Improving prediction of response to neoadjuvant treatment in patients with breast cancer by combining liquid biopsies with multiparametric MRI: Protocol of the LIMA study-a multicentre prospective observational cohort study

Introduction The response to neoadjuvant chemotherapy (NAC) in breast cancer has important prognostic implications. Dynamic prediction of tumour regression by NAC may allow for adaption of the treatment plan before completion, or even before the start of treatment. Such predictions may help prevent overtreatment and related toxicity and correct for undertreatment with ineffective regimens. Current imaging methods are not able to fully predict the efficacy of NAC. To successfully improve response prediction, tumour biology and heterogeneity as well as treatment-induced changes have to be considered. In the LIMA study, multiparametric MRI will be combined with liquid biopsies. In addition to conventional clinical and pathological information, these methods may give complementary information at multiple time points during treatment. Aim To combine multiparametric MRI and liquid biopsies in patients with breast cancer to predict residual cancer burden (RCB) after NAC, in adjunct to standard clinico-pathological information. Predictions will be made before the start of NAC, approximately halfway during treatment and after completion of NAC. Methods In this multicentre prospective observational study we aim to enrol 100 patients. Multiparametric MRI will be performed prior to NAC, approximately halfway and after completion of NAC. Liquid biopsies will be obtained immediately prior to every cycle of chemotherapy and after completion of NAC. The primary endpoint is RCB in the surgical resection specimen following NAC. Collected data will primarily be analysed using multivariable techniques such as penalised regression techniques. Ethics and dissemination Medical Research Ethics Committee Utrecht has approved this study (NL67308.041.19). Informed consent will be obtained from each participant. All data are anonymised before publication. The findings of this study will be submitted to international peer-reviewed journals. Trial registration number NCT04223492

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

Objectives: Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. Methods: This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. Results: The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). Conclusion: It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories

Deep Learning-Based Segmentation of Locally Advanced Breast Cancer on MRI in Relation to Residual Cancer Burden: A Multiinstitutional Cohort Study

Background: While several methods have been proposed for automated assessment of breast-cancer response to neoadjuvant chemotherapy on breast MRI, limited information is available about their performance across multiple institutions. Purpose: To assess the value and robustness of deep learning-derived volumes of locally advanced breast cancer (LABC) on MRI to infer the presence of residual disease after neoadjuvant chemotherapy. Study Type: Retrospective. Subjects: Training cohort: 102 consecutive female patients with LABC scheduled for neoadjuvant chemotherapy (NAC) from a single institution (age: 25–73 years). Independent testing cohort: 55 consecutive female patients with LABC from four institutions (age: 25–72 years). Field Strength/Sequence: Training cohort: single vendor 1.5 T or 3.0 T. Testing cohort: multivendor 3.0 T. Gradient echo dynamic contrast-enhanced sequences. Assessment: A convolutional neural network (nnU-Net) was trained to segment LABC. Based on resulting tumor volumes, an extremely randomized tree model was trained to assess residual cancer burden (RCB)-0/I vs. RCB-II/III. An independent model was developed using functional tumor volume (FTV). Models were tested on an independent testing cohort and response assessment performance and robustness across multiple institutions were assessed. Statistical Tests: The receiver operating characteristic (ROC) was used to calculate the area under the ROC curve (AUC). DeLong's method was used to compare AUCs. Correlations were calculated using Pearson's method. P values <0.05 were considered significant. Results: Automated segmentation resulted in a median (interquartile range [IQR]) Dice score of 0.87 (0.62–0.93), with similar volumetric measurements (R = 0.95, P < 0.05). Automated volumetric measurements were significantly correlated with FTV (R = 0.80). Tumor volume-derived from deep learning of DCE-MRI was associated with RCB, yielding an AUC of 0.76 to discriminate between RCB-0/I and RCB-II/III, performing similar to the FTV-based model (AUC = 0.77, P = 0.66). Performance was comparable across institutions (IQR AUC: 0.71–0.84). Data Conclusion: Deep learning-based segmentation estimates changes in tumor load on DCE-MRI that are associated with RCB after NAC and is robust against variations between institutions. Evidence Level: 2. Technical Efficacy: Stage 4

Long-term Survival in Breast Cancer Patients Is Associated with Contralateral Parenchymal Enhancement at MRI: Outcomes of the SELECT Study

