DCE-MRI biomarkers of tumour heterogeneity predict CRC liver metastasis shrinkage following bevacizumab and FOLFOX-6

Greybox: A hybrid algorithm for direct estimation of tracer kinetic parameters from undersampled DCE-MRI data

BACKGROUND

A variety of deep learning-based and iterative approaches are available to predict Tracer Kinetic (TK) parameters from fully sampled or undersampled dynamic contrast-enhanced (DCE) MRI data. However, both the methods offer distinct benefits and drawbacks.

PURPOSE

To propose a hybrid algorithm (named as 'Greybox'), using both model- as well as DL-based, for solving a multi-parametric non-linear inverse problem of directly estimating TK parameters from undersampled DCE MRI data, which is invariant to undersampling rate.

METHODS

The proposed algorithm was inspired by plug-and-play algorithms used for solving linear inverse imaging problems. This technique was tested for its effectiveness in solving the nonlinear ill-posed inverse problem of generating 3D TK parameter maps from four-dimensional (4D; Spatial + Temporal) retrospectively undersampled k-space data. The algorithm learns a deep learning-based prior using UNET to estimate theK trans $\mathbf {K_{trans}}$ andV p $\mathbf {V_{p}}$ parameters based on the Patlak pharmacokinetic model, and this trained prior was utilized to estimate the TK parameter maps using an iterative gradient-based optimization scheme. Unlike the existing DL models, this network is invariant to the undersampling rate of the input data. The proposed method was compared with the total variation-based direct reconstruction technique on brain, breast, and prostate DCE-MRI datasets for various undersampling rates using the Radial Golden Angle (RGA) scheme. For the breast dataset, an indirect estimation using the Fast Composite Splitting algorithm was utilized for comparison. Undersampling rates of 8 × $\times$ , 12 × $\times$ and 20 × $\times$ were used for the experiments, and the results were compared using the PSNR and SSIM as metrics. For the breast dataset of 10 patients, data from four patients were utilized for training (1032 samples), two for validation (752 samples), and the entire volume of four patients for testing. Similarly, for the prostate dataset of 18 patients, 10 patients were utilized for training (720 samples), five for validation (216 samples), and the whole volume of three patients for testing. For the brain dataset of nineteen patients, ten patients were used for training (3152 samples), five for validation (1168 samples), and the whole volume of four patients for testing. Statistical tests were also conducted to assess the significance of the improvement in performance.

RESULTS

The experiments showed that the proposed Greybox performs significantly better than other direct reconstruction methods. The proposed algorithm improved the estimatedK trans $\mathbf {K_{trans}}$ andV p $\mathbf {V_{p}}$ in terms of the peak signal-to-noise ratio by up to 3 dB compared to other standard reconstruction methods.

CONCLUSION

The proposed hybrid reconstruction algorithm, Greybox, can provide state-of-the-art performance in solving the nonlinear inverse problem of DCE-MRI. This is also the first of its kind to utilize convolutional neural network-based encodings as part of the plug-and-play priors to improve the performance of the reconstruction algorithm.

Multiparametric MRI for characterization of the tumour microenvironment

Our understanding of tumour biology has evolved over the past decades and cancer is now viewed as a complex ecosystem with interactions between various cellular and non-cellular components within the tumour microenvironment (TME) at multiple scales. However, morphological imaging remains the mainstay of tumour staging and assessment of response to therapy, and the characterization of the TME with non-invasive imaging has not yet entered routine clinical practice. By combining multiple MRI sequences, each providing different but complementary information about the TME, multiparametric MRI (mpMRI) enables non-invasive assessment of molecular and cellular features within the TME, including their spatial and temporal heterogeneity. With an increasing number of advanced MRI techniques bridging the gap between preclinical and clinical applications, mpMRI could ultimately guide the selection of treatment approaches, precisely tailored to each individual patient, tumour and therapeutic modality. In this Review, we describe the evolving role of mpMRI in the non-invasive characterization of the TME, outline its applications for cancer detection, staging and assessment of response to therapy, and discuss considerations and challenges for its use in future medical applications, including personalized integrated diagnostics.

Colorectal cancer cell membrane biomimetic ferroferric oxide nanomaterials for homologous bio-imaging and chemotherapy application

The research of nanomaterials for bio-imaging and theranostic are very active nowadays with unprecedented advantages in nanomedicine. Homologous targeting and bio-imaging greatly improve the ability of targeted drug delivery and enhance active targeting and treatment ability of nanomedicine for the tumor. In this work, lycorine hydrochloride (LH) and magnetic iron oxide nanoparticles coated with a colorectal cancer (CRC) cell membrane (LH-Fe3O4@M) were prepared, for homologous targeting, magnetic resonance imaging (MRI), and chemotherapy. Results showed that the LH-Fe3O4@M and Fe3O4@M intensity at HT29 tumor was significantly higher than that Fe3O4@PEG, proving the superior selectivity of cancer cell membrane-camouflaged nanomedicine for homologous tumors and the MRI effect of darkening contrast enhancement were remarkable at HT29 tumor. The LH-Fe3O4@M exhibited excellent chemotherapy effect in CRC models as well as LH alone and achieved a high tumor ablation rate but no damage to normal tissues and cells. Therefore, our biomimetic system achieved a homologous targeting, bio-imaging, and efficient therapeutic effect of CRC.

A hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme for breast cancer segmentation based on DCE-MRI

Automatically and accurately annotating tumor in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), which provides a noninvasive in vivo method to evaluate tumor vasculature architectures based on contrast accumulation and washout, is a crucial step in computer-aided breast cancer diagnosis and treatment. However, it remains challenging due to the varying sizes, shapes, appearances and densities of tumors caused by the high heterogeneity of breast cancer, and the high dimensionality and ill-posed artifacts of DCE-MRI. In this paper, we propose a hybrid hemodynamic knowledge-powered and feature reconstruction-guided scheme that integrates pharmacokinetics prior and feature refinement to generate sufficiently adequate features in DCE-MRI for breast cancer segmentation. The pharmacokinetics prior expressed by time intensity curve (TIC) is incorporated into the scheme through objective function called dynamic contrast-enhanced prior (DCP) loss. It contains contrast agent kinetic heterogeneity prior knowledge, which is important to optimize our model parameters. Besides, we design a spatial fusion module (SFM) embedded in the scheme to exploit intra-slices spatial structural correlations, and deploy a spatial-kinetic fusion module (SKFM) to effectively leverage the complementary information extracted from spatial-kinetic space. Furthermore, considering that low spatial resolution often leads to poor image quality in DCE-MRI, we integrate a reconstruction autoencoder into the scheme to refine feature maps in an unsupervised manner. We conduct extensive experiments to validate the proposed method and show that our approach can outperform recent state-of-the-art segmentation methods on breast cancer DCE-MRI dataset. Moreover, to explore the generalization for other segmentation tasks on dynamic imaging, we also extend the proposed method to brain segmentation in DSC-MRI sequence. Our source code will be released on https://github.com/AI-medical-diagnosis-team-of-JNU/DCEDuDoFNet.

Quantification of tumor heterogeneity: from data acquisition to metric generation

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care.

A model selection framework to quantify microvascular liver function in gadoxetate-enhanced MRI: Application to healthy liver, diseased tissue, and hepatocellular carcinoma

PURPOSE

We introduce a novel, generalized tracer kinetic model selection framework to quantify microvascular characteristics of liver and tumor tissue in gadoxetate-enhanced dynamic contrast-enhanced MRI (DCE-MRI).

METHODS

Our framework includes a hierarchy of nested models, from which physiological parameters are derived in 2 regimes, corresponding to the active transport and free diffusion of gadoxetate. We use simulations to show the sensitivity of model selection and parameter estimation to temporal resolution, time-series duration, and noise. We apply the framework in 8 healthy volunteers (time-series duration up to 24 minutes) and 10 patients with hepatocellular carcinoma (6 minutes).

RESULTS

The active transport regime is preferred in 98.6% of voxels in volunteers, 82.1% of patients' non-tumorous liver, and 32.2% of tumor voxels. Interpatient variations correspond to known co-morbidities. Simulations suggest both datasets have sufficient temporal resolution and signal-to-noise ratio, while patient data would be improved by using a time-series duration of at least 12 minutes.

