Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer.

PURPOSE: Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making. METHODS: We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers. RESULTS: Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes. CONCLUSIONS: Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

Background parenchymal enhancement on breast MRI: A comprehensive review.

The degree of normal fibroglandular tissue that enhances on breast MRI, known as background parenchymal enhancement (BPE), was initially described as an incidental finding that could affect interpretation performance. While BPE is now established to be a physiologic phenomenon that is affected by both endogenous and exogenous hormone levels, evidence supporting the notion that BPE frequently masks breast cancers is limited. However, compelling data have emerged to suggest BPE is an independent marker of breast cancer risk and breast cancer treatment outcomes. Specifically, multiple studies have shown that elevated BPE levels, measured qualitatively or quantitatively, are associated with a greater risk of developing breast cancer. Evidence also suggests that BPE could be a predictor of neoadjuvant breast cancer treatment response and overall breast cancer treatment outcomes. These discoveries come at a time when breast cancer screening and treatment have moved toward an increased emphasis on targeted and individualized approaches, of which the identification of imaging features that can predict cancer diagnosis and treatment response is an increasingly recognized component. Historically, researchers have primarily studied quantitative tumor imaging features in pursuit of clinically useful biomarkers. However, the need to segment less well-defined areas of normal tissue for quantitative BPE measurements presents its own unique challenges. Furthermore, there is no consensus on the optimal timing on dynamic contrast-enhanced MRI for BPE quantitation. This article comprehensively reviews BPE with a particular focus on its potential to increase precision approaches to breast cancer risk assessment, diagnosis, and treatment. It also describes areas of needed future research, such as the applicability of BPE to women at average risk, the biological underpinnings of BPE, and the standardization of BPE characterization. Level of Evidence: 3 Technical Efficacy Stage: 5 J. Magn. Reson. Imaging 2020;51:43-61.

Role of texture analysis in breast MRI as a cancer biomarker: A review.

Breast cancer is a known heterogeneous disease. Current clinically utilized histopathologic biomarkers may undersample tumor heterogeneity, resulting in higher rates of misdiagnosis for breast cancer. MRI can provide a whole-tumor sampling of disease burden and is widely utilized in clinical care. Texture analysis can provide a localized description of breast cancer, with particular emphasis on quantifying breast lesion heterogeneity. The object of this review is to provide an overview of texture analysis applications towards breast cancer diagnosis, prognosis, and treatment response evaluation and review the role of image-based texture features as noninvasive prognostic and predictive biomarkers. Level of Evidence: 5 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:927-938.

A quantitative microscopic approach to predict local recurrence based on in vivo intraoperative imaging of sarcoma tumor margins.

The goal of resection of soft tissue sarcomas located in the extremity is to preserve limb function while completely excising the tumor with a margin of normal tissue. With surgery alone, one-third of patients with soft tissue sarcoma of the extremity will have local recurrence due to microscopic residual disease in the tumor bed. Currently, a limited number of intraoperative pathology-based techniques are used to assess margin status; however, few have been widely adopted due to sampling error and time constraints. To aid in intraoperative diagnosis, we developed a quantitative optical microscopy toolbox, which includes acriflavine staining, fluorescence microscopy, and analytic techniques called sparse component analysis and circle transform to yield quantitative diagnosis of tumor margins. A series of variables were quantified from images of resected primary sarcomas and used to optimize a multivariate model. The sensitivity and specificity for differentiating positive from negative ex vivo resected tumor margins was 82 and 75%. The utility of this approach was tested by imaging the in vivo tumor cavities from 34 mice after resection of a sarcoma with local recurrence as a bench mark. When applied prospectively to images from the tumor cavity, the sensitivity and specificity for differentiating local recurrence was 78 and 82%. For comparison, if pathology was used to predict local recurrence in this data set, it would achieve a sensitivity of 29% and a specificity of 71%. These results indicate a robust approach for detecting microscopic residual disease, which is an effective predictor of local recurrence.

Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy.