Background Several single-center studies found that high contralateral parenchymal enhancement (CPE) at breast MRI was associated with improved long-term survival in patients with estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer. Due to varying sample sizes, population characteristics, and follow-up times, consensus of the association is currently lacking. Purpose To confirm whether CPE is associated with long-term survival in a large multicenter retrospective cohort, and to investigate if CPE is associated with endocrine therapy effectiveness. Materials and Methods This multicenter observational cohort included women with unilateral ER-positive HER2-negative breast cancer (tumor size ≤50 mm and ≤three positive lymph nodes) who underwent MRI from January 2005 to December 2010. Overall survival (OS), recurrence-free survival (RFS), and distant RFS (DRFS) were assessed. Kaplan-Meier analysis was performed to investigate differences in absolute risk after 10 years, stratified according to CPE tertile. Multivariable Cox proportional hazards regression analysis was performed to investigate whether CPE was associated with prognosis and endocrine therapy effectiveness. Results Overall, 1432 women (median age, 54 years [IQR, 47-63 years]) were included from 10 centers. Differences in absolute OS after 10 years were stratified according to CPE tertile as follows: 88.5% (95% CI: 88.1, 89.1) in tertile 1, 85.8% (95% CI: 85.2, 86.3) in tertile 2, and 85.9% (95% CI: 85.4, 86.4) in tertile 3. CPE was independently associated with OS, with a hazard ratio (HR) of 1.17 (95% CI: 1.0, 1.36; P = .047), but was not associated with RFS (HR, 1.11; P = .16) or DRFS (HR, 1.11; P = .19). The effect of endocrine therapy on survival could not be accurately assessed; therefore, the association between endocrine therapy efficacy and CPE could not reliably be estimated. Conclusion High contralateral parenchymal enhancement was associated with a marginally decreased overall survival in patients with estrogen receptor-positive and human epidermal growth factor receptor 2-negative breast cancer, but was not associated with recurrence-free survival (RFS) or distant RFS. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Honda and Iima in this issue

Association between parenchymal enhancement of the contralateral breast in dynamic contrast-enhanced MR imaging and outcome of patients with unilateral invasive breast cancer

Purpose: To retrospectively investigate whether parenchymal enhancement in dynamic contrast material-enhanced magnetic resonance (MR) imaging of the contralateral breast in patients with unilateral invasive breast cancer is associated with therapy outcome. Materials and Methods: After obtaining approval of the institutional review board and patients' written informed consent, 531 women with unilateral invasive breast cancer underwent dynamic contrastenhanced MR imaging between 2000 and 2008. The contralateral parenchyma was segmented automatically, in which the mean of the top 10% late enhancement was calculated. Cox regression was used to test associations between parenchymal enhancement, patient and tumor characteristics, and overall survival and invasive disease-free survival. Subset analyses were performed and stratified according to immunohistochemical subtypes and type of adjuvant treatment received. Results: Median follow-up was 86 months. Age (P < .001) and immunohistochemical subtype (P = .042) retained significance in multivariate analysis for overall survival. In patients with estrogen receptor-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (n = 398), age (P < .001), largest diameter on MR images (P = .049), and parenchymal enhancement (P = .011) were significant. In patients who underwent endocrine therapy (n = 174), parenchymal enhancement was the only significant covariate for overall survival and invasive disease-free survival (P < .001). Conclusion: Results suggest that parenchymal enhancement in the contralateral breast of patients with invasive unilateral breast cancer is significantly associated with long-term outcome, particularly in patients with estrogen receptor-positive, human epidermal growth factor receptor 2-negative breast cancer. Lower value of the mean top 10% enhancement of the parenchyma shows potential as a predictive biomarker for relatively poor outcome in patients who undergo endocrine therapy. These results should, however, be validated in a larger study

Prediction of Poor Outcome after Tisagenlecleucel in Patients with Relapsed or Refractory Diffuse Large B Cell Lymphoma (DLBCL) Using Artificial Intelligence Analysis of Pre-Infusion PET/CT