CONCLUSIONS

In patient data, gadoxetate exhibits different kinetics: (a) between liver and tumor regions and (b) within regions due to liver disease and/or tumor heterogeneity. Our generalized framework selects a physiological interpretation at each voxel, without preselecting a model for each region or duplicating time-consuming optimizations for models with identical functional forms.

Texture Analysis and its Applications in Biomedical Imaging: A Survey

Texture analysis describes a variety of image analysis techniques that quantify the variation in intensity and pattern. This paper provides an overview of several texture analysis approaches addressing the rationale supporting them, their advantages, drawbacks, and applications. This surveys emphasis is in collecting and categorising over five decades of active research on texture analysis. Brief descriptions of different approaches are presented along with application examples. From a broad range of texture analysis applications, this surveys final focus is on biomedical image analysis. An up-to-date list of biological tissues and organs in which disorders produce texture changes that may be used to spot disease onset and progression is provided. Finally, the role of texture analysis methods as biomarkers of disease is summarised.

Rim enhancement on hepatobiliary phase of pre-treatment 3.0 T MRI: A potential marker for early chemotherapy response in colorectal liver metastases treated with XELOX

PURPOSE

To assess the value of the enhanced features on the hepatobiliary phase (HBP) of pre-treatment Gd-EOB-DTPA MRI in evaluating response to chemotherapy in colorectal liver metastases (CRLMs).

METHODS

We retrospectively studied 65 patients with CRLMs who underwent Gd-EOB-DTPA enhanced MRI before chemotherapy from October 2015 to November 2017. The diagnosis of liver metastasis was established on the basis of imaging findings. Two radiologists evaluated the size, contrast-enhanced (CE) patterns of the maximum lesion on the HBP. According to the different CE patterns, we quantified area signal intensity (SI) by applying SI ratio (such as SI_center/outer and SI_rim/center). All of the above parameters were analyzed in terms of chemotherapy response.

RESULTS

Rim enhancement on the HBP was more frequent in the responding group of 28 patients (72%) than in the non-responding group of eight patients (31%). Additionally, there was a significant association between chemotherapy response and quantitative parameters: including diameter (P = 0.04), SI_center/outer (P = 0.047) and SI_rim/center (P = 0.012). The HBP CE pattern (P = 0.007) and SI_center/outer (P = 0.022) were independent factors for chemotherapy response. The areas under the curve (AUCs) of the above-mentioned parameters were significant associated with response to chemotherapy, in which diameter, HBP CE patterns, SI_center/outer, and SI_rim/center were 0.638, 0.706, 0.712, and 0.673, respectively. Moreover, the combination of these parameters obtained the largest AUC of 0.821.

CONCLUSION

The CE patterns, in particular with rim enhancement, and SI ratio parameters on the HBP are useful indicators for early evaluation of therapeutic response after chemotherapy in patients with CRLMs.

Advances in Imaging Modalities and Contrast Agents for the Early Diagnosis of Colorectal Cancer

Colorectal cancer is one of the most common gastrointestinal cancers worldwide. The mortality rate of colorectal cancer has declined by more than 20% due to the rapid development of early diagnostic techniques and effective treatment. At present, there are many diagnostic modalities available for the evaluation of colorectal cancer, such as the carcinoembryonic antigen test, the fecal occult blood test, endoscopy, X-ray barium meal, computed tomography, magnetic resonance imaging, and radionuclide examination. Sensitive and specific imaging modalities have played an increasingly important role in the diagnosis of colorectal cancer following the rapid development of novel contrast agents. This review discusses the applications and challenges of different imaging techniques and contrast agents applied to detect colorectal cancer, for the purpose of the early diagnosis and treatment of patients with colorectal cancer.

The Biological Meaning of Radiomic Features

An earlier incorrect version appeared online. This article was corrected on February 10, 2021.

Quantitative tumor heterogeneity MRI profiling improves machine learning-based prognostication in patients with metastatic colon cancer

OBJECTIVES

Intra-tumor heterogeneity has been previously shown to be an independent predictor of patient survival. The goal of this study is to assess the role of quantitative MRI-based measures of intra-tumor heterogeneity as predictors of survival in patients with metastatic colorectal cancer.

METHODS

In this IRB-approved retrospective study, we identified 55 patients with stage 4 colon cancer with known hepatic metastasis on MRI. Ninety-four metastatic hepatic lesions were identified on post-contrast images and manually volumetrically segmented. A heterogeneity phenotype vector was extracted from each lesion. Univariate regression analysis was used to assess the contribution of 110 extracted features to survival prediction. A random forest-based machine learning technique was applied to the feature vector and to the standard prognostic clinical and pathologic variables. The dataset was divided into a training and test set at a ratio of 4:1. ROC analysis and confusion matrix analysis were used to assess classification performance.

RESULTS

Mean survival time was 39 ± 3.9 months for the study population. A total of 22 texture features were associated with patient survival (p < 0.05). The trained random forest machine learning model that included standard clinical and pathological prognostic variables resulted in an area under the ROC curve of 0.83. A model that adds imaging-based heterogeneity features to the clinical and pathological variables resulted in improved model performance for survival prediction with an AUC of 0.94.

CONCLUSIONS

MRI-based texture features are associated with patient outcomes and improve the performance of standard clinical and pathological variables for predicting patient survival in metastatic colorectal cancer.

KEY POINTS

• MRI-based tumor heterogeneity texture features are associated with patient survival outcomes. • MRI-based tumor texture features complement standard clinical and pathological variables for prognosis prediction in metastatic colorectal cancer. • Agglomerative hierarchical clustering shows that patient survival outcomes are associated with different MRI tumor profiles.

Evaluating Heterogeneity of Primary Lung Tumor Using Clinical Routine Magnetic Resonance Imaging and a Tumor Heterogeneity Index

Objective

To improve the assessment of primary tumor heterogeneity in magnetic resonance imaging (MRI) of non-small cell lung cancer (NSCLC), we proposed a method using basic measurements from T1- and T2-weighted MRI.

Methods

One hundred and four NSCLC patients with different T stages were studied. Fifty-two patients were analyzed as training group and another 52 as testing group. The ratios of standard deviation (SD)/mean signal value of primary tumor from T1-weighted (T1WI), T1-enhanced (T1C), T2-weighted (T2WI), and T2 fat suppression (T2fs) images were calculated. In the training group, correlation analyses were performed between the ratios and T stages. Then an ordinal regression model was built to generate the tumor heterogeneous index (THI) for evaluating the heterogeneity of tumor. The model was validated in the testing group.

Results

There were 11, 32, 40, and 21 patients with T1, T2, T3, and T4 disease, respectively. In the training group, the median SD/mean on T1WI, T1C, T2WI, and T2fs sequences was 0.11, 0.19, 0.16, and 0.15 respectively. The SD/mean on T1C (p=0.003), T2WI (p=0.000), and T2fs sequences (p=0.002) correlated significantly with T stages. Patients with more advanced T stage showed higher SD/mean on T2-weighted, T2fs, and T1C sequences. The median THI in the training group was 2.15. THI correlated with T stage significantly (p=0.000). In the testing group, THI was also significantly related to T stages (p=0.001). Higher THI had relevance to more advanced T stage.

Conclusions

The proposed ratio measurements and THI based on MRI can serve as functional radiomic markers that correlated with T stages for evaluating heterogeneity of lung tumors.

Tumor heterogeneity for differentiation between liver tumors and normal liver tissue in 18F-FDG PET/CT

AIM

 Malignancies show higher spatial heterogeneity than normal tissue. We investigated, if textural parameters from FDG PET describing the heterogeneity function as tool to differentiate between tumor and normal liver tissue.

METHODS

 FDG PET/CT scans of 80 patients with liver metastases and 80 patients with results negative upper abdominal organs were analyzed. Metastases and normal liver tissue were analyzed drawing up to three VOIs with a diameter of 25 mm in healthy liver tissue of the tumoral affected and results negative liver, whilst up to 3 metastases per patient were delineated. Within these VOIs 30 different textural parameters were calculated as well as SUV. The parameters were compared in terms of intra-patient and inter-patient variability (2-sided t test). ROC analysis was performed to analyze predictive power and cut-off values.

