investigator's eye being developed. The applications of this new research field, called radiomics, presented an exponential growth with direct implications in the diagnosis and prediction of response to therapy. Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype with a severe prognosis, despite the aggressive multimodal treatments applied according to the guidelines. Radiomics has already proven the ability to differentiate TNBC from fibroadenoma. Radiomics features extracted from digital mammography may also distinguish between TNBC and non-TNBC. Recent research has identified three distinct subtypes of TNBC using IRM breast images voxel-level radiomics features (size/shape related features, texture features, sharpness). The correlation of these TNBC subtypes with the clinical response to neoadjuvant therapy may lead to the identification of biomarkers in order to guide the clinical decision. Furthermore, the variation of some radiomics features in the neoadjuvant settings provides a tool for the rapid evaluation of treatment efficacy. The association of radiomics features with already identified biomarkers can generate complex predictive and prognostic models. Standardization of image acquisition and also of radiomics feature extraction is required to validate this method in clinical practice.

Recent advances of nanotechnology in the diagnosis and therapy of triple-negative breast cancer (TNBC)

BACKGROUND

The development of advanced treatment of triple-negative breast cancer (TNBC) is the utmost need of an era. TNBC is recognized as the most aggressive, metastatic cancer and the leading cause of mortality in females worldwide. The lack of expression of triple receptors namely, estrogen, progesterone, and human epidermal receptor2 defined TNBC.

OBJECTIVE

The current review introduced the novel biomarkers such as miRNA and family, PD1, EGFR, VEGF, TILs, P53, AR and PI3K, etc. contributed significantly to the prognosis and diagnosis of TNBC. Once diagnosed the utilization advanced approaches available for TNBC because of the limitations of chemotherapy. Novel approaches include lipid-based (liposomes, SLN, NLC, and SNEDDS), polymer-based (micelle, nanoparticles, dendrimers, and quantum dots), advanced nanocarriers such as (exosomes, antibody and peptide-drug conjugates), carbon-based nanocarriers (Carbon nanotubes, and graphene oxide). Lipid-based delivery is used for excellent carriers for hydrophobic drugs, biocompatibility, and lesser systemic toxicities than chemotherapeutic agents. Polymer-based approaches are preferred over lipids for providing longer circulation time, nanosize, high loading efficiency, high linking; avoiding the expulsion of drugs, targeted action, diagnostic and biosensing abilities. Advanced approaches like exosomes, conjugated moieties are preferred over polymeric for possessing potency, high penetrability, biomarkers, and avoiding the toxicity of tissues. Carbon-based gained wide applicability for their unique properties like a versatile carrier, prognostic, diagnostic, sensing, photodynamic, and photothermal characteristics.

CONCLUSION

The survival rate can be increased by utilizing several kinds of biomarkers. The advanced approaches can also be significantly useful in the prognosis and theranostic of triple-negative breast cancer. One of the biggest successes in treating with nanotechnology-based approaches is the marked reduction of systemic toxicity with high therapeutic effectiveness compared with chemotherapy, surgery, etc. The requirements such as prompt diagnosis, longer circulation time, high efficiency, and high potency, can be fulfilled with these nanocarriers.

AI in spotting high-risk characteristics of medical imaging and molecular pathology

Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, medical imaging can be further mined for detailed pathological information. Many studies have shown that the macroscopic imaging characteristics of tumors are closely related to microscopic gene, protein and molecular changes. In order to explore the function of artificial intelligence algorithms in in-depth analysis of medical imaging information, this paper reviews the articles published in recent years from three perspectives: medical imaging analysis method, clinical applications and the development of medical imaging in the direction of pathological molecular prediction. We believe that AI-aided medical imaging analysis will be extensively contributing to precise and efficient clinical decision.

Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis

INTRODUCTION

Breast cancer has four distinct molecular subtypes which are discriminated using gene expression profiling following biopsy. Radiogenomics is an emerging field which utilises diagnostic imaging to reveal genomic properties of disease. We aimed to perform a systematic review of the current literature to evaluate the value radiomics in differentiating breast cancers into their molecular subtypes using diagnostic imaging.

METHODS

A systematic review was performed as per PRISMA guidelines. Studies assessing radiomictumour analysis in differentiatingbreast cancer molecular subtypeswere included. Quality was assessed using the radiomics quality score (RQS). Diagnostic sensitivity and specificity of radiomic analyses were included for meta-analysis; Study specific sensitivity and specificity were retrieved and summary ROC analysis were performed to compile pooled sensitivities and specificities.

RESULTS

Forty-one studies were included. Overall, there were 10,090 female patients (mean age of 47.6 ± 11.7 years, range: 21-93) and molecular subtypewas reported in 7,693 of cases, with Luminal A (LABC), Luminal B (LBBC), Human Epidermal Growth Factor Receptor-2 overexpressing (HER2+), and Triple Negative (TNBC) breast cancers representing 51.3%, 19.9%, 12.3% and 16.3% of tumour respectively. Seven studies provided radiomic analysis to determine molecular subtypes using mammography to differentiateTNBCvs.others (sensitivity: 0.82,specificity:0.79). Thirty-five studies reported on radiomic analysis of magnetic resonance imaging (MRI); LABC versus others(sensitivity:0.78,specificity:0.83),HER2+versusothers(sensitivity:0.87,specificity:0.88), andLBBCversusTNBC (sensitivity: 0.79,specificity:0.88) respectively.

CONCLUSION

Radiomic tumour assessment of contemporary breast imaging provide a novel option in determining breast cancer molecular subtypes. However, amelioration of such techniques are required and genetic expression assessment will remain the gold standard.