BACKGROUND: Early changes in breast intratumor heterogeneity during neoadjuvant chemotherapy may reflect the tumor's ability to adapt and evade treatment. We investigated the combination of precision medicine predictors of genomic and MRI data towards improved prediction of recurrence free survival (RFS). METHODS: A total of 100 women from the ACRIN 6657/I-SPY 1 trial were retrospectively analyzed. We estimated MammaPrint, PAM50 ROR-S, and p53 mutation scores from publicly available gene expression data and generated four, voxel-wise 3-D radiomic kinetic maps from DCE-MR images at both pre- and early-treatment time points. Within the primary lesion from each kinetic map, features of change in radiomic heterogeneity were summarized into 6 principal components. RESULTS: We identify two imaging phenotypes of change in intratumor heterogeneity (p < 0.01) demonstrating significant Kaplan-Meier curve separation (p < 0.001). Adding phenotypes to established prognostic factors, functional tumor volume (FTV), MammaPrint, PAM50, and p53 scores in a Cox regression model improves the concordance statistic for predicting RFS from 0.73 to 0.79 (p = 0.002). CONCLUSIONS: These results demonstrate an important step in combining personalized molecular signatures and longitudinal imaging data towards improved prognosis.

Early changes in tumor properties during treatment may tell us whether or not a patient's tumor is responding to treatment. Such changes may be seen on imaging. Here, changes in breast cancer properties are identified on imaging and are used in combination with gene markers to investigate whether response to treatment can be predicted using mathematical models. We demonstrate that tumor properties seen on imaging early on in treatment can help to predict patient outcomes. Our approach may allow clinicians to better inform patients about their prognosis and choose appropriate and effective therapies.

Resampling and harmonization for mitigation of heterogeneity in image parameters of baseline scans.

Our study investigates the effects of heterogeneity in image parameters on the reproducibility of prognostic performance of models built using radiomic biomarkers. We compare the prognostic performance of models derived from the heterogeneity-mitigated features with that of models obtained from raw features, to assess whether reproducibility of prognostic scores improves upon application of our methods. We used two datasets: The Breast I-SPY1 dataset-Baseline DCE-MRI scans of 156 women with locally advanced breast cancer, treated with neoadjuvant chemotherapy, publicly available via The Cancer Imaging Archive (TCIA); The NSCLC IO dataset-Baseline CT scans of 107 patients with stage 4 non-small cell lung cancer (NSCLC), treated with pembrolizumab immunotherapy at our institution. Radiomic features (n = 102) are extracted from the tumor ROIs. We use a variety of resampling and harmonization scenarios to mitigate the heterogeneity in image parameters. The patients were divided into groups based on batch variables. For each group, the radiomic phenotypes are combined with the clinical covariates into a prognostic model. The performance of the groups is assessed using the c-statistic, derived from a Cox proportional hazards model fitted on all patients within a group. The heterogeneity-mitigation scenario (radiomic features, derived from images that have been resampled to minimum voxel spacing, are harmonized using the image acquisition parameters as batch variables) gave models with highest prognostic scores (for e.g., IO dataset; batch variable: high kernel resolution-c-score: 0.66). The prognostic performance of patient groups is not comparable in case of models built using non-heterogeneity mitigated features (for e.g., I-SPY1 dataset; batch variable: small pixel spacing-c-score: 0.54, large pixel spacing-c-score: 0.65). The prognostic performance of patient groups is closer in case of heterogeneity-mitigated scenarios (for e.g., scenario-harmonize by voxel spacing parameters: IO dataset; thin slice-c-score: 0.62, thick slice-c-score: 0.60). Our results indicate that accounting for heterogeneity in image parameters is important to obtain more reproducible prognostic scores, irrespective of image site or modality. For non-heterogeneity mitigated models, the prognostic scores are not comparable across patient groups divided based on batch variables. This study can be a step in the direction of constructing reproducible radiomic biomarkers, thus increasing their application in clinical decision making.

Expert tumor annotations and radiomics for locally advanced breast cancer in DCE-MRI for ACRIN 6657/I-SPY1.