RESULTS

 28 textural parameters differentiated healthy and pathological tissue (p < 0.05) with high sensitivity and specificity. SUV showed ability to differentiate but with a lower significance. 15 textural parameters as well as SUV showed a significant variation between healthy tissues out of tumour infested and negative livers. Mean intra- and inter-patient variability of metastases were found comparable or lower for 6 of the textural features than the ones of SUV. They also showed good values of mean intra- and inter-patient variability of VOIs drawn in liver tissue of patients with metastases and of results negative ones.

CONCLUSION

 Heterogeneity parameters assessed in FDG PET are promising to classify tissue and differentiate malignant lesions usable for more personalized treatment planning, therapy response evaluation and precise delineation of tumors for target volume determination as part of radiation therapy planning.

Enhanced Rim on MDCT of Colorectal Liver Metastases: Assessment of Ability to Predict Progression-Free Survival and Response to Bevacizumab-Based Chemotherapy

OBJECTIVE. The purpose of this article is to evaluate the enhanced rim on the portal venous phase (PVP) on MDCT as a predictor of 1-year progression-free survival (PFS) and response to bevacizumab-based chemotherapy in patients with colorectal liver metastases (CRLM). MATERIALS AND METHODS. We retrospectively identified 111 patients with primary unresectable CRLM treated with bevacizumab-based chemotherapy at two institutions between 2012 and 2018. Pretreatment contrast-enhanced MDCT images were reviewed and data on clinical characteristics were collected from the electronic medical records. Univariable and multivariable analyses were conducted to assess several imaging features and clinical characteristics as potential predictors of 1-year PFS and objective response rate (ORR). RESULTS. After 1 year of follow-up, liver metastatic tumor progression was detected in 52 patients (46.8%) after bevacizumab-based chemotherapy. A log-rank test showed that enhanced rim on PVP (chi-square test, 5.862; p = 0.015) and the occurrence of liver resection surgery (chi-square test, 7.836; p = 0.005) were significant predictors of 1-year PFS. Multivariable analysis showed that enhanced rim on PVP images was an independent predictor of 1-year PFS (hazard ratio, 0.510; 95% CI, 0.282-0.926; p = 0.027) and ORR (odds ratio, 4.694; p < 0.001). CONCLUSION. The presence of an enhanced rim on PVP MDCT is an independent predictor of survival and response to bevacizumab-based chemotherapy among patients with CRLM.

Identification of Predominant Histopathological Growth Patterns of Colorectal Liver Metastasis by Multi-Habitat and Multi-Sequence Based Radiomics Analysis

Purpose: Developing an MRI-based radiomics model to effectively and accurately predict the predominant histopathologic growth patterns (HGPs) of colorectal liver metastases (CRLMs). Materials and Methods: In this study, 182 resected and histopathological proven CRLMs of chemotherapy-naive patients from two institutions, including 123 replacement CRLMs and 59 desmoplastic CRLMs, were retrospectively analyzed. Radiomics analysis was performed on two regions of interest (ROI), the tumor zone and the tumor-liver interface (TLI) zone. Decision tree (DT) algorithm was used for radiomics modeling on each MR sequence, and fused radiomics model was constructed by combining the radiomics signature of each sequence. The clinical and combination models were developed through multivariate logistic regression method. The performance of the developed models was assessed by receiver operating characteristic (ROC) curves with indicators of area under curve (AUC), accuracy, sensitivity, and specificity. A nomogram was constructed to evaluate the discrimination, calibration, and usefulness. Results: The fused radiomics^tumor and radiomics^TLI models showed better performance than any single sequence and clinical model. In addition, the radiomics^TLI model exhibited better performance than radiomics^tumor model (AUC of 0.912 vs. 0.879) in internal validation cohort. The combination model showed good discrimination, and the AUC of nomogram was 0.971, 0.909, and 0.905 in the training, internal validation, and external validation cohorts, respectively. Conclusion: MRI-based radiomics method has high potential in predicting the predominant HGPs of CRLM. Preoperative non-invasive identification of predominant HGPs could further explore the ability of HGPs as a potential biomarker for clinical treatment strategy, reflecting different biological pathways.

MR Imaging Texture Analysis in the Abdomen and Pelvis

Add "which is a" before "distribution"? Texture analysis (TA) is a form of radiomics that refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MR images of the abdomen and pelvis, with the main strength quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MRTA. Despite these limitations, there is a growing body of literature supporting MRTA. This review discusses application of MRTA to the abdomen and pelvis.

Tumour subregion analysis of colorectal liver metastases using semi-automated clustering based on DCE-MRI: Comparison with histological subregions and impact on pharmacokinetic parameter analysis

PURPOSE

To use a novel segmentation methodology based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to define tumour subregions of liver metastases from colorectal cancer (CRC), to compare these with histology, and to use these to compare extracted pharmacokinetic (PK) parameters between tumour subregions.

MATERIALS AND METHODS

This ethically-approved prospective study recruited patients with CRC and ≥1 hepatic metastases scheduled for hepatic resection. Patients underwent DCE-MRI pre-metastasectomy. Histological sections of resection specimens were spatially matched to DCE-MRI acquisitions and used to define histological subregions of viable and non-viable tumour. A semi-automated voxel-wise image segmentation algorithm based on the DCE-MRI contrast-uptake curves was used to define imaging subregions of viable and non-viable tumour. Overlap of histologically-defined and imaging subregions was compared using the Dice similarity coefficient (DSC). DCE-MRI PK parameters were compared for the whole tumour and histology-defined and imaging-derived subregions.

RESULTS

Fourteen patients were included in the analysis. Direct histological comparison with imaging was possible in nine patients. Mean DSC for viable tumour subregions defined by imaging and histology was 0.738 (range 0.540-0.930). There were significant differences between Ktrans and kep for viable and non-viable subregions (p < 0.001) and between whole lesions and viable subregions (p < 0.001).

CONCLUSION

We demonstrate good concordance of viable tumour segmentation based on pre-operative DCE-MRI with a post-operative histological gold-standard. This can be used to extract viable tumour-specific values from quantitative image analysis, and could improve treatment response assessment in clinical practice.

Signal changes in T2-weighted MRI of liver metastases under bevacizumab-A practical imaging biomarker?

OBJECTIVE

The purpose of this study was to investigate signal changes in T2-weighted magnetic resonance imaging of liver metastases under treatment with and without bevacizumab-containing chemotherapy and to compare these signal changes to tumor contrast enhancement.

MATERIALS AND METHODS

Retrospective analysis of 44 patients, aged 36-84 years, who underwent liver magnetic resonance imaging including T2-weighted and dynamic contrast enhancement sequences. Patients received bevacizumab-containing (n = 22) or conventional cytotoxic chemotherapy (n = 22). Magnetic resonance imaging was obtained at baseline and at three follow-ups (on average 3, 6 and 9 months after initial treatment). Three independent readers rated the T2 signal intensity and the relative contrast enhancement of the metastases on a 5-point scale.

RESULTS

T2 signal intensity of metastases treated with bevacizumab showed a significant (p<0.001) decrease in T2 signal intensity after initial treatment and exhibit compared to conventionally treated metastases significantly (p<0.001 for each follow-up) hypointense (bevacizumab: 0.70 ± 0.83 before vs. -1.55 ± 0.61, -1.91 ± 0.62, and -1.97 ± 0.52; cytotoxic: 0.73 ± 0.79 before vs. -0.69 ± 0.81, -0.71 ± 0.68, and -0.75 ± 0.65 after 3, 6, and 9 months, respectively). T2 signal intensity was strongly correlated with tumor contrast enhancement (r = 0.71; p<0.001). Intra-observer agreement for T2-signal intensity was substantial (κ = 0.75). The agreement for tumoral contrast enhancement between the readers was considerably lower (κ = 0.39).

CONCLUSION

Liver metastases exhibit considerably hypointense in T2-weighted imaging after treatment with bevacizumab, in contrast to conventionally treated liver metastases. Therefore, T2-weighted imaging seems to reflect the effect of bevacizumab.

Early prediction of neoadjuvant treatment outcome in locally advanced breast cancer using parametric response mapping and radial heterogeneity from breast MRI

BACKGROUND

Early prediction of nonresponse is essential in order to avoid inefficient treatments.