Introduction In the pivotal JULIET trial patients with relapsed or refractory Diffuse Large B cell Lymphoma (DLBC) received a single intravenous infusion of Tisagenlecleucel. In long-term follow-up analysis, an overall response rate (ORR) of 53% and a complete response (CR) of 39% were reported. In 115 evaluable patients, 62% had disease progression or died. At 40.3 months follow up, the median progression-free survival (PFS) was 2.9 months and the median overall survival (OS) was 11.1 months. The median PFS and OS of 35% of the patients with complete response at 3 months, 6 months, or both, were not reached suggesting durable response [1].Although high metabolic tumor volume (MTV) measured by [18F] FDG PET/CT during CART cell therapy was found to be predictive of early relapse [2], pretreatment available factors - such as high IPI, elevated LDH, low platelets, and MTV - do not fully elucidate which individuals have poor outcome after therapy [1-3]. Accurate prediction of poor outcome in individuals at the treatment-decision stage may lead to more effective patient selection, preventing unnecessary cost and adverse treatment effects.The aim of this study is to demonstrate feasibility of identifying a subgroup of patients at very high risk of poor outcome (death or disease progression) prior to infusion of Tisagenlecleucel, using Artificial Intelligence (AI) to characterize pre-infusion FDG PET/CT in combination with clinicopathological parameters.Material and Methods In this secondary analysis of data from the prospective JULIET trial [1,4], 115 FDG PET/CT data sets were included from 115 patients with R-R DLBCL from 27 treatment sites between 2015 and 2018. All patients received a single intravenous infusion of Tisagenlecleucel . The pre-infusion FDG PET/CT images were processed automatically using deep learning. Clinicopathological parameters were added: patient age, IPI, LDH, platelet count, MTV, and LDH.A novel automated test ("AI signature") was developed to identify a subgroup of patients at very high risk of poor outcome (death or disease progression). In short, an attention-gated convolutional neural network (AG-CNN) was trained to delineate the PET/CT disease foci automatically and consistently across the different treatment centers. MTV was automatically derived, and disease foci were further characterized by their activations in the most densely compressed layer in latent space of the AG-CNN. After additional dimensionality reduction, the AI signature was derived using multivariate Cox regression, random survival forests, Receiver Operating Characteristics (ROC) analysis, and Kaplan Meier modelling. The AI signature was validated using nested 5-fold cross validation: data were partitioned five times into training and testing folds. Models derived from the training folds were tested on the testing folds. Median testing performance and interquartile range (IQR) were reported.Results The median patient age was 56 years (IQR 46-64). The median follow-up-time was 80 days (IQR 29-554). After 5-fold cross validation, 52.4% of the patients (IQR 39.1-56.5) had a negative AI-signature of whom 100% (IQR 92.9-100) had poor outcome. 47.6% Of the patients (IQR 39.1-56.5) had a positive AI-signature of whom 55.6% (range 53.3-61.5) had poor outcome. Median PFS in long-term follow up was 13.8% (range 11.5-14.6) and 29.8% (range 29.6-33.2) in patients with negative and positive AI-signature, respectively. The median cross-validated area under the ROC curve after multivariate Cox regression was 0.74 (IQR 0.70 - 0.81). The HR was 0.36 (95% CI 0.13 - 0.95; P = . 0.0432) after multivariable correction for age, IPI, LDH, platelet count, MTV, and LDH.Conclusions Results from the JULIET trial data indicate that an automated test based on AI analysis of pre-infusion FDG PET/CT and clinicopathological parameters is feasible to identify a subgroup of patients at very high risk of poor outcome after Tisagenlecleucel. The test retained significance after multivariable correction for other parameters known to be associated with CART response, IPI, LDH, age, platelet count, MTV, and LDH. Follow-up studies will focus on validating these findings in independent patient cohorts

Deep learning for multi-task medical image segmentation in multiple modalities

Automatic segmentation of medical images is an important task for many clinical applications. In practice,a wide range of anatomical structures are visualised using different imaging modalities. In this paper,we investigate whether a single convolutional neural network (CNN) can be trained to perform different segmentation tasks. A single CNN is trained to segment six tissues in MR brain images,the pectoral muscle in MR breast images,and the coronary arteries in cardiac CTA. The CNN therefore learns to identify the imaging modality,the visualised anatomical structures,and the tissue classes. For each of the three tasks (brain MRI,breast MRI and cardiac CTA),this combined training procedure resulted in a segmentation performance equivalent to that of a CNN trained specifically for that task,demonstrating the high capacity of CNN architectures. Hence,a single system could be used in clinical practice to automatically perform diverse segmentation tasks without task-specific training

Semi-automated primary tumor volume measurements by dynamic contrast-enhanced MRI in patients with head and neck cancer

Background: Tumor volume is a significant prognostic factor in the treatment of malignant head and neck tumors. Unfortunately, it is not routinely measured because of the workload involved. Methods: Twenty-one patients, between 2009 and 2010, were studied. Dynamic contrast-enhanced MRI (DCE-MRI) at 3.0T was performed. A workstation previously developed for semi-automated segmentation of breast cancers on DCE-MRI was used to segment the head and neck cancers. The Pearson correlation analysis was used to assess the agreement between volumetric measurements and the manually derived gross tumor volume (GTV). Results: In 90.5% of the patients (19 of 21) correlation could be made between DCE-MRI and the manually derived GTV. The Pearson correlation coefficient between the automatically derived tumor volume at DCE-MRI and the manually derived GTVs was R2 = 0.95 (p <.001). Conclusion: Semi-automated tumor volumes on DCE-MRI were representative of those derived from the manually derived GTV (R2 = 0.95; p <.001)