Breast cancer is one of the most pervasive forms of cancer and its inherent intra- and inter-tumor heterogeneity contributes towards its poor prognosis. Multiple studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of having consistency in: a) data quality, b) quality of expert annotation of pathology, and c) availability of baseline results from computational algorithms. To address these limitations, here we propose the enhancement of the I-SPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. Specifically, the proposed dataset includes a) uniformly processed scans that are harmonized to match intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Radiogenomics in brain, breast, and lung cancer: opportunities and challenges.

The field of radiogenomics largely focuses on developing imaging surrogates for genomic signatures and integrating imaging, genomic, and molecular data to develop combined personalized biomarkers for characterizing various diseases. Our study aims to highlight the current state-of-the-art and the role of radiogenomics in cancer research, focusing mainly on solid tumors, and is broadly divided into four sections. The first section reviews representative studies that establish the biologic basis of radiomic signatures using gene expression and molecular profiling information. The second section includes studies that aim to non-invasively predict molecular subtypes of tumors using radiomic signatures. The third section reviews studies that evaluate the potential to augment the performance of established prognostic signatures by combining complementary information encoded by radiomic and genomic signatures derived from cancer tumors. The fourth section includes studies that focus on ascertaining the biological significance of radiomic phenotypes. We conclude by discussing current challenges and opportunities in the field, such as the importance of coordination between imaging device manufacturers, regulatory organizations, health care providers, pharmaceutical companies, academic institutions, and physicians for the effective standardization of the results from radiogenomic signatures and for the potential use of these findings to improve precision care for cancer patients.

Analysis of Four-Dimensional Data for Total Body PET Imaging.

The high sensitivity and total-body coverage of total-body PET scanners will be valuable for a number of clinical and research applications outlined in this article.

Interactive Machine Learning-Based Multi-Label Segmentation of Solid Tumors and Organs.

We seek the development and evaluation of a fast, accurate, and consistent method for general-purpose segmentation, based on interactive machine learning (IML). To validate our method, we identified retrospective cohorts of 20 brain, 50 breast, and 50 lung cancer patients, as well as 20 spleen scans, with corresponding ground truth annotations. Utilizing very brief user training annotations and the adaptive geodesic distance transform, an ensemble of SVMs is trained, providing a patient-specific model applied to the whole image. Two experts segmented each cohort twice with our method and twice manually. The IML method was faster than manual annotation by 53.1% on average. We found significant (p < 0.001) overlap difference for spleen (DiceIML/DiceManual = 0.91/0.87), breast tumors (DiceIML/DiceManual = 0.84/0.82), and lung nodules (DiceIML/DiceManual = 0.78/0.83). For intra-rater consistency, a significant (p = 0.003) difference was found for spleen (DiceIML/DiceManual = 0.91/0.89). For inter-rater consistency, significant (p < 0.045) differences were found for spleen (DiceIML/DiceManual = 0.91/0.87), breast (DiceIML/DiceManual = 0.86/0.81), lung (DiceIML/DiceManual = 0.85/0.89), the non-enhancing (DiceIML/DiceManual = 0.79/0.67) and the enhancing (DiceIML/DiceManual = 0.79/0.84) brain tumor sub-regions, which, in aggregation, favored our method. Quantitative evaluation for speed, spatial overlap, and consistency, reveals the benefits of our proposed method when compared with manual annotation, for several clinically relevant problems. We publicly release our implementation through CaPTk (Cancer Imaging Phenomics Toolkit) and as an MITK plugin.

Imaging Phenotypes of Breast Cancer Heterogeneity in Preoperative Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) Scans Predict 10-Year Recurrence.