PURPOSE

To evaluate if parametrical response mapping (PRM)-derived biomarkers could predict early morphological response (EMR) and pathological complete response (pCR) 24-72 hours after initiation of chemotherapy treatment and whether concentric analysis of nonresponding PRM regions could better predict response.

STUDY TYPE

This was a retrospective analysis of prospectively acquired cohort, nonrandomized, monocentric, diagnostic study.

POPULATION

Sixty patients were initially recruited, with 39 women participating in the final cohort.

FIELD STRENGTH/SEQUENCE

A 1.5T scanner was used for MRI examinations.

ASSESSMENT

Dynamic contrast-enhanced (DCE)-MR images were acquired at baseline (timepoint 1, TP1), 24-72 hours after the first chemotherapy (TP2), and after the end of anthracycline treatment (TP3). PRM was performed after fusion of T₁ subtraction images from TP1 and TP2 using an affine registration algorithm. Pixels with an increase of more than 10% of their value (PRMdce+) were corresponding nonresponding regions of the tumor. Patients with a decrease of maximum diameter (%dDmax) between TP1 and TP3 of more than 30% were defined as EMR responders. pCR patients achieved a residual cancer burden score of 0.

STATISTICAL TESTS

T-test, receiver operating characteristic (ROC) curves, and logistic regression were used for the analysis.

RESULTS

PRM showed a statistical difference between pCR response groups (P < 0.01) and AUC of 0.88 for the prediction of non-pCR. Logistic regression analysis demonstrated that PRMdce+ and Grade II were significant (P < 0.01) for non-pCR prediction (AUC = 0.94). Peripheral tumor region demonstrated higher performance for the prediction of non-pCR (AUC = 0.85) than intermediate and central zones; however, statistical comparison showed no significant difference.

DATA CONCLUSION

PRM could be predictive of non-pCR 24-72 hours after initiation of chemotherapy treatment. Moreover, the peripheral region showed increased AUC for non-pCR prediction and increased signal intensity during treatment for non-pCR tumors, information that could be used for optimal tissue sampling.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 4 J. Magn. Reson. Imaging 2020;51:1403-1411.

Diagnosis of epithelial ovarian cancer using a combined protein biomarker panel

BACKGROUND

An early detection tool for EOC was constructed from analysis of biomarker expression data from serum collected during the UKCTOCS.

METHODS

This study included 49 EOC cases (19 Type I and 30 Type II) and 31 controls, representing 482 serial samples spanning seven years pre-diagnosis. A logit model was trained by analysis of dysregulation of expression data of four putative biomarkers, (CA125, phosphatidylcholine-sterol acyltransferase, vitamin K-dependent protein Z and C-reactive protein); by scoring the specificity associated with dysregulation from the baseline expression for each individual.

RESULTS

The model is discriminatory, passes k-fold and leave-one-out cross-validations and was further validated in a Type I EOC set. Samples were analysed as a simulated annual screening programme, the algorithm diagnosed cases with >30% PPV 1-2 years pre-diagnosis. For Type II cases (~80% were HGS) the algorithm classified 64% at 1 year and 28% at 2 years tDx as severe.

CONCLUSIONS

The panel has the potential to diagnose EOC one-two years earlier than current diagnosis. This analysis provides a tangible worked example demonstrating the potential for development as a screening tool and scrutiny of its properties. Limits on interpretation imposed by the number of samples available are discussed.

Medullary Thyroid Carcinoma: An Update on Imaging

Medullary thyroid carcinoma (MTC), arising from the parafollicular C cells of the thyroid, accounts for 1–2% of thyroid cancers. MTC is frequently aggressive and metastasizes to cervical and mediastinal lymph nodes, lungs, liver, and bones. Although a number of new imaging modalities for directing the management of oncologic patients evolved over the last two decades, the clinical application of these novel techniques is limited in MTC. In this article, we review the biology and molecular aspects of MTC as an important background for the use of current imaging modalities and approaches for this tumor. We discuss the modern and currently available imaging techniques—advanced magnetic resonance imaging (MRI)-based techniques such as whole-body MRI, dynamic contrast-enhanced (DCE) technique, diffusion-weighted imaging (DWI), positron emission tomography/computed tomography (PET/CT) with 18F-FDOPA and 18F-FDG, and integrated positron emission tomography/magnetic resonance (PET/MR) hybrid imaging—for primary as well as metastatic MTC tumor, including its metastatic spread to lymph nodes and the most common sites of distant metastases: lungs, liver, and bones.

Dynamic contrast-enhanced MRI to assess hepatocellular carcinoma response to Transarterial chemoembolization using LI-RADS criteria: A pilot study

PURPOSE

To identify quantitative dynamic contrast-enhanced (DCE)-MRI perfusion parameters indicating tumor response of hepatocellular carcinoma (HCC) to transarterial chemoembolization (TACE).

MATERIALS AND METHODS

This prospective pilot study was approved by our institutional review board; written and informed consent was obtained for each participant. Patients underwent DCE-MRI examinations before and after TACE. A variable flip-angle unenhanced 3D mDixon sequence was performed for T1 mapping. A dynamic 4D mDixon sequence was performed after contrast injection for assessing dynamic signal enhancement. Nonparametric analysis was conducted on the time-intensity curves. Parametric analysis was performed on the time-concentration curves using a dual-input single-compartment model. Treatment response according to Liver Reporting and Data System (LI-RADS) v2018 was used as the reference standard. The comparisons within groups (before vs. after treatment) and between groups (nonviable vs. equivocal or viable tumor) were performed using nonparametric bootstrap taking into account the clustering effect of lesions in patients.

RESULTS

Twenty-eight patients with 52 HCCs (size: 10-104 mm) were evaluated. For nonviable tumors (n = 27), time to peak increased from 62.5 ± 18.2 s before to 83.3 ± 12.8 s after treatment (P< 0.01). For equivocal or viable tumors (n = 25), time to peak and mean transit time significantly increased (from 54.4 ± 24.1 s to 69.5 ± 18.9 s, P < 0.01 and from 14.2 ± 11.8 s to 33.9 ± 36.8 s, P= 0.01, respectively) and the transfer constant from the extracellular and extravascular space to the central vein significantly decreased from 14.8 ± 14.1 to 8.1 ± 9.1 s^-1 after treatment (P= 0.01).

CONCLUSION

This prospective pilot DCE-MRI study showed that time to peak significantly changed after TACE treatment for both groups (nonviable tumors and equivocal or viable tumors). In our cohort, several perfusion parameters may provide an objective marker for differentiation of treatment response after TACE in HCC patients.

The association of intra-therapeutic heterogeneity of somatostatin receptor expression with morphological treatment response in patients undergoing PRRT with [177Lu]-DOTATATE

Aim

Purpose of this study was to evaluate the association of the spatial heterogeneity (asphericity, ASP) in intra-therapeutic SPECT/ CT imaging of somatostatin receptor (SSR) positive metastatic gastroenteropancreatic neuroendocrine neoplasms (GEP-NEN) for morphological treatment response to peptide receptor radionuclide therapy (PRRT). Secondly, we correlated ASP derived form a pre-therapeutic OctreoScan (ASP_[In]) and an intra-therapeutic [¹⁷⁷Lu]-SPECT/CT (ASP_[Lu]).

Materials and methods

Data from first therapy cycle [¹⁷⁷Lu-DOTA⁰-Tyr³]octreotate ([¹⁷⁷Lu]-DOTATATE)-PRRT was retrospectively analyzed in 33 patients (m = 20; w = 13; median age, 72 [46–88] years). The evaluation of response to PRRT was performed according to RECIST 1.1 in responding lesions [RL (SD, PR, CR), n = 104] and non-responding lesions [NRL (PD), n = 27]. The association of SSR tumor heterogeneity with morphological response was evaluated by Kruskal-Wallis test and receiver operating characteristic curve (ROC). The optimal threshold for separation (RL vs. NRL) was calculated using the Youden-index. Relationship between pre- and intra-therapeutic ASP was determined with Spearman’s rank correlation coefficient (ρ) and Bland-Altman plots.

