Integration of radiogenomic features for early prediction of pathological complete response in patients with triple-negative breast cancer and identification of potential therapeutic targets

Early prediction of neoadjuvant therapy response in breast cancer using MRI-based neural networks: data from the ACRIN 6698 trial and a prospective Chinese cohort

BACKGROUND

Early prediction of treatment response to neoadjuvant therapy (NAT) in breast cancer patients can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a MRI-based enhanced self-attention network (MESN) for predicting pathological complete response (pCR) based on longitudinal images at the early stage of NAT.

METHODS

Two imaging datasets were utilized: a subset from the ACRIN 6698 trial (dataset A, n = 227) and a prospective collection from a Chinese hospital (dataset B, n = 245). These datasets were divided into three cohorts: an ACRIN 6698 training cohort (n = 153) from dataset A, an ACRIN 6698 test cohort (n = 74) from dataset A, and an external test cohort (n = 245) from dataset B. The proposed MESN allowed for the integration of multiple timepoint features and extraction of dynamic information from longitudinal MR images before and after early-NAT. We also constructed the Pre model based on pre-NAT MRI features. Clinicopathological characteristics were added to these image-based models to create integrated models (MESN-C and Pre-C), and their performance was evaluated and compared.

RESULTS

The MESN-C yielded area under the receiver operating characteristic curve (AUC) values of 0.944 (95% CI: 0.906 - 0.973), 0.903 (95%CI: 0.815 - 0.965), and 0.861 (95%CI: 0.811 - 0.906) in the ACRIN 6698 training, ACRIN 6698 test and external test cohorts, respectively, which were significantly higher than those of the clinical model (AUC: 0.720 [95%CI: 0.587 - 0.842], 0.738 [95%CI: 0.669 - 0.796] for the two test cohorts, respectively; p < 0.05) and Pre-C (AUC: 0.697 [95%CI: 0.554 - 0.819], 0.726 [95%CI: 0.666 - 0.797] for the two test cohorts, respectively; p < 0.05). High AUCs of the MESN-C maintained in the ACRIN 6698 standard (AUC = 0.853 [95%CI: 0.676 - 1.000]) and experimental (AUC = 0.905 [95%CI: 0.817 - 0.993]) subcohorts, and the interracial and external subcohort (AUC = 0.861 [95%CI: 0.811 - 0.906]). Moreover, the MESN-C increased the positive predictive value from 48.6 to 71.3% compared with Pre-C model, and maintained a high negative predictive value (80.4-86.7%).

CONCLUSION

The MESN-C using longitudinal multiparametric MRI after a short-term therapy achieved favorable performance for predicting pCR, which could facilitate timely adjustment of treatment regimens, increasing the rates of pCR and avoiding toxic effects.

TRIAL REGISTRATION

Trial registration at https://www.chictr.org.cn/ .

REGISTRATION NUMBER

ChiCTR2000038578, registered September 24, 2020.

Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot

Background

Despite the improved survival rates of children with tetralogy of Fallot (TOF), various degrees of neurodevelopmental disorders persist. Currently, there is a lack of quantitative and objective imaging markers to assess the neurodevelopment of individuals with TOF. This study aimed to noninvasively examine potential quantitative imaging markers of TOF neurodevelopment by combining radiomics signatures and morphological features and to further clarify the relationship between imaging markers and clinical neurodevelopment metrics.

Methods

This study included 33 preschool children who had undergone surgical correction for TOF and 29 healthy controls (36 in the training cohort and 26 in the testing cohort), all of whom underwent three-dimensional T1-weighted high-resolution (T1-3D) head magnetic resonance imaging (MRI). Radiomics features were extracted by Pyradiomics to construct radiomics models, while surface morphometry (surface and volumetric) features were analyzed to build morphometry models. Merged models integrating radiomics and morphometry features were subsequently developed. The optimal discriminative radiomics signatures were identified via least absolute shrinkage and selection operator (LASSO). Machine learning classification models include support vector machine (SVM) with radial basis function (RBF) and multivariable logistic regression (MLR) models, both of which were used to evaluate the potential imaging biomarkers. Performances of models were evaluated based on their calibration and classification metrics. The area under the receiver operating characteristic curves (AUCs) of the models were evaluated using the Delong test. Neurodevelopmental assessments for children with corrected TOF were conducted with the Wechsler Preschool and Primary Scale of Intelligence-Fourth Edition (WPPSI-IV). Furthermore, the correlation of the significant discriminative indicators with clinical metrics and neurodevelopmental scales was evaluated.