PURPOSE: Identifying imaging phenotypes and understanding their relationship with prognostic markers and patient outcomes can allow for a noninvasive assessment of cancer. The purpose of this study was to identify and validate intrinsic imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans and evaluate their prognostic performance in predicting 10 years recurrence. EXPERIMENTAL DESIGN: Pretreatment DCE-MRI scans of 95 women with primary invasive breast cancer with at least 10 years of follow-up from a clinical trial at our institution (2002-2006) were retrospectively analyzed. For each woman, a signal enhancement ratio (SER) map was generated for the entire segmented primary lesion volume from which 60 radiomic features of texture and morphology were extracted. Intrinsic phenotypes of tumor heterogeneity were identified via unsupervised hierarchical clustering of the extracted features. An independent sample of 163 women diagnosed with primary invasive breast cancer (2002-2006), publicly available via The Cancer Imaging Archive, was used to validate phenotype reproducibility. RESULTS: Three significant phenotypes of low, medium, and high heterogeneity were identified in the discovery cohort and reproduced in the validation cohort (P < 0.01). Kaplan-Meier curves showed statistically significant differences (P < 0.05) in recurrence-free survival (RFS) across phenotypes. Radiomic phenotypes demonstrated added prognostic value (c = 0.73) predicting RFS. CONCLUSIONS: Intrinsic imaging phenotypes of breast cancer tumor heterogeneity at primary diagnosis can predict 10-year recurrence. The independent and additional prognostic value of imaging heterogeneity phenotypes suggests that radiomic phenotypes can provide a noninvasive characterization of tumor heterogeneity to augment personalized prognosis and treatment.

Precision diagnostics based on machine learning-derived imaging signatures.

The complexity of modern multi-parametric MRI has increasingly challenged conventional interpretations of such images. Machine learning has emerged as a powerful approach to integrating diverse and complex imaging data into signatures of diagnostic and predictive value. It has also allowed us to progress from group comparisons to imaging biomarkers that offer value on an individual basis. We review several directions of research around this topic, emphasizing the use of machine learning in personalized predictions of clinical outcome, in breaking down broad umbrella diagnostic categories into more detailed and precise subtypes, and in non-invasively estimating cancer molecular characteristics. These methods and studies contribute to the field of precision medicine, by introducing more specific diagnostic and predictive biomarkers of clinical outcome, therefore pointing to better matching of treatments to patients.

An integrated strategy for improving contrast, durability, and portability of a Pocket Colposcope for cervical cancer screening and diagnosis.

INTRODUCTION: We have previously developed a portable Pocket Colposcope for cervical cancer screening in resource-limited settings. In this manuscript we report two different strategies (cross-polarization and an integrated reflector) to improve image contrast levels achieved with the Pocket Colposcope and evaluate the merits of each strategy compared to a standard-of-care digital colposcope. The desired outcomes included reduced specular reflection (glare), increased illumination beam pattern uniformity, and reduced electrical power budget. In addition, anti-fogging and waterproofing features were incorporated to prevent the Pocket Colposcope from fogging in the vaginal canal and to enable rapid disinfection by submersion in chemical agents. METHODS: Cross-polarization (Generation 3 Pocket Colposcope) and a new reflector design (Generation 4 Pocket Colposcope) were used to reduce glare and improve contrast. The reflector design (including the angle and height of the reflector sidewalls) was optimized through ray-tracing simulations. Both systems were characterized with a series of bench tests to assess specular reflection, beam pattern uniformity, and image contrast. A pilot clinical study was conducted to compare the Generation 3 and 4 Pocket Colposcopes to a standard-of-care colposcope (Leisegang Optik 2). Specifically, paired images of cervices were collected from the standard-of-care colposcope and either the Generation 3 (n = 24 patients) or the Generation 4 (n = 32 patients) Pocket Colposcopes. The paired images were blinded by device, randomized, and sent to an expert physician who provided a diagnosis for each image. Corresponding pathology was obtained for all image pairs. The primary outcome measures were the level of agreement (%) and κ (kappa) statistic between the standard-of-care colposcope and each Pocket Colposcope (Generation 3 and Generation 4). RESULTS: Both generations of Pocket Colposcope had significantly higher image contrast when compared to the standard-of-care colposcope. The addition of anti-fog and waterproofing features to the Generation 3 and 4 Pocket Colposcope did not impact image quality based on qualitative and quantitative metrics. The level of agreement between the Generation 3 Pocket Colposcope and the standard-of-care colposcope was 75.0% (kappa = 0.4000, p = 0.0028, n = 24). This closely matched the level of agreement between the Generation 4 Pocket Colposcope and the standard-of-care colposcope which was also 75.0% (kappa = 0.4941, p = 0.0024, n = 32). CONCLUSION: Our results indicate that the Generation 3 and 4 Pocket Colposcopes perform comparably to the standard-of-care colposcope, with the added benefit of being low-cost and waterproof, which is ideal for use in resource-limited settings. Additionally, the reflector significantly reduces the electrical requirements of the Generation 4 Pocket Colposcope enhancing portability without altering performance compared to the Generation 3 system.