Results

A total of 131 lesions (liver: n = 59, lymph nodes: n = 48, bone: n = 19, pancreas: n = 5) were analyzed. Lesions with higher ASP values showed a significantly poorer response to PRRT (PD, median: 11.3, IQR: 8.5–15.5; SD, median: 3.4, IQR: 2.1–4.5; PR, median 1.7, IQR: 0.9–2.8; CR, median: 0.5, IQR: 0.0–1.3); Kruskal-Wallis, p<0.001). ROC analyses revealed a significant separation between RL and NRL for ASP after 4 months (AUC 0.85, p<0.001) and after 12 months (AUC 0.94, p<0.001). The optimal threshold for ASP was >5.45% (sensitivity 96% and specificity 82%). The correlation coefficient of pre- and intra-therapeutic ASP revealed ρ = 0.72 (p <0.01). The mean absolute difference between ASP_[In] and ASP_[Lu] was -0.04 (95% Limits of Agreement, -6.1–6.0).

Conclusion

Pre- and intra-therapeutic ASP shows a strong correlation and might be an useful tool for therapy monitoring.

Biofabrication strategies for creating microvascular complexity

Design and fabrication of effective biomimetic vasculatures constitutes a relevant and yet unsolved challenge, lying at the heart of tissue repair and regeneration strategies. Even if cell growth is achieved in 3D tissue scaffolds or advanced implants, tissue viability inevitably requires vascularization, as diffusion can only transport nutrients and eliminate debris within a few hundred microns. This engineered vasculature may need to mimic the intricate branching geometry of native microvasculature, referred to herein as vascular complexity, to efficiently deliver blood and recreate critical interactions between the vascular and perivascular cells as well as parenchymal tissues. This review first describes the importance of vascular complexity in labs- and organs-on-chips, the biomechanical and biochemical signals needed to create and maintain a complex vasculature, and the limitations of current 2D, 2.5D, and 3D culture systems in recreating vascular complexity. We then critically review available strategies for design and biofabrication of complex vasculatures in cell culture platforms, labs- and organs-on-chips, and tissue engineering scaffolds, highlighting their advantages and disadvantages. Finally, challenges and future directions are outlined with the hope of inspiring researchers to create the reliable, efficient and sustainable tools needed for design and biofabrication of complex vasculatures.

How rapid advances in imaging are defining the future of precision radiation oncology

Imaging has an essential role in the planning and delivery of radiotherapy. Recent advances in imaging have led to the development of advanced radiotherapy techniques—including image-guided radiotherapy, intensity-modulated radiotherapy, stereotactic body radiotherapy and proton beam therapy. The optimal use of imaging might enable higher doses of radiation to be delivered to the tumour, while sparing normal surrounding tissues. In this article, we review how the integration of existing and novel forms of computed tomography, magnetic resonance imaging and positron emission tomography have transformed tumour delineation in the radiotherapy planning process, and how these advances have the potential to allow a more individualised approach to the cancer therapy. Recent data suggest that imaging biomarkers that assess underlying tumour heterogeneity can identify areas within a tumour that are at higher risk of radio-resistance, and therefore potentially allow for biologically focussed dose escalation. The rapidly evolving concept of adaptive radiotherapy, including artificial intelligence, requires imaging during treatment to be used to modify radiotherapy on a daily basis. These advances have the potential to improve clinical outcomes and reduce radiation-related long-term toxicities. We outline how recent technological advances in both imaging and radiotherapy delivery can be combined to shape the future of precision radiation oncology.

Dynamic contrast-enhanced magnetic resonance imaging in locally advanced rectal cancer: role of perfusion parameters in the assessment of response to treatment

PURPOSE

To correlate dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters to tumor grading and to assess their reliability in predicting pathological complete response (pCR) before neoadjuvant chemoradiotherapy (CRT) in patients with locally advanced rectal cancer (LARC).

MATERIALS AND METHODS

Forty patients (24 male; mean age, 67.3 ± 8.1 years) with histologically proven LARC who had undergone 3-Tesla DCE-MRI before (MRI_1) and after CRT (MRI_2) between August 2015 and February 2016 were included in this retrospective study. DCE-MRI parameters at MRI_1 and MRI_2 were extracted by two board certified radiologists in consensus reading with Olea Sphere 2.3 software using the extended Tofts model. Based on DCE-MRI results, patients were divided in complete responders (CR) and non-complete responders (nCR) and the perfusion parameters were correlated to tumor grading and pCR.

RESULTS

Wash-out and K_ep at MRI_1 showed significant correlation with LARC grading (P = 0.004 and 0.01, respectively). V_e showed a significant increase between MRI_1 (0.47 ± 0.27) and MRI_2 (0.63 ± 0.23; P = 0.007). K_trans measured at MRI_1 was significantly higher in CR (0.66 ± 0.48) compared to nCR (0.53 ± 0.34, P = 0.02).

CONCLUSION

Wash-out and K_ep measured before CRT correlate with LARC grading. V_e changes during CRT, while K_trans measured before CRT may predict the response to therapy. Therefore, DCE-MRI parameters can predict tumor aggressiveness and CRT efficacy, playing a role as imaging biomarkers in patients with LARC.

A radiologist's point of view in the presurgical and intraoperative setting of colorectal liver metastases

Multidisciplinary management of patients with metastatic colorectal cancer requires in each phase an adequate choice of the most appropriate imaging modality. The first challenging step is liver lesions detection and characterization, using several imaging modality ultrasound, computed tomography, magnetic resonance and positron emission tomography. The criteria to establish the metastases resectability have been modified. Not only the lesions number and site but also the functional volume remnant after surgery and the quality of the nontumoral liver must be taken into account. Radiologists should identify the liver functional volume remnant and during liver surgical procedures should collaborate with the surgeon to identify all lesions, including those that disappeared after the therapy, using intraoperative ultrasound with or without contrast medium.

Noninvasive Biomarkers of Colorectal Cancer: Role in Diagnosis and Personalised Treatment Perspectives

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths worldwide. It has been estimated that more than one-third of patients are diagnosed when CRC has already spread to the lymph nodes. One out of five patients is diagnosed with metastatic CRC. The stage of diagnosis influences treatment outcome and survival. Notwithstanding the recent advances in multidisciplinary management and treatment of CRC, patients are still reluctant to undergo screening tests because of the associated invasiveness and discomfort (e.g., colonoscopy with biopsies). Moreover, the serological markers currently used for diagnosis are not reliable and, even if they were useful to detect disease recurrence after treatment, they are not always detected in patients with CRC (e.g., CEA). Recently, translational research in CRC has produced a wide spectrum of potential biomarkers that could be useful for diagnosis, treatment, and follow-up of these patients. The aim of this review is to provide an overview of the newer noninvasive or minimally invasive biomarkers of CRC. Here, we discuss imaging and biomolecular diagnostics ranging from their potential usefulness to obtain early and less-invasive diagnosis to their potential implementation in the development of a bespoke treatment of CRC.

Survival prediction in patients undergoing radionuclide therapy based on intratumoral somatostatin-receptor heterogeneity

The NETTER-1 trial demonstrated significantly improved progression-free survival (PFS) for peptide receptor radionuclide therapy (PRRT) in neuroendocrine tumors (NET) emphasizing the high demand for response prediction in appropriate candidates. In this multicenter study, we aimed to elucidate the prognostic value of tumor heterogeneity as assessed by somatostatin receptor (SSTR)-PET/CT. 141 patients with SSTR-expressing tumors were analyzed obtaining SSTR-PET/CT before PRRT (1-6 cycles, ¹⁷⁷Lu somatostatin analog). Using the Interview Fusion Workstation (Mediso), a total of 872 metastases were manually segmented. Conventional PET parameters as well as textural features representing intratumoral heterogeneity were computed. The prognostic ability for PFS and overall survival (OS) were examined. After performing Cox regression, independent parameters were determined by ROC analysis to obtain cut-off values to be used for Kaplan-Meier analysis. Within follow-up (median, 43.1 months), 75 patients showed disease progression (median, 22.2 m) and 54 patients died (median, 27.6 m). Cox analysis identified 8 statistically independent heterogeneity parameters for time-to-progression and time-to-death. Among them, the textural feature Entropy predicted both PFS and OS. Conventional PET parameters failed in response prediction. Imaging-based heterogeneity assessment provides prognostic information in PRRT candidates and outperformed conventional PET parameters. Its implementation in clinical practice can pave the way for individualized patient management.