Results

Twelve discriminative radiomics signatures, optimized for classification, were identified. The performance of the merged model (AUCs of 0.922 and 0.917 for the training set and test set with SVM, respectively) was superior to that of the single radiomics model (AUCs of 0.915 and 0.917 for the training set and test set with SVM, respectively) and that of the single morphometric models (AUCs of 0.803 and 0.756 for the training set and test set with SVM, respectively). The radiomics model demonstrated higher significance than did the morphometric models in training set with SVM (AUC: 0.915 vs. 0.803; P<0.001). Additionally, the significant indicators showed a correlation with clinical indicators and neurodevelopmental scales.

Conclusions

MRI-based radiomics features combined with morphometry features can provide complementary information to identify neurodevelopmental abnormalities in children with corrected TOF, which will provide potential evidence for clinical diagnosis and treatment.

Radiogenomics: bridging the gap between imaging and genomics for precision oncology

Genomics allows the tracing of origin and evolution of cancer at molecular scale and underpin modern cancer diagnosis and treatment systems. Yet, molecular biomarker-guided clinical decision-making encounters major challenges in the realm of individualized medicine, consisting of the invasiveness of procedures and the sampling errors due to high tumor heterogeneity. By contrast, medical imaging enables noninvasive and global characterization of tumors at a low cost. In recent years, radiomics has overcomes the limitations of human visual evaluation by high-throughput quantitative analysis, enabling the comprehensive utilization of the vast amount of information underlying radiological images. The cross-scale integration of radiomics and genomics (hereafter radiogenomics) has the enormous potential to enhance cancer decoding and act as a catalyst for digital precision medicine. Herein, we provide a comprehensive overview of the current framework and potential clinical applications of radiogenomics in patient care. We also highlight recent research advances to illustrate how radiogenomics can address common clinical problems in solid tumors such as breast cancer, lung cancer, and glioma. Finally, we analyze existing literature to outline challenges and propose solutions, while also identifying future research pathways. We believe that the perspectives shared in this survey will provide a valuable guide for researchers in the realm of radiogenomics aiming to advance precision oncology.

Use of Pretreatment Perfusion MRI-based Intratumoral Heterogeneity to Predict Pathologic Response of Triple-Negative Breast Cancer to Neoadjuvant Chemoimmunotherapy

Background Neoadjuvant chemoimmunotherapy (NACI) has significantly increased the rate of pathologic complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC), although predictors of response to this regimen have not been identified. Purpose To investigate pretreatment perfusion MRI-based radiomics as a predictive marker for pCR in patients with TNBC undergoing NACI. Materials and Methods This prospective study enrolled women with early-stage TNBC who underwent NACI at two different centers from August 2021 to July 2023. Pretreatment dynamic contrast-enhanced MRI scans obtained using scanners from multiple vendors were analyzed using the Tofts model to segment tumors and analyze pharmacokinetic parameters. Radiomics features were extracted from the rate constant for contrast agent plasma-to-interstitial transfer (or Ktrans), volume fraction of extravascular and extracellular space (Ve), and maximum contrast agent uptake rate (Slopemax) maps and analyzed using unsupervised correlation and least absolute shrinkage and selector operator, or LASSO, to develop a radiomics score. Score effectiveness was assessed using the area under the receiver operating characteristic curve (AUC), and multivariable logistic regression was used to develop a multimodal nomogram for enhanced prediction. The discrimination, calibration, and clinical utility of the nomogram were evaluated in an external test set. Results The training set included 112 female participants from center 1 (mean age, 52 years ± 11 [SD]), and the external test set included 83 female participants from center 2 (mean age, 47 years ± 11). The radiomics score demonstrated an AUC of 0.80 (95% CI: 0.70, 0.89) for predicting pCR. A nomogram incorporating the radiomics score, grade, and Ki-67 yielded an AUC of 0.86 (95% CI: 0.78, 0.94) in the test set. Associations were found between higher radiomics score (>0.25) and tumor size (P < .001), washout enhancement (P = .01), androgen receptor expression (P = .009), and programmed death ligand 1 expression (P = .01), demonstrating a correlation with tumor immune environment in participants with TNBC. Conclusion A radiomics score derived from pharmacokinetic parameters at pretreatment dynamic contrast-enhanced MRI exhibited good performance for predicting pCR in participants with TNBC undergoing NACI, and could potentially be used to enhance clinical decision making. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Rauch in this issue.