Rapid staining and imaging of subnuclear features to differentiate between malignant and benign breast tissues at a point-of-care setting.

PURPOSE: Histopathology is the clinical standard for tissue diagnosis; however, it requires tissue processing, laboratory personnel and infrastructure, and a highly trained pathologist to diagnose the tissue. Optical microscopy can provide real-time diagnosis, which could be used to inform the management of breast cancer. The goal of this work is to obtain images of tissue morphology through fluorescence microscopy and vital fluorescent stains and to develop a strategy to segment and quantify breast tissue features in order to enable automated tissue diagnosis. METHODS: We combined acriflavine staining, fluorescence microscopy, and a technique called sparse component analysis to segment nuclei and nucleoli, which are collectively referred to as acriflavine positive features (APFs). A series of variables, which included the density, area fraction, diameter, and spacing of APFs, were quantified from images taken from clinical core needle breast biopsies and used to create a multivariate classification model. The model was developed using a training data set and validated using an independent testing data set. RESULTS: The top performing classification model included the density and area fraction of smaller APFs (those less than 7 µm in diameter, which likely correspond to stained nucleoli).When applied to the independent testing set composed of 25 biopsy panels, the model achieved a sensitivity of 82 %, a specificity of 79 %, and an overall accuracy of 80 %. CONCLUSIONS: These results indicate that our quantitative microscopy toolbox is a potentially viable approach for detecting the presence of malignancy in clinical core needle breast biopsies.

Algorithms for differentiating between images of heterogeneous tissue across fluorescence microscopes.

Fluorescence microscopy can be used to acquire real-time images of tissue morphology and with appropriate algorithms can rapidly quantify features associated with disease. The objective of this study was to assess the ability of various segmentation algorithms to isolate fluorescent positive features (FPFs) in heterogeneous images and identify an approach that can be used across multiple fluorescence microscopes with minimal tuning between systems. Specifically, we show a variety of image segmentation algorithms applied to images of stained tumor and muscle tissue acquired with 3 different fluorescence microscopes. Results indicate that a technique called maximally stable extremal regions followed by thresholding (MSER + Binary) yielded the greatest contrast in FPF density between tumor and muscle images across multiple microscopy systems.

NF1 deletion generates multiple subtypes of soft-tissue sarcoma that respond to MEK inhibition.

Soft-tissue sarcomas are a heterogeneous group of tumors arising from connective tissue. Recently, mutations in the neurofibromin 1 (NF1) tumor suppressor gene were identified in multiple subtypes of human soft-tissue sarcomas. To study the effect of NF1 inactivation in the initiation and progression of distinct sarcoma subtypes, we have developed a novel mouse model of temporally and spatially restricted NF1-deleted sarcoma. To generate primary sarcomas, we inject adenovirus containing Cre recombinase into NF1(flox/flox); Ink4a/Arf(flox/flox) mice at two distinct orthotopic sites: intramuscularly or in the sciatic nerve. The mice develop either high-grade myogenic sarcomas or malignant peripheral nerve sheath tumor (MPNST)-like tumors, respectively. These tumors reflect the histologic properties and spectrum of sarcomas found in patients. To explore the use of this model for preclinical studies, we conducted a study of mitogen-activated protein kinase (MAPK) pathway inhibition with the MEK inhibitor PD325901. Treatment with PD325901 delays tumor growth through decreased cyclin D1 mRNA and cell proliferation. We also examined the effects of MEK inhibition on the native tumor stroma and find that PD325901 decreases VEGFα expression in tumor cells with a corresponding decrease in microvessel density. Taken together, our results use a primary tumor model to show that sarcomas can be generated by loss of NF1 and Ink4a/Arf, and that these tumors are sensitive to MEK inhibition by direct effects on tumor cells and the surrounding microenvironment. These studies suggest that MEK inhibitors should be further explored as potential sarcoma therapies in patients with tumors containing NF1 deletion.