Pancreatic adenocarcinoma: cross-sectional imaging techniques

Pancreatic adenocarcinoma is a common malignancy that has a poor prognosis. Imaging is vital in its detection, staging, and management. Although a variety of imaging techniques are available, MDCT is the preferred imaging modality for staging and assessing the resectability of pancreatic adenocarcinoma. MR also has an important adjunct role, and may be used in addition to CT or as a problem-solving tool. A dedicated pancreatic protocol should be acquired as a biphasic technique optimized for the detection of pancreatic adenocarcinoma and to allow accurate local and distant disease staging. Emerging techniques like dual-energy CT and texture analysis of CT and MR images have a great potential in improving lesion detection, characterization, and treatment monitoring.

Advanced imaging to predict response to chemotherapy in colorectal liver metastases - a systematic review

BACKGROUND

The assessment of colorectal liver metastases (CRLM) after treatment with chemotherapy is challenging due to morphological and/or functional change without changes in size. The aim of this review was to assess the value of FDG-PET, FDG-PET-CT, CT and MRI in predicting response to chemotherapy in CRLM.

METHODS

A systematic review was undertaken based on PRISMA statement. PubMed and Embase were searched up to October 2016 for studies on the accuracy of PET, PET-CT, CT and MRI in predicting RECIST or metabolic response to chemotherapy and/or survival in patients with CRLM. Articles evaluating the assessment of response after chemotherapy were excluded.

RESULTS

Sixteen studies met the inclusion criteria and were included for further analysis. Study results were available for 6 studies for FDG-PET(-CT), 6 studies for CT and 9 studies for MRI. Generally, features predicting RECIST or metabolic response often predicted shorter survival. The ADC (apparent diffusion coefficient, on MRI) seems to be the most promising predictor of response and survival. In CT-related studies, few attenuation-related parameters and texture features show promising results. In FDG-PET(-CT), findings were ambiguous.

CONCLUSION

Radiological data on the prediction of response to chemotherapy for CRLM is relatively sparse and heterogeneous. Despite that, a promising parameter might be ADC. Second, there seems to be a seemingly counterintuitive correlation between parameters that predict a good response and also predict poor survival.

Molecular imaging in ovarian cancer

Ovarian cancer has a high mortality and novel-targeted treatment strategies have not resulted in breakthroughs for this disease. Insight into the molecular characteristics of ovarian tumors may improve diagnosis and selection of patients for treatment with targeted therapies. A potential way to achieve this is by means of molecular imaging. Generic tumor processes, such as glucose metabolism ((18)F-fluorodeoxyglucose) and DNA synthesis ((18)F-fluorodeoxythymidine), can be visualized non-invasively. More specific targets, such as hormone receptors, growth factor receptors, growth factors and targets of immunotherapy, can also be visualized. Molecular imaging can capture data on intra-patient tumor heterogeneity and is of potential value for individualized, target-guided treatment selection. Early changes in molecular characteristics during therapy may serve as early predictors of response. In this review, we describe the current knowledge on molecular imaging in the diagnosis and as an upfront or early predictive biomarker in patients with ovarian cancer.

Simultaneous acquisition sequence for improved hepatic pharmacokinetics quantification accuracy (SAHA) for dynamic contrast-enhanced MRI of liver

PURPOSE

To propose a simultaneous acquisition sequence for improved hepatic pharmacokinetics quantification accuracy (SAHA) method for liver dynamic contrast-enhanced MRI.

METHODS

The proposed SAHA simultaneously acquired high temporal-resolution 2D images for vascular input function extraction using Cartesian sampling and 3D large-coverage high spatial-resolution liver dynamic contrast-enhanced images using golden angle stack-of-stars acquisition in an interleaved way. Simulations were conducted to investigate the accuracy of SAHA in pharmacokinetic analysis. A healthy volunteer and three patients with cirrhosis or hepatocellular carcinoma were included in the study to investigate the feasibility of SAHA in vivo.

RESULTS

Simulation studies showed that SAHA can provide closer results to the true values and lower root mean square error of estimated pharmacokinetic parameters in all of the tested scenarios. The in vivo scans of subjects provided fair image quality of both 2D images for arterial input function and portal venous input function and 3D whole liver images. The in vivo fitting results showed that the perfusion parameters of healthy liver were significantly different from those of cirrhotic liver and HCC.

CONCLUSIONS

The proposed SAHA can provide improved accuracy in pharmacokinetic modeling and is feasible in human liver dynamic contrast-enhanced MRI, suggesting that SAHA is a potential tool for liver dynamic contrast-enhanced MRI. Magn Reson Med 79:2629-2641, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Chemotherapy-Response Monitoring of Breast Cancer Patients Using Quantitative Ultrasound-Based Intra-Tumour Heterogeneities

Anti-cancer therapies including chemotherapy aim to induce tumour cell death. Cell death introduces alterations in cell morphology and tissue micro-structures that cause measurable changes in tissue echogenicity. This study investigated the effectiveness of quantitative ultrasound (QUS) parametric imaging to characterize intra-tumour heterogeneity and monitor the pathological response of breast cancer to chemotherapy in a large cohort of patients (n = 100). Results demonstrated that QUS imaging can non-invasively monitor pathological response and outcome of breast cancer patients to chemotherapy early following treatment initiation. Specifically, QUS biomarkers quantifying spatial heterogeneities in size, concentration and spacing of acoustic scatterers could predict treatment responses of patients with cross-validated accuracies of 82 ± 0.7%, 86 ± 0.7% and 85 ± 0.9% and areas under the receiver operating characteristic (ROC) curve of 0.75 ± 0.1, 0.80 ± 0.1 and 0.89 ± 0.1 at 1, 4 and 8 weeks after the start of treatment, respectively. The patients classified as responders and non-responders using QUS biomarkers demonstrated significantly different survivals, in good agreement with clinical and pathological endpoints. The results form a basis for using early predictive information on survival-linked patient response to facilitate adapting standard anti-cancer treatments on an individual patient basis.

Primary Rectal Cancer: Repeatability of Global and Local-Regional MR Imaging Texture Features

Purpose To assess the day-to-day repeatability of global and local-regional magnetic resonance (MR) imaging texture features derived from primary rectal cancer. Materials and Methods After ethical approval and patient informed consent were obtained, two pretreatment T2-weighted axial MR imaging studies performed prospectively with the same imaging unit on 2 consecutive days in 14 patients with rectal cancer (11 men [mean age, 61.7 years], three women [mean age, 70.0 years]) were analyzed to extract (a) global first-order statistical histogram and model-based fractal features reflecting the whole-tumor voxel intensity histogram distribution and repeating patterns, respectively, without spatial information and (b) local-regional second-order and high-order statistical texture features reflecting the intensity and spatial interrelationships between adjacent in-plane or multiplanar voxels or regions, respectively. Repeatability was assessed for 46 texture features, and mean difference, 95% limits of agreement, within-subject coefficient of variation (wCV), and repeatability coefficient (r) were recorded. Results Repeatability was better for global parameters than for most local-regional parameters. In particular, histogram mean, median, and entropy, fractal dimension mean and standard deviation, and second-order entropy, homogeneity, difference entropy, and inverse difference moment demonstrated good repeatability, with narrow limits of agreement and wCVs of 10% or lower. Repeatability was poorest for the following high-order gray-level run-length (GLRL) gray-level zone size matrix (GLZSM) and neighborhood gray-tone difference matrix (NGTDM) parameters: GLRL intensity variability, GLZSM short-zone emphasis, GLZSM intensity nonuniformity, GLZSM intensity variability, GLZSM size zone variability, and NGTDM complexity, demonstrating wider agreement limits and wCVs of 50% or greater. Conclusion MR imaging repeatability is better for global texture parameters than for local-regional texture parameters, indicating that global texture parameters should be sufficiently robust for clinical practice. Online supplemental material is available for this article.