A preoperative radiogenomic model based on quantitative heterogeneity for predicting outcomes in triple-negative breast cancer patients who underwent neoadjuvant chemotherapy

BACKGROUND

Triple-negative breast cancer (TNBC) is highly heterogeneous, resulting in different responses to neoadjuvant chemotherapy (NAC) and prognoses among patients. This study sought to characterize the heterogeneity of TNBC on MRI and develop a radiogenomic model for predicting both pathological complete response (pCR) and prognosis.

MATERIALS AND METHODS

In this retrospective study, TNBC patients who underwent neoadjuvant chemotherapy at Fudan University Shanghai Cancer Center were enrolled as the radiomic development cohort (n = 315); among these patients, those whose genetic data were available were enrolled as the radiogenomic development cohort (n = 98). The study population of the two cohorts was randomly divided into a training set and a validation set at a ratio of 7:3. The external validation cohort (n = 77) included patients from the DUKE and I-SPY 1 databases. Spatial heterogeneity was characterized using features from the intratumoral subregions and peritumoral region. Hemodynamic heterogeneity was characterized by kinetic features from the tumor body. Three radiomics models were developed by logistic regression after selecting features. Model 1 included subregional and peritumoral features, Model 2 included kinetic features, and Model 3 integrated the features of Model 1 and Model 2. Two fusion models were developed by further integrating pathological and genomic features (PRM: pathology-radiomics model; GPRM: genomics-pathology-radiomics model). Model performance was assessed with the AUC and decision curve analysis. Prognostic implications were assessed with Kaplan‒Meier curves and multivariate Cox regression.

RESULTS

Among the radiomic models, the multiregional model representing multiscale heterogeneity (Model 3) exhibited better pCR prediction, with AUCs of 0.87, 0.79, and 0.78 in the training, internal validation, and external validation sets, respectively. The GPRM showed the best performance for predicting pCR in the training (AUC = 0.97, P = 0.015) and validation sets (AUC = 0.93, P = 0.019). Model 3, PRM and GPRM could stratify patients by disease-free survival, and a predicted nonpCR was associated with poor prognosis (P = 0.034, 0.001 and 0.019, respectively).

CONCLUSION

Multiscale heterogeneity characterized by DCE-MRI could effectively predict the pCR and prognosis of TNBC patients. The radiogenomic model could serve as a valuable biomarker to improve the prediction performance.

Investigating the effects of artificial intelligence on the personalization of breast cancer management: a systematic study

BACKGROUND

Providing appropriate specialized treatment to the right patient at the right time is considered necessary in cancer management. Targeted therapy tailored to the genetic changes of each breast cancer patient is a desirable feature of precision oncology, which can not only reduce disease progression but also potentially increase patient survival. The use of artificial intelligence alongside precision oncology can help physicians by identifying and selecting more effective treatment factors for patients.

METHOD

A systematic review was conducted using the PubMed, Embase, Scopus, and Web of Science databases in September 2023. We performed the search strategy with keywords, namely: Breast Cancer, Artificial intelligence, and precision Oncology along with their synonyms in the article titles. Descriptive, qualitative, review, and non-English studies were excluded. The quality assessment of the articles and evaluation of bias were determined based on the SJR journal and JBI indices, as well as the PRISMA2020 guideline.

RESULTS

Forty-six studies were selected that focused on personalized breast cancer management using artificial intelligence models. Seventeen studies using various deep learning methods achieved a satisfactory outcome in predicting treatment response and prognosis, contributing to personalized breast cancer management. Two studies utilizing neural networks and clustering provided acceptable indicators for predicting patient survival and categorizing breast tumors. One study employed transfer learning to predict treatment response. Twenty-six studies utilizing machine-learning methods demonstrated that these techniques can improve breast cancer classification, screening, diagnosis, and prognosis. The most frequent modeling techniques used were NB, SVM, RF, XGBoost, and Reinforcement Learning. The average area under the curve (AUC) for the models was 0.91. Moreover, the average values for accuracy, sensitivity, specificity, and precision were reported to be in the range of 90-96% for the models.

CONCLUSION

Artificial intelligence has proven to be effective in assisting physicians and researchers in managing breast cancer treatment by uncovering hidden patterns in complex omics and genetic data. Intelligent processing of omics data through protein and gene pattern classification and the utilization of deep neural patterns has the potential to significantly transform the field of complex disease management.

Pretreatment Sarcopenia and MRI-Based Radiomics to Predict the Response of Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer

The association between sarcopenia and the effectiveness of neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC) remains uncertain. This study aims to examine the potential of sarcopenia as a predictive factor for the response to NAC in TNBC, and to assess whether its combination with MRI radiomic signatures can improve the predictive accuracy. We collected clinical and pathological information, as well as pretreatment breast MRI and abdominal CT images, of 121 patients with TNBC who underwent NAC at our hospital between January 2012 and September 2021. The presence of pretreatment sarcopenia was assessed using the L3 skeletal muscle index. Clinical models were constructed based on independent risk factors identified by univariate regression analysis. Radiomics data were extracted on breast MRI images and the radiomics prediction models were constructed. We integrated independent risk factors and radiomic features to build the combined models. The results of this study demonstrated that sarcopenia is an independent predictive factor for NAC efficacy in TNBC. The combination of sarcopenia and MRI radiomic signatures can further improve predictive performance.

Pretreatment Pan-Immune-Inflammation Value (PIV) in Predicting Therapeutic Response and Clinical Outcomes of Neoadjuvant Immunochemotherapy for Esophageal Squamous Cell Carcinoma

PURPOSE

The pan-immune-inflammation value (PIV), which reflects the balance between the host immune and inflammatory status, is a readily available index for evaluating cancer outcomes. Until now, however, no study has demonstrated the clinical response of PIV to neoadjuvant immunochemotherapy (NICT) in esophageal squamous cell carcinoma (ESCC).

METHODS

This retrospective study included 218 patients with ESCC who underwent NICT. The relationship between PIV and therapeutic response (pathological complete response [PCR]) and clinical outcomes (overall survival [OS] and disease-free survival [DFS]) was examined. Cox proportional, hazard-regression analyses and the Kaplan-Meier method were used for survival analyses. Recursive partitioning analysis (RPA) was used to establish a novel risk stratification model.

RESULTS

Sixty-six patients (30.3%) achieved PCR after NICT. Using PCR as the endpoint of interest, patients were compared in groups based on the optimal threshold. PIV was closely related to PCR (odds ratio [OR] 0.311, 95% confidence interval [CI] 0.140-0.690, P = 0.004). Compared with patients in the low PIV cohort, patients with high PIV had worse 3-year OS (58.7% vs. 83.6%, P < 0.001) and DFS (51.9% vs. 79.1%, P < 0.001). PIV was an independent predictor of OS (hazard ratio [HR] 2.364, 95% CI 1.183-4.724, P = 0.015) and DFS (HR 1.729, 95% CI 1.026-2.913, P = 0.040). Three risk groups with varied DFS and OS were staged by using an RPA method, and the prognostication accuracy was considerably improved.