Endometrial Carcinoma: MR Imaging-based Texture Model for Preoperative Risk Stratification-A Preliminary Analysis

Purpose To evaluate the associations among mathematical modeling with the use of magnetic resonance (MR) imaging-based texture features and deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and histologic high-grade endometrial carcinoma. Materials and Methods Institutional review board approval was obtained for this retrospective study. This study included 137 women with endometrial carcinomas measuring greater than 1 cm in maximal diameter who underwent 1.5-T MR imaging before hysterectomy between January 2011 and December 2015. Texture analysis was performed with commercial research software with manual delineation of a region of interest around the tumor on MR images (T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced images and apparent diffusion coefficient maps). Areas under the receiver operating characteristic curve and diagnostic performance of random forest models determined by using a subset of the most relevant texture features were estimated and compared with those of independent and blinded visual assessments by three subspecialty radiologists. Results A total of 180 texture features were extracted and ultimately limited to 11 features for DMI, 12 for LVSI, and 16 for high-grade tumor for random forest modeling. With random forest models, areas under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were estimated at 0.84, 79.3%, 82.3%, 81.0%, 76.7%, and 84.4% for DMI; 0.80, 80.9%, 72.5%, 76.6%, 74.3%, and 79.4% for LVSI; and 0.83, 81.0%, 76.8%, 78.1%, 60.7%, and 90.1% for high-grade tumor, respectively. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of visual assessment for DMI were 84.5%, 82.3%, 83.2%, 77.7%, and 87.8% (reader 3). Conclusion The mathematical models that incorporated MR imaging-based texture features were associated with the presence of DMI, LVSI, and high-grade tumor and achieved equivalent accuracy to that of subspecialty radiologists for assessment of DMI in endometrial cancers larger than 1 cm. However, these preliminary results must be interpreted with caution until they are validated with an independent data set, because the small sample size relative to the number of features extracted may have resulted in overfitting of the models. ^© RSNA, 2017 Online supplemental material is available for this article.

Cancer heterogeneity and imaging

There is interest in identifying and quantifying tumor heterogeneity at the genomic, tissue pathology and clinical imaging scales, as this may help better understand tumor biology and may yield useful biomarkers for guiding therapy-based decision making. This review focuses on the role and value of using x-ray, CT, MRI and PET based imaging methods that identify, measure and map tumor heterogeneity. In particular we highlight the potential value of these techniques and the key challenges required to validate and qualify these biomarkers for clinical use.

Texture analysis of medical images for radiotherapy applications

The high-throughput extraction of quantitative information from medical images, known as radiomics, has grown in interest due to the current necessity to quantitatively characterize tumour heterogeneity. In this context, texture analysis, consisting of a variety of mathematical techniques that can describe the grey-level patterns of an image, plays an important role in assessing the spatial organization of different tissues and organs. For these reasons, the potentiality of texture analysis in the context of radiotherapy has been widely investigated in several studies, especially for the prediction of the treatment response of tumour and normal tissues. Nonetheless, many different factors can affect the robustness, reproducibility and reliability of textural features, thus limiting the impact of this technique. In this review, an overview of the most recent works that have applied texture analysis in the context of radiotherapy is presented, with particular focus on the assessment of tumour and tissue response to radiations. Preliminary, the main factors that have an influence on features estimation are discussed, highlighting the need of more standardized image acquisition and reconstruction protocols and more accurate methods for region of interest identification. Despite all these limitations, texture analysis is increasingly demonstrating its ability to improve the characterization of intratumour heterogeneity and the prediction of clinical outcome, although prospective studies and clinical trials are required to draw a more complete picture of the full potential of this technique.

Novel risk models for early detection and screening of ovarian cancer

PURPOSE

Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS).

RESULTS

Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal.

MATERIALS AND METHODS

This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels.

CONCLUSIONS

These models have encouraging sensitivities for detecting pre-clinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.

Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging

Tumour heterogeneity in cancers has been observed at the histological and genetic levels, and increased levels of intra-tumour genetic heterogeneity have been reported to be associated with adverse clinical outcomes. This review provides an overview of radiomics, radiogenomics, and habitat imaging, and examines the use of these newly emergent fields in assessing tumour heterogeneity and its implications. It reviews the potential value of radiomics and radiogenomics in assisting in the diagnosis of cancer disease and determining cancer aggressiveness. This review discusses how radiogenomic analysis can be further used to guide treatment therapy for individual tumours by predicting drug response and potential therapy resistance and examines its role in developing radiomics as biomarkers of oncological outcomes. Lastly, it provides an overview of the obstacles in these emergent fields today including reproducibility, need for validation, imaging analysis standardisation, data sharing and clinical translatability and offers potential solutions to these challenges towards the realisation of precision oncology.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

After seminal papers over the period 2009 - 2011, the use of texture analysis of PET/CT images for quantification of intratumour uptake heterogeneity has received increasing attention in the last 4 years. Results are difficult to compare due to the heterogeneity of studies and lack of standardization. There are also numerous challenges to address. In this review we provide critical insights into the recent development of texture analysis for quantifying the heterogeneity in PET/CT images, identify issues and challenges, and offer recommendations for the use of texture analysis in clinical research. Numerous potentially confounding issues have been identified, related to the complex workflow for the calculation of textural features, and the dependency of features on various factors such as acquisition, image reconstruction, preprocessing, functional volume segmentation, and methods of establishing and quantifying correspondences with genomic and clinical metrics of interest. A lack of understanding of what the features may represent in terms of the underlying pathophysiological processes and the variability of technical implementation practices makes comparing results in the literature challenging, if not impossible. Since progress as a field requires pooling results, there is an urgent need for standardization and recommendations/guidelines to enable the field to move forward. We provide a list of correct formulae for usual features and recommendations regarding implementation. Studies on larger cohorts with robust statistical analysis and machine learning approaches are promising directions to evaluate the potential of this approach.

Dynamic fractal signature dissimilarity analysis for therapeutic response assessment using dynamic contrast-enhanced MRI

PURPOSE

To develop a dynamic fractal signature dissimilarity (FSD) method as a novel image texture analysis technique for the quantification of tumor heterogeneity information for better therapeutic response assessment with dynamic contrast-enhanced (DCE)-MRI.

METHODS

A small animal antiangiogenesis drug treatment experiment was used to demonstrate the proposed method. Sixteen LS-174T implanted mice were randomly assigned into treatment and control groups (n = 8/group). All mice received bevacizumab (treatment) or saline (control) three times in two weeks, and one pretreatment and two post-treatment DCE-MRI scans were performed. In the proposed dynamic FSD method, a dynamic FSD curve was generated to characterize the heterogeneity evolution during the contrast agent uptake, and the area under FSD curve (AUCFSD) and the maximum enhancement (MEFSD) were selected as representative parameters. As for comparison, the pharmacokinetic parameter K(trans) map and area under MR intensity enhancement curve AUCMR map were calculated. Besides the tumor's mean value and coefficient of variation, the kurtosis, skewness, and classic Rényi dimensions d1 and d2 of K(trans) and AUCMR maps were evaluated for heterogeneity assessment for comparison. For post-treatment scans, the Mann-Whitney U-test was used to assess the differences of the investigated parameters between treatment/control groups. The support vector machine (SVM) was applied to classify treatment/control groups using the investigated parameters at each post-treatment scan day.

RESULTS

The tumor mean K(trans) and its heterogeneity measurements d1 and d2 values showed significant differences between treatment/control groups in the second post-treatment scan. In contrast, the relative values (in reference to the pretreatment value) of AUCFSD and MEFSD in both post-treatment scans showed significant differences between treatment/control groups. When using AUCFSD and MEFSD as SVM input for treatment/control classification, the achieved accuracies were 93.8% and 93.8% at first and second post-treatment scan days, respectively. In comparison, the classification accuracies using d1 and d2 of K(trans) map were 87.5% and 100% at first and second post-treatment scan days, respectively.

CONCLUSIONS

As quantitative metrics of tumor contrast agent uptake heterogeneity, the selected parameters from the dynamic FSD method accurately captured the therapeutic response in the experiment. The potential application of the proposed method is promising, and its addition to the existing DCE-MRI techniques could improve DCE-MRI performance in early assessment of treatment response.

DCE-MRI Perfusion and Permeability Parameters as predictors of tumor response to CCRT in Patients with locally advanced NSCLC

In this prospective study, 36 patients with stage III non-small cell lung cancers (NSCLC), who underwent dynamic contrast-enhanced MRI (DCE-MRI) before concurrent chemo-radiotherapy (CCRT) were enrolled. Pharmacokinetic analysis was carried out after non-rigid motion registration. The perfusion parameters [including Blood Flow (BF), Blood Volume (BV), Mean Transit Time (MTT)] and permeability parameters [including endothelial transfer constant (K^trans), reflux rate (K_ep), fractional extravascular extracellular space volume (V_e), fractional plasma volume (V_p)] were calculated, and their relationship with tumor regression was evaluated. The value of these parameters on predicting responders were calculated by receiver operating characteristic (ROC) curve. Multivariate logistic regression analysis was conducted to find the independent variables. Tumor regression rate is negatively correlated with V_e and its standard variation V_e_SD and positively correlated with K^trans and K_ep. Significant differences between responders and non-responders existed in K^trans, K_ep, V_e, V_e_SD, MTT, BV_SD and MTT_SD (P < 0.05). ROC indicated that V_e < 0.24 gave the largest area under curve of 0.865 to predict responders. Multivariate logistic regression analysis also showed V_e was a significant predictor. Baseline perfusion and permeability parameters calculated from DCE-MRI were seen to be a viable tool for predicting the early treatment response after CCRT of NSCLC.

Imaging in Colorectal Cancer: Progress and Challenges for the Clinicians

The use of imaging in colorectal cancer (CRC) has significantly evolved over the last twenty years, establishing important roles in surveillance, diagnosis, staging, treatment selection and follow up. The range of modalities has broadened with the development of novel tracer and contrast agents, and the fusion of technologies such as positron emission tomography (PET) and computed tomography (CT). Traditionally, the most widely used modality for assessing treatment response in metastasised colon and rectal tumours is CT, combined with use of the RECIST guidelines. However, a growing body of evidence suggests that tumour size does not always adequately correlate with clinical outcomes. Magnetic resonance imaging (MRI) is a more versatile technique and dynamic contrast-enhanced (DCE)-MRI and diffusion-weighted (DW)-MRI may be used to evaluate biological and functional effects of treatment. Integrated fluorodeoxyglucose (FDG)-PET/CT combines metabolic and anatomical imaging to improve sensitivity and specificity of tumour detection, and a number of studies have demonstrated improved diagnostic accuracy of this modality in a variety of tumour types, including CRC. These developments have enabled the progression of treatment strategies in rectal cancer and improved the detection of hepatic metastatic disease, yet are not without their limitations. These include technical, economical and logistical challenges, along with a lack of robust evidence for standardisation and formal guidance. In order to successfully apply these novel imaging techniques and utilise their benefit to provide truly personalised cancer care, advances need to be clinically realised in a routine and robust manner.

Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images

PURPOSE

Changes in textural characteristics of diffuse optical spectroscopic (DOS) functional images, accompanied by alterations in their mean values, are demonstrated here for the first time as early surrogates of ultimate treatment response in locally advanced breast cancer (LABC) patients receiving neoadjuvant chemotherapy (NAC). NAC, as a standard component of treatment for LABC patient, induces measurable heterogeneous changes in tumor metabolism which were evaluated using DOS-based metabolic maps. This study characterizes such inhomogeneous nature of response development, by determining alterations in textural properties of DOS images apparent at early stages of therapy, followed later by gross changes in mean values of these functional metabolic maps.

METHODS

Twelve LABC patients undergoing NAC were scanned before and at four times after treatment initiation, and tomographic DOS images were reconstructed at each time. Ultimate responses of patients were determined clinically and pathologically, based on a reduction in tumor size and assessment of residual tumor cellularity. The mean-value parameters and textural features were extracted from volumetric DOS images for several functional and metabolic parameters prior to the treatment initiation. Changes in these DOS-based biomarkers were also monitored over the course of treatment. The measured biomarkers were applied to differentiate patient responses noninvasively and compared to clinical and pathologic responses.

RESULTS

Responding and nonresponding patients demonstrated different changes in DOS-based textural and mean-value parameters during chemotherapy. Whereas none of the biomarkers measured prior the start of therapy demonstrated a significant difference between the two patient populations, statistically significant differences were observed at week one after treatment initiation using the relative change in contrast/homogeneity of seven functional maps (0.001<p<0.049), and mean value of water content in tissue (p=0.010). The cross-validated sensitivity and specificity of these parameters at week one of therapy ranged between 80%-100% and 67%-100%, respectively. Higher levels of statistically significant differences were exhibited at week four after start of treatment, with cross-validated sensitivities and specificities ranging between 80% and 100% for three textural and three mean-value parameters. The combination of the textural and mean-value parameters in a "hybrid" profile could better separate the two patient populations early on during a course of treatment, with cross-validated sensitivities and specificities of up to 100% (p=0.001).

CONCLUSIONS

The results of this study suggest that alterations in textural characteristics of DOS images, in conjunction with changes in their mean values, can classify noninvasively the ultimate clinical and pathologic response of LABC patients to chemotherapy, as early as one week after start of their treatment. This provides a basis for using DOS imaging as a tool for therapy personalization.

Differentiation of tumor vasculature heterogeneity levels in small animals based on total hemoglobin concentration using magnetic resonance-guided diffuse optical tomography in vivo

Insight into the vasculature of the tumor in small animals has the potential to impact many areas of cancer research. The heterogeneity of the vasculature of a tumor is directly related to tumor stage and disease progression. In this small scale animal study, we investigated the feasibility of differentiating tumors with different levels of vasculature heterogeneity in vivo using a previously developed hybrid magnetic resonance imaging (MRI) and diffuse optical tomography (DOT) system for small animal imaging. Cross-sectional total hemoglobin concentration maps of 10 Fisher rats bearing R3230 breast tumors are reconstructed using multi-wavelength DOT measurements both with and without magnetic resonance (MR) structural a priori information. Simultaneously acquired MR structural images are used to guide and constrain the DOT reconstruction, while dynamic contrast-enhanced MR functional images are used as the gold standard to classify the vasculature of the tumor into two types: high versus low heterogeneity. These preliminary results show that the stand-alone DOT is unable to differentiate tumors with low and high vascular heterogeneity without structural a priori information provided by a high resolution imaging modality. The mean total hemoglobin concentrations comparing the vasculature of the tumors with low and high heterogeneity are significant (p-value 0.02) only when MR structural a priori information is utilized.

Evaluation of the effect of transcytolemmal water exchange analysis for therapeutic response assessment using DCE-MRI: a comparison study

This study compares the shutter-speed (SS) and the Tofts models as used in assessing therapeutic response in a longitudinal DCE-MRI experiment. Sixteen nu/nu mice with implanted colorectal adenocarcinoma cell line (LS-174T) were randomly assigned into treatment/control groups (n  =  8/group) and received bevacizumab/saline twice weekly (Day1/Day4/Day8). All mice were scanned at one pre- (Day0) and two post-treatment (Day2/Day9) time points using a high spatiotemporal resolution DCE-MRI pulse sequence. The CA extravasation rate constant [Formula: see text] from the Tofts/SS model and the mean intracellular water residence time [Formula: see text] from the SS model were analyzed. A biological subvolume (BV) within the tumor was identified based on the [Formula: see text] intensity distribution, and the SS model parameters within the BV ([Formula: see text] and [Formula: see text]) were analyzed. It is found that [Formula: see text] and [Formula: see text] have a similar spatial distribution in the tumor volume. The Bayesian information criterion results show that the SS model was a better fit for all scans. At Day9, the treatment group had significantly higher tumor mean [Formula: see text] (p  =  0.021), [Formula: see text] (p  =  0.021) and [Formula: see text] (p  = 0.045). When BV from transcytolemmal water exchange analysis was adopted, the treatment group had higher mean [Formula: see text] at both Day2 (p  =  0.038) and Day9 (p  =  0.007). Additionally, at Day9, the treatment group had higher mean [Formula: see text] (p  =  0.045) and higher [Formula: see text] spatial heterogeneity indices (Rényi dimensions) d 1 (p  = 0.010) and d 2 (p  = 0.021). When mean [Formula: see text] and its coefficient of variation (CV) were used to separate treatment/control group samples using supporting vector machine, the accuracy of treatment/control classification was 68.8% at Day2 and 87.5% at Day9; in contrast, the Day2/Day9 accuracy were 62.5%/87.5% using tumor mean [Formula: see text] and its CV and were 50.0%/87.5% using tumor mean [Formula: see text] and its CV, respectively. These results suggest that the SS model parameters outperformed the Tofts model parameters in terms of capturing bevacizumab therapeutic effect in this longitudinal experiment.