CONCLUSIONS

Pretreatment PIV can predict the therapeutic efficacy of NICT for ESCC. Because of better prognostic stratification, pretreatment PIV is a novel, sensitive, and effective indicator in ESCC receiving NICT. The prognostic results of PIV need to be verified in additional prospective studies.

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.

A novel immune-nutritional score predicts response to neoadjuvant immunochemotherapy after minimally invasive esophagectomy for esophageal squamous cell carcinoma

Background

The role of neoadjuvant immunochemotherapy (NICT) has gradually attracted attention in recent years. To date, sensitive and reliable blood indicators to forecast the therapeutic response are still lacking. This study aimed to conduct a novel predictive score based on a variety of peripheral hematological immune-nutritional indicators to predict the therapeutic response in esophageal squamous cell carcinoma (ESCC) receiving NICT.

Methods

There were 206 ESCC patients receiving NICT retrospectively recruited. With pathological complete response (pCR) as the dependent variable, independent risk variables of various peripheral blood immune-nutritional indexes were screened by logistic regression analyses to establish an integrative score.

Results

By logical regression analyses, lymphocyte to monocyte ratio (LMR) and body mass index (BMI) were independent risk factors among all immune-nutritional indices. Then, an integrative score named BMI-LMR score (BLS) was established. Compared with BMI or LMR, BLS was related to complications, especially for respiratory complication (P=0.012) and vocal cord paralysis (P=0.021). Among all patients, 61 patients (29.6%) achieved pCR after NICT. BLS was significantly related to pCR [odds ratio (OR)=0.269, P<0.001)]. Patients in high BLS cohort demonstrated higher 3-year overall survival (OS) (89.9% vs. 67.9%, P=0.001) and disease-free survival (DFS) (81.2% vs. 62.1%, P=0.001). BLS served as an independent factor of DFS [hazard ratio (HR) =2.044, P =0.020) and OS (HR =2.960, P =0.019).

Conclusion

The BLS, based on immune-nutritional indicators of BMI and LMR, employed as a straightforward, accurate, and useful indicator of pCR and prognostic prediction in ESCC patients undergoing NICT.

Development and validation of a radiogenomics model to predict axillary lymph node metastasis in breast cancer integrating MRI with transcriptome data: A multicohort study

Introduction

To develop and validate a radiogenomics model for predicting axillary lymph node metastasis (ALNM) in breast cancer compared to a genomics and radiomics model.

Methods

This retrospective study integrated transcriptomic data from The Cancer Genome Atlas with matched MRI data from The Cancer Imaging Archive for the same set of 111 patients with breast cancer, which were used as the training and testing groups. Fifteen patients from one hospital were enrolled as the external validation group. Radiomics features were extracted from dynamic contrast-enhanced (DCE)-MRI of breast cancer, and genomics features were derived from differentially expressed gene analysis of transcriptome data. Boruta was used for genomics and radiomics data dimension reduction and feature selection. Logistic regression was applied to develop genomics, radiomics, and radiogenomics models to predict ALNM. The performance of the three models was assessed by receiver operating characteristic curves and compared by the Delong test.

Results

The genomics model was established by nine genomics features, and the radiomics model was established by three radiomics features. The two models showed good discrimination performance in predicting ALNM in breast cancer, with areas under the curves (AUCs) of 0.80, 0.67, and 0.52 for the genomics model and 0.72, 0.68, and 0.71 for the radiomics model in the training, testing and external validation groups, respectively. The radiogenomics model integrated with five genomics features and three radiomics features had a better performance, with AUCs of 0.84, 0.75, and 0.82 in the three groups, respectively, which was higher than the AUC of the radiomics model in the training group and the genomics model in the external validation group (both P < 0.05).

Conclusion

The radiogenomics model combining radiomics features and genomics features improved the performance to predict ALNM in breast cancer.

Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting

Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced-MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BACKGROUND

To investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.

METHODS

Two radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial-temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.

RESULTS

Expression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).

CONCLUSIONS

Our results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis.