Multiparametric imaging of tumor response to therapy

Spectrum of Imaging Findings and Complications After Hepatic Transarterial Chemoembolization for Liver Tumors

ABSTRACT

This study reviews the spectrum of imaging findings and complications after transarterial chemoembolization (TACE) for the treatment of primary liver tumors (hepatocellular carcinoma, cholangiocarcinoma) and liver metastases. The review encompasses a spectrum of imaging criteria for assessing treatment response, including the modified Response Evaluation Criteria in Solid Tumors guidelines, tumor enhancement, and apparent diffusion coefficient alterations.We discuss the expected posttreatment changes and imaging responses to TACE, describing favorable and poor responses. Moreover, we present cases that demonstrate potential complications post-TACE, including biloma formation, acute cholecystitis, abscesses, duodenal perforation, arterial injury, and nontarget embolization. Each complication is described in detail, considering its causes, risk factors, clinical presentation, and imaging characteristics.To illustrate these findings, a series of clinical cases is presented, featuring diverse imaging modalities including computed tomography, magnetic resonance imaging, and digital subtraction angiography.

Diagnostic performance of MRI for assessing axillary lymph node status after neoadjuvant chemotherapy in breast cancer: a systematic review and meta-analysis

OBJECTIVE

This systematic review examined the diagnostic performance of magnetic resonance imaging (MRI) for assessing axillary lymph node status (ALNS) after neoadjuvant chemotherapy (NAC) in breast cancer patients.

METHODS

We searched PubMed, Embase, Cochrane Library, and Web of Science to identify relevant studies and used the QUADAS-2 tool to assess methodological quality of eligible studies. We used STATA version 12.0 to perform data pooling, heterogeneity testing, subgroup analysis, and sensitivity analysis.

RESULTS

For the 21 enrolled studies, including 2875 patients, the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were respectively 0.63 (95% CI: 0.53-0.72), 0.75 (95% CI: 0.68-0.81), 2.52 (95% CI: 1.98-3.19), 0.50 (95% CI: 0.39-0.63), and 5.08 (95% CI: 3.38-7.63). The AUC was 0.76 (95% CI: 0.72-0.79). I2 values of sensitivity (I2 = 94.41%) and specificity (I2 = 88.97%) were both > 50%. For the initial positive ALN patients, the pooled sensitivity and specificity were 0.64 (95% CI: 0.53-0.75) and 0.74 (95% CI: 0.64-0.82), respectively. Sensitivity analyses by focusing on studies with MRI performed post-NAC, studies using DCE-MRI, or studies with low risk of bias showed similar results to the primary analyses.

CONCLUSION

MRI may have suboptimal diagnostic value in assessing ALNS after NAC for breast cancer patients. Due to the inconsistency of NAC regimens, the variability of axillary surgery, and the lack of time interval between MRI and surgery, further studies are needed to confirm our findings.

CLINICAL RELEVANCE STATEMENT

Our study provided the diagnostic value of MRI in assessing axillary lymph node status after neoadjuvant chemotherapy for breast cancer patients.

KEY POINTS

• MRI may have suboptimal diagnostic value in assessing axillary lymph node status after NAC for general breast cancer patients. • The initial axillary lymph node status has little impact on the diagnostic efficacy of MRI. • The substantial heterogeneity among studies highlights the need for further studies to provide more high-quality evidence in this field.

Targeting vascular normalization: a promising strategy to improve immune-vascular crosstalk in cancer immunotherapy

Blood vessels are a key target for cancer therapy. Compared with the healthy vasculature, tumor blood vessels are extremely immature, highly permeable, and deficient in pericytes. The aberrantly vascularized tumor microenvironment is characterized by hypoxia, low pH, high interstitial pressure, and immunosuppression. The efficacy of chemotherapy, radiotherapy, and immunotherapy is affected by abnormal blood vessels. Some anti-angiogenic drugs show vascular normalization effects in addition to targeting angiogenesis. Reversing the abnormal state of blood vessels creates a normal microenvironment, essential for various cancer treatments, specifically immunotherapy. In addition, immune cells and molecules are involved in the regulation of angiogenesis. Therefore, combining vascular normalization with immunotherapy may increase the efficacy of immunotherapy and reduce the risk of adverse reactions. In this review, we discussed the structure, function, and formation of abnormal vessels. In addition, we elaborated on the role of the immunosuppressive microenvironment in the formation of abnormal vessels. Finally, we described the clinical challenges associated with the combination of immunotherapy with vascular normalization, and highlighted future research directions in this therapeutic area.

Hyperpolarised 13C-MRI using 13C-pyruvate in breast cancer: A review

Tumour metabolism can be imaged with a novel imaging technique termed hyperpolarised carbon-13 (13C)-MRI using probes, i.e., endogenously found molecules that are labeled with 13C. Hyperpolarisation of the 13C label increases the sensitivity to a level that allows dynamic imaging of the distribution and metabolism of the probes. Dynamic imaging of [1-13C]pyruvate metabolism is of particular biological interest in cancer because of the Warburg effect resulting in the intratumoural accumulation of [1-13C]pyruvate and conversion to [1-13C]lactate. Numerous preclinical studies in breast cancer and other tumours have shown that hyperpolarised 13C-pyruvate has potential for metabolic phenotyping and response assessment at earlier timepoints than the current clinical imaging techniques allow. The clinical feasibility of hyperpolarised 13C-MRI after the injection of pyruvate in patients with breast cancer has now been demonstrated, with increased 13C-label exchange between pyruvate and lactate present in higher grade tumours with associated increased expression of the monocarboxylate transporter 1 (MCT1), the transmembrane transporter mediating intracellular pyruvate uptake, and lactate dehydrogenase (LDH) as the enzyme catalysing the conversion of pyruvate to lactate. Furthermore, a study in patients with breast cancer undergoing neoadjuvant chemotherapy suggested that early changes in 13C-label exchange can distinguish between patients who reach pathologic complete response (pCR) and those who do not. This review summarises the current literature on preclinical and clinical research on hyperpolarised 13C-MRI with [1-13C]-pyruvate in breast cancer imaging.

AI-Enhanced PET and MR Imaging for Patients with Breast Cancer

New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.

Early Prediction of Response to Neoadjuvant Chemotherapy Using Quantitative Parameters on Automated Breast Ultrasound Combined with Contrast-Enhanced Ultrasound in Breast Cancer

OBJECTIVE

This prospective study was aimed at evaluating the role of automated breast ultrasound (ABUS) and contrast-enhanced ultrasound (CEUS) in the early prediction of treatment response to neoadjuvant chemotherapy (NAC) in patients with breast cancer.

METHODS

Forty-three patients with pathologically confirmed invasive breast cancer treated with NAC were included. The standard for evaluation of response to NAC was based on surgery within 21 d of completing treatment. The patients were classified as having a pathological complete response (pCR) and a non-pCR. All patients underwent CEUS and ABUS 1 wk before receiving NAC and after two treatment cycles. The rising time (RT), time to peak (TTP), peak intensity (PI), wash-in slope (WIS) and wash-in area under the curve (Wi-AUC) were measured on the CEUS images before and after NAC. The maximum tumor diameters in the coronal and sagittal planes were measured on ABUS, and the tumor volume (V) was calculated. The difference (∆) in each parameter between the two treatment time points was compared. Binary logistic regression analysis was used to identify the predictive value of each parameter.

RESULTS

∆V, ∆TTP and ∆PI were independent predictors of pCR. The CEUS-ABUS model achieved the highest AUC (0.950), followed by those based on CEUS (0.918) and ABUS (0.891) alone.

CONCLUSION

The CEUS-ABUS model could be used clinically to optimize the treatment of patients with breast cancer.

Watch-and-Wait Approach to Rectal Cancer: The Role of Imaging

The diagnosis and treatment of rectal cancer have evolved dramatically over the past several decades. At the same time, its incidence has increased in younger populations. This review will inform the reader of advances in both diagnosis and treatment. These advances have led to the watch-and-wait approach, otherwise known as nonsurgical management. This review briefly outlines changes in medical and surgical treatment, advances in MRI technology and interpretation, and landmark studies or trials that have led to this exciting juncture. Herein, the authors delve into current state-of-the-art methods to assess response to treatment with MRI and endoscopy. Currently, these methods for avoiding surgery can be used to detect a complete clinical response in as many as 50% of patients with rectal cancer. Finally, the limitations of imaging and endoscopy and future challenges will be discussed.

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

OBJECTIVES

Multiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT).

METHODS

We retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient's disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders.

RESULTS

The interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71-0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p  < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT.

CONCLUSION

The mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC.

TRIAL REGISTRATION

Retrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).

Clinical utility of combined assessments of 4D volumetric perfusion CT, diffusion-weighted MRI and 18F-FDG PET-CT for the prediction of outcomes of head and neck squamous cell carcinoma treated with chemoradiotherapy

BACKGROUND

Multiparametric imaging has been seen as a route to improved prediction of chemoradiotherapy treatment outcomes. Four-dimensional volumetric perfusion CT (4D PCT) is useful for whole-organ perfusion measurement, as it reflects the heterogeneity of the tumor and its perfusion parameters. However, there has been no study using multiparametric imaging including 4D PCT for the prognostic prediction of chemoradiotherapy. The purpose of this study was to determine whether combining assessments of 4D PCT with diffusion-weighted MRI (DWI) and ¹⁸F-fluorodeoxyglucose PET-CT could enhance prognostic accuracy in head and neck squamous cell carcinoma (HNSCC) patients treated with chemoradiotherapy.

METHODS

We examined 53 patients with HNSCC who underwent 4D PCT, DWI and PET-CT before chemoradiotherapy. The imaging and clinical parameters were assessed the relations to locoregional control (LRC) and progression-free survival (PFS) by logistic regression analyses. A receiver operating characteristic (ROC) analysis was performed to assess the accuracy of the significant parameters identified by the multivariate analysis for the prediction of LRC and PFS. We additionally assessed using the scoring system whether these independent parameters could have a complementary role for the prognostic prediction.

RESULTS

The median follow-up was 30 months. In multivariate analysis, blood flow (BF; p = 0.02) and blood volume (BV; p = 0.04) were significant prognostic factors for LRC, and BF (p = 0.03) and skewness of the ADC histogram (p = 0.02) were significant prognostic factors for PFS. A significant positive correlation was found between BF and BV (ρ = 0.6, p < 0.001) and between BF and skewness (ρ = 0.46, p < 0.01). The ROC analysis showed that prognostic accuracy for LRC of BF, BV, and combination of BF and BV were 77.8%, 70%, and 92.9%, and that for PFS of BF, skewness, and combination of BF and skewness were 55.6%, 63.2%, and 77.5%, respectively. The scoring system demonstrated that the combination of higher BF and higher BV was significantly associated with better LRC (p = 0.04), and the combination of lower BF and lower skewness was significantly associated with worse PFS (p = 0.004).

CONCLUSION

A combination of parameters derived from 4DPCT and ADC histograms may enhance prognostic accuracy in HNSCC patients treated with chemoradiotherapy.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely. The approach proposed offers several advantages over QIBs as multiple endpoints and avoids combining them into a single composite that obscures the medical meaning of the individual measurements. We focus on establishing statistically rigorous methods to create a single, simultaneous measure from multiple QIBs that preserves the sensitivity of each univariate QIB while incorporating the correlation among QIBs. Details are provided for metrological methods to quantify the technical performance. Methods to reduce the set of QIBs, test the superiority of the mp-QIB model to any univariate QIB model, and design study strategies for generating precision and validity claims are also provided. QIBs of Alzheimer's Disease from the ADNI merge data set are used as a case study to illustrate the methods described.

Radiomic Analysis of Multi-parametric MR Images (MRI) for Classification of Parotid Tumors

Background

Characterization of parotid tumors before surgery using multi-parametric magnetic resonance imaging (MRI) scans can support clinical decision making about the best-suited therapeutic strategy for each patient.

Objective

This study aims to differentiate benign from malignant parotid tumors through radiomics analysis of multi-parametric MR images, incorporating T2-w images with ADC-map and parametric maps generated from Dynamic Contrast Enhanced MRI (DCE-MRI).

Material and Methods

MRI scans of 31 patients with histopathologically-confirmed parotid gland tumors (23 benign, 8 malignant) were included in this retrospective study. For DCE-MRI, semi-quantitative analysis, Tofts pharmacokinetic (PK) modeling, and five-parameter sigmoid modeling were performed and parametric maps were generated. For each patient, borders of the tumors were delineated on whole tumor slices of T2-w image, ADC-map, and the late-enhancement dynamic series of DCE-MRI, creating regions-of-interest (ROIs). Radiomic analysis was performed for the specified ROIs.

Results

Among the DCE-MRI-derived parametric maps, wash-in rate (WIR) and PK-derived K^trans parameters surpassed the accuracy of other parameters based on support vector machine (SVM) classifier. Radiomics analysis of ADC-map outperformed the T2-w and DCE-MRI techniques using the simpler classifier, suggestive of its inherently high sensitivity and specificity. Radiomics analysis of the combination of T2-w image, ADC-map, and DCE-MRI parametric maps resulted in accuracy of 100% with both classifiers with fewer numbers of selected texture features than individual images.

Conclusion

In conclusion, radiomics analysis is a reliable quantitative approach for discrimination of parotid tumors and can be employed as a computer-aided approach for pre-operative diagnosis and treatment planning of the patients.

MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method

Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.

Update on MR Imaging of Soft Tissue Tumors of Head and Neck

This article reviews soft tissue tumors of the head and neck following the 2020 revision of WHO Classification of Soft Tissue and Bone Tumours. Common soft tissue tumors in the head and neck and tumors are discussed, along with newly added entities to the classification system. Salient clinical and imaging features that may allow for improved diagnostic accuracy or to narrow the imaging differential diagnosis are covered. Advanced imaging techniques are discussed, with a focus on diffusion-weighted and dynamic contrast imaging and their potential to help characterize soft tissue tumors and aid in distinguishing malignant from benign tumors.

Update in Imaging Evaluation of Bone and Soft Tissue Sarcomas

A evolução na avaliação por imagens dos sarcomas musculoesqueléticos contribuiu para melhora significativa no prognóstico e na sobrevida dos portadores destas neoplasias. A caracterização precisa destas lesões, mediante utilização das modalidades de imagem mais adequadas a cada condição clínica apresentada, é de suma importância no delineamento da abordagem terapêutica a ser instituída, com impacto direto sobre os desfechos clínicos. O presente artigo busca atualizar o leitor a propósito das metodologias de imagem no contexto da avaliação local e sistêmica dos sarcomas ósseos e das partes moles.

Descriptive analysis of MRI functional changes occurring during reduced dose radiotherapy for myxoid liposarcomas

OBJECTIVES

Myxoid liposarcomas (MLS) show enhanced response to radiotherapy due to their distinctive vascular pattern and therefore could be effectively treated with lower radiation doses. This is a descriptive study to explore the use of functional MRI to identify response in a uniform cohort of MLS patients treated with reduced dose radiotherapy.

METHODS

10 patients with MLS were imaged pre-, during, and post-radiotherapy receiving reduced dose radiotherapy and the response to treatment was histopathologically assessed post-radiotherapy. Apparent diffusion coefficient (ADC), T2* relaxation time, volume transfer constant (Ktrans), initial area under the gadolinium curve over 60 s (IAUGC60) and (Gd) were estimated for a central tumour volume.

RESULTS

All parameters showed large inter- and intrasubject variabilities. Pre-treatment (Gd), IAUGC60 and Ktrans were significantly different between responders and non-responders. Post-radiotherapy reductions from baseline were demonstrated for T2*, (Gd), IAUGC60 and Ktrans for responders. No statistically significant ADC differences were demonstrated between the two response groups. Significantly greater early tumour volume reductions were observed for responders.

CONCLUSIONS

MLS are heterogenous lesions, characterised by a slow gradual contrast-agent uptake. Pre-treatment vascular parameters, early changes to tumour volume, vascular parameters and T2* have potential in identifying response to treatment. The delayed (Gd) is a suitable descriptive parameter, relying simply on T1 measurements. Volume changes precede changes in MLS functionality and could be used to identify early response.

ADVANCES IN KNOWLEDGE

MLS are are characterised by slow gradual contrast-agent uptake. Measurement of the delayed contrast-agent uptake (Gd) is simple to implement and able to discriminate response.

PET Molecular Imaging: A Holistic Review of Current Practice and Emerging Perspectives for Diagnosis, Therapeutic Evaluation and Prognosis in Clinical Oncology

PET/CT molecular imaging has been imposed in clinical oncological practice over the past 20 years, driven by its two well-grounded foundations: quantification and radiolabeled molecular probe vectorization. From basic visual interpretation to more sophisticated full kinetic modeling, PET technology provides a unique opportunity to characterize various biological processes with different levels of analysis. In clinical practice, many efforts have been made during the last two decades to standardize image analyses at the international level, but advanced metrics are still under use in practice. In parallel, the integration of PET imaging with radionuclide therapy, also known as radiolabeled theranostics, has paved the way towards highly sensitive radionuclide-based precision medicine, with major breakthroughs emerging in neuroendocrine tumors and prostate cancer. PET imaging of tumor immunity and beyond is also emerging, emphasizing the unique capabilities of PET molecular imaging to constantly adapt to emerging oncological challenges. However, these new horizons face the growing complexity of multidimensional data. In the era of precision medicine, statistical and computer sciences are currently revolutionizing image-based decision making, paving the way for more holistic cancer molecular imaging analyses at the whole-body level.

Combined 18F-FET PET and diffusion kurtosis MRI in posttreatment glioblastoma: differentiation of true progression from treatment-related changes

Background

Radiological differentiation of tumor progression (TPR) from treatment-related changes (TRC) in pretreated glioblastoma is crucial. This study aimed to explore the diagnostic value of diffusion kurtosis MRI combined with information derived from O-(2-[¹⁸F]-fluoroethyl)-l-tyrosine (¹⁸F-FET) PET for the differentiation of TPR from TRC in patients with pretreated glioblastoma.

Methods

Thirty-two patients with histomolecularly defined and pretreated glioblastoma suspected of having TPR were included in this retrospective study. Twenty-one patients were included in the TPR group, and 11 patients in the TRC group, as assessed by neuropathology or clinicoradiological follow-up. Three-dimensional (3D) regions of interest were generated based on increased ¹⁸F-FET uptake using a tumor-to-brain ratio of 1.6. Furthermore, diffusion MRI kurtosis maps were obtained from the same regions of interest using co-registered ¹⁸F-FET PET images, and advanced histogram analysis of diffusion kurtosis map parameters was applied to generated 3D regions of interest. Diagnostic accuracy was analyzed by receiver operating characteristic curve analysis and combinations of PET and MRI parameters using multivariate logistic regression.

Results

Parameters derived from diffusion MRI kurtosis maps show high diagnostic accuracy, up to 88%, for differentiating between TPR and TRC. Logistic regression revealed that the highest diagnostic accuracy of 94% (area under the curve, 0.97; sensitivity, 94%; specificity, 91%) was achieved by combining the maximum tumor-to-brain ratio of ¹⁸F-FET uptake and diffusion MRI kurtosis metrics.

Conclusions

The combined use of ¹⁸F-FET PET and MRI diffusion kurtosis maps appears to be a promising approach to improve the differentiation of TPR from TRC in pretreated glioblastoma and warrants further investigation.

Opportunities in cancer imaging: a review of oesophageal, gastric and colorectal malignancies

The incidence of gastrointestinal (GI) malignancy is increasing worldwide. In particular, there is a concerning rise in incidence of GI cancer in younger adults. Direct endoscopic visualisation of luminal tumour sites requires invasive procedures, which are associated with certain risks, but remain necessary because of limitations in current imaging techniques and the continuing need to obtain tissue for diagnosis and genetic analysis; however, management of GI cancer is increasingly reliant on non-invasive, radiological imaging to diagnose, stage, and treat these malignancies. Oesophageal, gastric, and colorectal malignancies require specialist investigation and treatment due to the complex nature of the anatomy, biology, and subsequent treatment strategies. As cancer imaging techniques develop, many opportunities to improve tumour detection, diagnostic accuracy and treatment monitoring present themselves. This review article aims to report current imaging practice, advances in various radiological modalities in relation to GI luminal tumour sites and describes opportunities for GI radiologists to improve patient outcomes.

Toward radiomics for assessment of response to systemic therapies in lung cancer

This editorial comment explains recent developments in radiomics regarding the use of quantitative imaging biomarkers to predict lung cancer sensitivity to a variety of cancer therapies. Tumor response assessment has been a crucial component guiding cancer treatment. Evaluation of treatment response was standardized and classically based on measuring changes in tumor lesion size. Recent breakthroughs in artificial intelligence pave the way for the use of radiomics in tumor response assessment. Such objective techniques would bring a remarkable transformation to conventional methods, which can be inherently subjective. Successful implementation of these technologies would allow for faster and more accurate predictions of treatment efficacy, which will be critical to the advancement of personalized medicine.

PET Parametric Imaging: Past, Present, and Future

Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

PURPOSE

To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.

PATIENTS AND METHODS

We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort.

RESULTS

These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP.

CONCLUSION

Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.

Cancer genotypes prediction and associations analysis from imaging phenotypes: a survey on radiogenomics

In this paper, we present a survey on the progress of radiogenomics research, which predicts cancer genotypes from imaging phenotypes and investigates the associations between them. First, we present an overview of the popular technology modalities for obtaining diagnostic medical images. Second, we summarize recently used methodologies for radiogenomics analysis, including statistical analysis, radiomics and deep learning. And then, we give a survey on the recent research based on several types of cancers. Finally, we discuss these studies and propose possible future research directions. In conclusion, we have identified strong correlations between cancer genotypes and imaging phenotypes. In addition, with the rapid growth of medical data, deep learning models show great application potential for radiogenomics.

Radiomics in Lung Cancer from Basic to Advanced: Current Status and Future Directions

Ideally, radiomics features and radiomics signatures can be used as imaging biomarkers for diagnosis, staging, prognosis, and prediction of tumor response. Thus, the number of published radiomics studies is increasing exponentially, leading to a myriad of new radiomics-based evidence for lung cancer. Consequently, it is challenging for radiologists to keep up with the development of radiomics features and their clinical applications. In this article, we review the basics to advanced radiomics in lung cancer to guide young researchers who are eager to start exploring radiomics investigations. In addition, we also include technical issues of radiomics, because knowledge of the technical aspects of radiomics supports a well-informed interpretation of the use of radiomics in lung cancer.

18F-FDG PET and DCE kinetic modeling and their correlations in primary NSCLC: first voxel-wise correlative analysis of human simultaneous [18F]FDG PET-MRI data

OBJECTIVES

To decipher the correlations between PET and DCE kinetic parameters in non-small-cell lung cancer (NSCLC), by using voxel-wise analysis of dynamic simultaneous [18F]FDG PET-MRI.

MATERIAL AND METHODS

Fourteen treatment-naïve patients with biopsy-proven NSCLC prospectively underwent a 1-h dynamic [18F]FDG thoracic PET-MRI scan including DCE. The PET and DCE data were normalized to their corresponding T1-weighted MR morphological space, and tumors were masked semi-automatically. Voxel-wise parametric maps of PET and DCE kinetic parameters were computed by fitting the dynamic PET and DCE tumor data to the Sokoloff and Extended Tofts models respectively, by using in-house developed procedures. Curve-fitting errors were assessed by computing the relative root mean square error (rRMSE) of the estimated PET and DCE signals at the voxel level. For each tumor, Spearman correlation coefficients (rs) between all the pairs of PET and DCE kinetic parameters were estimated on a voxel-wise basis, along with their respective bootstrapped 95% confidence intervals (n = 1000 iterations).

RESULTS

Curve-fitting metrics provided fit errors under 20% for almost 90% of the PET voxels (median rRMSE = 10.3, interquartile ranges IQR = 8.1; 14.3), whereas 73.3% of the DCE voxels showed fit errors under 45% (median rRMSE = 31.8%, IQR = 22.4; 46.6). The PET-PET, DCE-DCE, and PET-DCE voxel-wise correlations varied according to individual tumor behaviors. Beyond this wide variability, the PET-PET and DCE-DCE correlations were mainly high (absolute rs values > 0.7), whereas the PET-DCE correlations were mainly low to moderate (absolute rs values < 0.7). Half the tumors showed a hypometabolism with low perfused/vascularized profile, a hallmark of hypoxia, and tumor aggressiveness.

CONCLUSION

A dynamic "one-stop shop" procedure applied to NSCLC is technically feasible in clinical practice. PET and DCE kinetic parameters assessed simultaneously are not highly correlated in NSCLC, and these correlations showed a wide variability among tumors and patients. These results tend to suggest that PET and DCE kinetic parameters might provide complementary information. In the future, this might make PET-MRI a unique tool to characterize the individual tumor biological behavior in NSCLC.

MRI/TRUS fusion vs. systematic biopsy: intra-patient comparison of diagnostic accuracy for prostate cancer using PI-RADS v2

OBJECTIVE

To evaluate the efficacy of multiparametric magnetic resonance/transrectal ultrasound fusion (MRI/TRUS fusion) biopsy versus systematic biopsy and its association with PI-RADS v2 categories in patients with suspected prostate cancer.

MATERIALS AND METHODS

122 patients undergoing both MRI/TRUS fusion and systematic biopsy, with suspicion of prostate cancer, with suspicious findings on MRI based on PI-RADS v2, were included between April 2016 and March 2017. Comparison of tumor detection rates using each technique and combined techniques was performed for all lesions as well as those that are traditionally difficult to access (i.e., anterior lesions).

RESULTS

Prostate cancer was detected in 83/122 patients (68%) with 74.6% clinically significant lesions (Gleason 3 + 4 or greater). There was a statistically significant difference in presence of clinically significant prostate cancer in PI-RADS v2 categories of 3, 4, and 5 (20%, 52% and 77%, respectively, p < 0.001). Fusion biopsy was positive in a significantly higher percentage of patients versus systematic biopsy (56% versus 48%, respectively, p < 0.05). The fusion biopsy alone was positive in 20%. Of 34 patients with anterior lesions on MRI, 44% were detected only by fusion biopsy, with a joint yield of 71%. In patients with previous negative systematic biopsies, 48.7% lesions were found by fusion biopsy with 20.5% being exclusively positive by this method. The percentage of positive cores for fusion biopsies was significantly higher than for systematic biopsies (26% vs. 12.3%, p < 0.001).

CONCLUSION

The incorporation of MRI/TRUS fusion biopsy significantly improves the detection rate of prostate cancer versus systematic biopsy, particularly for anterior lesions.

The flow-metabolism ratio might predict treatment response and survival in patients with locally advanced esophageal squamous cell carcinoma

Background

Perfusion CT can offer functional information about tumor angiogenesis, and ¹⁸F-FDG PET/CT quantifies the glucose metabolic activity of tumors. This prospective study aims to investigate the value of biologically relevant imaging biomarkers for predicting treatment response and survival outcomes in patients with locally advanced esophageal squamous cell cancer (LA ESCC).

Methods

Twenty-seven patients with pathologically proven ESCC were included. All patients had undergone perfusion CT and ¹⁸F-FDG PET/CT using separate imaging systems before receiving definitive chemoradiotherapy (dCRT). The perfusion parameters included blood flow (BF), blood volume (BV), and time to peak (TTP), and the metabolic parameters included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The flow-metabolism ratio (FMR) was defined as BF divided by SUVmax. Statistical methods used included Spearman’s rank correlation, Mann–Whitney U test or two-sample t test, receiver operating characteristic (ROC) curve analysis, the Kaplan–Meier method, and Cox proportional hazards models.

Results

The median overall survival (OS) and progression-free survival (PFS) were 18 and 11.6 months, respectively. FMR was significantly positively correlated with BF (r = 0.886, p < 0.001) and negatively correlated with SUVmax (r = − 0.547, p = 0.003) and TTP (r = − 0.462, p = 0.015) in the tumors. However, there was no significant correlation between perfusion and PET parameters. After dCRT, 14 patients (51.9%) were identified as responders, and another 13 were nonresponders. The BF and FMR of the responders were significantly higher than those of the nonresponders (42.05 ± 16.47 vs 27.48 ± 8.55, p = 0.007; 3.18 ± 1.15 vs 1.84 ± 0.65, p = 0.001). The ROC curves indicated that the FMR [area under the curve (AUC) = 0.846] was a better biomarker for predicting treatment response than BF (AUC = 0.802). Univariable Cox analysis revealed that of all imaging parameters, only the FMR was significantly correlated with overall survival (OS) (p = 0.015) and progression-free survival (PFS) (p = 0.017). Specifically, patients with a lower FMR had poorer survival. Multivariable analysis showed that after adjusting for age, clinical staging, and treatment response, the FMR remained an independent predictor of OS (p = 0.026) and PFS (p = 0.014).

Conclusions

The flow-metabolism mismatch demonstrated by a low FMR shows good potential in predicting chemoradiotherapy sensitivity and prognosis in ESCC.

Does multiparametric imaging with 18F-FDG-PET/MRI capture spatial variation in immunohistochemical cancer biomarkers in head and neck squamous cell carcinoma?

Background

The purpose of this study is to test if functional multiparametric imaging with ¹⁸F-FDG-PET/MRI correlates spatially with immunohistochemical biomarker status within a lesion of head and neck squamous cell carcinoma (HNSCC), and also whether a biopsy with the highest FDG uptake was more likely to have the highest PD-L1 expression or the highest percentage of vital tumour cells (VTC) compared with a random biopsy.

Methods

Thirty-one patients with HNSCC were scanned on an integrated PET/MRI scanner with FDG prior to surgery in this prospective study. Imaging was quantified with SUV, ADC and K^trans. A 3D-morphometric MRI scan of the specimen was used to co-register the patient and the specimen scans. All specimens were sectioned in consecutive slices, and slices from six different locations were selected randomly from each tumour. Core biopsies were performed to construct TMA blocks for IHC staining with the ten predefined biomarkers. The spatial correlation was assessed with a partial correlation analysis.

Results

Twenty-eight patients with a total of 33 lesions were eligible for further analysis. There were significant correlations between the three imaging biomarkers and some of the IHC biomarkers. Moreover, a biopsy taken from the most FDG-avid part of the tumour did not have a statistically significantly higher probability of higher PD-L1 expression or VTC, compared with a random biopsy.

Conclusion

We found statistically significant correlations between functional imaging parameters and key molecular cancer markers.

Tracking tumor radiotherapy response in vivo with Cherenkov-excited luminescence ink imaging

This study demonstrates remote imaging for in vivo detection of radiation-induced tumor microstructural changes by tracking the diffusive spread of injected intratumor UV excited tattoo ink using Cherenkov-excited luminescence imaging (CELI). Micro-liter quantities of luminescent tattoo ink with UV absorption and visible emission were injected at a depth of 2 mm into mouse tumors prior to receiving a high dose treatment of radiation. X-rays from a clinical linear accelerator were used to excite phosphorescent compounds within the tattoo ink through Cherenkov emission. The in vivo phosphorescence was detected using a time-gated intensified CMOS camera immediately after injection, and then again at varying time points after the ink had broken down with the apoptotic tumor cells. Ex vivo tumors were imaged post-mortem using hyperspectral cryo-fluorescence imaging to quantify necrosis and compared to Cherenkov-excited light imaging of diffusive ink spread measured in vivo. Imaging of untreated control mice showed that ink distributions remained constant after four days with less than 3% diffusive spread measured using full width at 20% max. For all mice, in vivo CELI measurements matched within 12% of the values estimated by the high-resolution ex vivo sliced luminescence imaging of the tumors. The tattoo ink spread in treated mice was found to correlate well with the nonperfusion necrotic core volume (R2 = 0.92) but not well with total tumor volume changes (R2 = 0.34). In vivo and ex vivo findings indicate that the diffusive spread of the injected tattoo ink can be related to radiation-induced necrosis, independent of total tumor volume change. Tracking the diffusive spread of the ink allows for distinguishing between an increase in tumor size due to new cellular growth and an increase in tumor size due to edema. Furthermore, the imaging resolution of CELI allows for in vivo tracking of subtle microenvironmental changes which occur earlier than tumor shrinkage and this offers the potential for novel, minimally invasive radiotherapy response assay without interrupting a singular clinical workflow.

Imaging response assessment following stereotactic body radiotherapy for solid tumour metastases of the spine: Current challenges and future directions

Patients with metastatic disease are routinely serially imaged to assess disease burden and response to systemic and local therapies, which places ever-expanding demands on our healthcare resources. Image interpretation following stereotactic body radiotherapy (SBRT) for spine metastases can be challenging; however, appropriate and accurate assessment is critical to ensure patients are managed correctly and resources are optimised. Here, we take a critical review of the merits and pitfalls of various imaging modalities, current response assessment guidelines, and explore novel imaging approaches and the potential for radiomics to add value in imaging assessment.

Multi-parametric and multi-regional histogram analysis of MRI: modality integration reveals imaging phenotypes of glioblastoma

OBJECTIVES

Integrating multiple imaging modalities is crucial for MRI data interpretation. The purpose of this study is to determine whether a previously proposed multi-view approach can effectively integrate the histogram features from multi-parametric MRI and whether the selected features can offer incremental prognostic values over clinical variables.

METHODS

Eighty newly-diagnosed glioblastoma patients underwent surgery and chemoradiotherapy. Histogram features of diffusion and perfusion imaging were extracted from contrast-enhancing (CE) and non-enhancing (NE) regions independently. An unsupervised patient clustering was performed by the multi-view approach. Kaplan-Meier and Cox proportional hazards regression analyses were performed to evaluate the relevance of patient clustering to survival. The metabolic signatures of patient clusters were compared using multi-voxel spectroscopy analysis. The prognostic values of histogram features were evaluated by survival and ROC curve analyses.

RESULTS

Two patient clusters were generated, consisting of 53 and 27 patients respectively. Cluster 2 demonstrated better overall survival (OS) (p = 0.007) and progression-free survival (PFS) (p < 0.001) than Cluster 1. Cluster 2 displayed lower N-acetylaspartate/creatine ratio in NE region (p = 0.040). A higher mean value of anisotropic diffusion in NE region was associated with worse OS (hazard ratio [HR] = 1.40, p = 0.020) and PFS (HR = 1.36, p = 0.031). The seven features selected by this approach showed significantly incremental value in predicting 12-month OS (p = 0.020) and PFS (p = 0.022).

CONCLUSIONS

The multi-view clustering method can provide an effective integration of multi-parametric MRI. The histogram features selected may be used as potential prognostic markers.

KEY POINTS

• Multi-parametric magnetic resonance imaging captures multi-faceted tumor physiology. • Contrast-enhancing and non-enhancing tumor regions represent different tumor components with distinct clinical relevance. • Multi-view data analysis offers a method which can effectively select and integrate multi-parametric and multi-regional imaging features.

Simultaneous positron emission tomography and magnetic resonance imaging for the detection and characterisation of liver lesions in patients with colorectal cancer: A pictorial review

Patients with colorectal cancer undergo frequent diagnostic imaging to stage the extent of metastatic disease and assess response to treatment. Imaging is typically via diagnostic contrast-enhanced CT or combined FDG-PET/CT. However, recent research has demonstrated promising benefits of combined FDG-PET/MRI in oncologic imaging due to the superior soft-tissue contrast of MRI. The extent of both intrahepatic and extrahepatic disease is important in establishing treatment options for colorectal cancer patients, and FDG-PET/CT and dedicated liver imaging are often both required. FDG-PET/MRI offers the advantage of a single examination which can be completed within a similar duration as dedicated liver MRI imaging. This improves patient convenience and anatomical co-registration between PET and MRI imaging and provides a potential cost benefit. The diagnostic benefits of FDG-PET/MRI include the simultaneous characterisation of focal liver lesions, exclusion of extrahepatic disease, the detection of additional hepatic metastases and extrahepatic disease, and the multi-parametric assessment of treatment response. This pictorial review highlights examples of these benefits.

Early detection of changes in phospholipid metabolism during neoadjuvant chemotherapy in breast cancer patients using phosphorus magnetic resonance spectroscopy at 7T

The purpose of this work was to investigate whether noninvasive early detection (after the first cycle) of response to neoadjuvant chemotherapy (NAC) in breast cancer patients was possible. ³¹ P-MRSI at 7 T was used to determine different phosphor metabolites ratios and correlate this to pathological response. ³¹ P-MRSI was performed in 12 breast cancer patients treated with NAC. ³¹ P spectra were fitted and aligned to the frequency of phosphoethanolamine (PE). Metabolic signal ratios for phosphomonoesters/phosphodiesters (PME/PDE), phosphocholine/glycerophosphatidylcholine (PC/GPtC), phosphoethanolamine/glycerophosphoethanolamine (PE/GPE) and phosphomonoesters/in-organic phosphate (PME/Pi) were determined from spectral fitting of the individual spectra and the summed spectra before and after the first cycle of NAC. Metabolic ratios were subsequently related to pathological response. Additionally, the correlation between the measured metabolic ratios and Ki-67 levels was determined using linear regression. Four patients had a pathological complete response after treatment, five patients a partial pathological response, and three patients did not respond to NAC. In the summed spectrum after the first cycle of NAC, PME/Pi and PME/PDE decreased by 18 and 13%, respectively. A subtle difference among the different response groups was observed in PME/PDE, where the nonresponders showed an increase and the partial and complete responders a decrease (P = 0.32). No significant changes in metabolic ratios were found. However, a significant association between PE/Pi and the Ki-67 index was found (P = 0.03). We demonstrated that it is possible to detect subtle changes in ³¹ P metabolites with a 7 T MR system after the first cycle of NAC treatment in breast cancer patients. Nonresponders showed different changes in metabolic ratios compared with partial and complete responders, in particular for PME/PDE; however, more patients need to be included to investigate its clinical value.

Computational modelling of resistance and associated treatment response heterogeneity in metastatic cancers

Metastatic cancer patients invariably develop treatment resistance. Different levels of resistance lead to observed heterogeneity in treatment response. The main goal was to evaluate treatment response heterogeneity with a computation model simulating the dynamics of drug-sensitive and drug-resistant cells. Model parameters included proliferation, drug-induced death, transition and proportion of intrinsically resistant cells. The model was benchmarked with imaging metrics extracted from 39 metastatic prostate cancer patients who had ¹⁸F-NaF-PET/CT scans performed at baseline and at three cycles into chemotherapy or hormonal therapy. Two initial model assumptions were evaluated: considering only inter-patient heterogeneity and both inter-patient and intra-patient heterogeneity in the proportion of intrinsically resistant cells. The correlation between the median proportion of intrinsically resistant cells and baseline patient-level imaging metrics was assessed with Spearman's rank correlation coefficient. The impact of model parameters on simulated treatment response was evaluated with a sensitivity study. Treatment response after periods of six, nine, and 12 months was predicted with the model. The median predicted range of response for patients treated with both therapies was compared with a Wilcoxon rank sum test. For each patient, the time was calculated when the proportion of disease with a non-favourable response outperformed a favourable response. By taking into account inter-patient and intra-patient heterogeneity in the proportion of intrinsically resistant cells, the model performed significantly better ([Formula: see text]) than by taking into account only inter-patient heterogeneity ([Formula: see text]). The median proportion of intrinsically resistant cells showed a moderate correlation (ρ  =  0.55) with mean patient-level uptake, and a low correlation (ρ  =  0.36) with the dispersion of mean metastasis-level uptake in a patient. The sensitivity study showed a strong impact of the proportion of intrinsically resistant cells on model behaviour after three cycles of therapy. The difference in the median range of response (MRR) was not significant between cohorts at any time point (p   >  0.15). The median time when the proportion of disease with a non-favourable response outperformed the favourable response was eight months, for both cohorts. The model provides an insight into inter-patient and intra-patient heterogeneity in the evolution of treatment resistance.

Characterizing tumor invasiveness of glioblastoma using multiparametric magnetic resonance imaging

OBJECTIVE

The objective of this study was to characterize the abnormalities revealed by diffusion tensor imaging (DTI) using MR spectroscopy (MRS) and perfusion imaging, and to evaluate the prognostic value of a proposed quantitative measure of tumor invasiveness by combining contrast-enhancing (CE) and DTI abnormalities in patients with glioblastoma.

METHODS

Eighty-four patients with glioblastoma were recruited preoperatively. DTI was decomposed into isotropic (p) and anisotropic (q) components. The relative cerebral blood volume (rCBV) was calculated from the dynamic susceptibility contrast imaging. Values of N-acetylaspartate, myoinositol, choline (Cho), lactate (Lac), and glutamate + glutamine (Glx) were measured from multivoxel MRS and normalized as ratios to creatine (Cr). Tumor regions of interest (ROIs) were manually segmented from the CE T1-weighted (CE-ROI) and DTI-q (q-ROI) maps. Perfusion and metabolic characteristics of these ROIs were measured and compared. The relative invasiveness coefficient (RIC) was calculated as a ratio of the characteristic radii of CE-ROI and q-ROI. The prognostic significance of RIC was tested using Kaplan-Meier and multivariate Cox regression analyses.

RESULTS

The Cho/Cr, Lac/Cr, and Glx/Cr in q-ROI were significantly higher than CE-ROI (p = 0.004, p = 0.005, and p = 0.007, respectively). CE-ROI had significantly higher rCBV values than q-ROI (p < 0.001). A higher RIC was associated with worse survival in a multivariate overall survival (OS) model (hazard ratio [HR] 1.40, 95% confidence interval [CI] 1.06-1.85, p = 0.016) and progression-free survival (PFS) model (HR 1.55, 95% CI 1.16-2.07, p = 0.003). An RIC cutoff value of 0.89 significantly predicted shorter OS (median 384 vs 605 days, p = 0.002) and PFS (median 244 vs 406 days, p = 0.001).

CONCLUSIONS

DTI-q abnormalities displayed higher tumor load and hypoxic signatures compared with CE abnormalities, whereas CE regions potentially represented the tumor proliferation edge. Integrating the extents of invasion visualized by DTI-q and CE images into clinical practice may lead to improved treatment efficacy.

A whole-body FDG PET/MR atlas for multiparametric voxel-based analysis

Quantitative multiparametric imaging is a potential key application for Positron Emission Tomography/Magnetic Resonance (PET/MR) hybrid imaging. To enable objective and automatic voxel-based multiparametric analysis in whole-body applications, the purpose of this study was to develop a multimodality whole-body atlas of functional 18F-fluorodeoxyglucose (FDG) PET and anatomical fat-water MR data of adults. Image registration was used to transform PET/MR images of healthy control subjects into male and female reference spaces, producing a fat-water MR, local tissue volume and FDG PET whole-body normal atlas consisting of 12 male (66.6 ± 6.3 years) and 15 female (69.5 ± 3.6 years) subjects. Manual segmentations of tissues and organs in the male and female reference spaces confirmed that the atlas contained adequate physiological and anatomical values. The atlas was applied in two anomaly detection tasks as proof of concept. The first task automatically detected anomalies in two subjects with suspected malignant disease using FDG data. The second task successfully detected abnormal liver fat infiltration in one subject using fat fraction data.

Bolus arrival time estimation in dynamic contrast-enhanced magnetic resonance imaging of small animals based on spline models

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is used to quantify perfusion and vascular permeability. In most cases a bolus arrival time (BAT) delay exists between the arterial input function (AIF) and the contrast agent arrival in the tissue of interest which needs to be estimated. Existing methods for BAT estimation are tailored to tissue concentration curves, which have a fast upslope to the peak as frequently observed in patient data. However, they may give poor results for curves that do not have this characteristic shape such as tissue concentration curves of small animals. In this paper, we propose a method for BAT estimation of signals that do not have a fast upslope to their peak. The model is based on splines which are able to adapt to a large variety of concentration curves. Furthermore, the method estimates BATs on a continuous time scale. All relevant model parameters are automatically determined by generalized cross validation. We use simulated concentration curves of small animal and patient settings to assess the accuracy and robustness of our approach. The proposed method outperforms a state-of-the-art method for small animal data and it gives competitive results for patient data. Finally, it is tested on in vivo acquired rat data where accuracy of BAT estimation was also improved upon the state-of-the-art method. The results indicate that the proposed method is suitable for accurate BAT estimation of DCE-MRI data, especially for small animals.

Pathophysiological mapping of tumor habitats in the breast in DCE-MRI using molecular texture descriptor

BACKGROUND

We propose a computational methodology capable of detecting and analyzing breast tumor habitats in images acquired by magnetic resonance imaging with dynamic contrast enhancement (DCE-MRI), based on the pathophysiological behavior of the contrast agent (CA).

METHODS

The proposed methodology comprises three steps. In summary, the first step is the acquisition of images from the Quantitative Imaging Network Breast. In the second step, the segmentation of the breasts is performed to remove the background, noise, and other unwanted objects from the image. In the third step, the generation of habitats is performed by applying two techniques: the molecular texture descriptor (MTD) that highlights the CA regions in the breast, and pathophysiological texture mapping (MPT), which generates tumor habitats based on the behavior of the CA. The combined use of these two techniques allows the automatic detection of tumors in the breast and analysis of each separate habitat with respect to their malignancy type.

RESULTS

The results found in this study were promising, with 100% of breast tumors being identified. The segmentation results exhibited an accuracy of 99.95%, sensitivity of 71.07%, specificity of 99.98%, and volumetric similarity of 77.75%. Moreover, we were able to classify the malignancy of the tumors, with 6 classified as malignant type III (WashOut) and 14 as malignant type II (Plateau), for a total of 20 cases.

CONCLUSION

We proposed a method for the automatic detection of tumors in the breast in DCE-MRI and performed the pathophysiological mapping of tumor habitats by analyzing the behavior of the CA, combining MTD and MPT, which allowed the mapping of internal tumor habitats.

Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer

The volume transfer constant K^trans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported K^trans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. K^trans , k_ep and v_e ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T₁ -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. K^trans , k_ep and v_e were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in K^trans , k_ep or v_e for the studied injection durations. The study demonstrates that the TKA parameters K^trans , k_ep and v_e , measured in the prostate, do not show a significant change as a function of injection duration.

A novel approach to monitoring the efficacy of anti-tumor treatments in animal models: combining functional MRI and texture analysis

Background

The aim of this study was to evaluate the early anti-tumor efficiency of different therapeutic agents with a combination of multi-b-value DWI, DCE-MRI and texture analysis.

Methods

Eighteen 4 T1 homograft tumor models were divided into control, paclitaxel monotherapy and paclitaxel and bevacizumab combination therapy groups (n = 6) that underwent multi-b-value DWI, DCE-MRI and texture analysis before and 15 days after treatment.

Results

After treatment, the tumors in the control group were significantly larger than those in the combination group (P = 0.018). In multi-b-value DWI, the ADC_slow obviously increased in the combination group compared to that in the others (P < 0.01). The f increased in the control and paclitaxel groups, but the combination group showed a significant decrease versus the others (P < 0.02). Additionally, in DCE-MRI, the decreasing K^trans showed an evident difference between the combination and control groups (P = 0.003) due to the latter’s increasing K^trans. The intra-group comparisons of tumor texture in pre-, mid- and post-treatments showed that the entropy had all significantly increased in all groups (P < 0.01, SSF = 0–6), though the MPP, mean and SD increased only in the combination group (P_MPP,mean,SD < 0.05, SSF = 4–6). Moreover, the inter-group comparisons revealed that the mean and MPP exhibited significant differences after treatment (P_mean,MPP < 0.05, SSF = 0–3).

Conclusion

All these results suggest some strong correlations among DWI, DCE and texture analysis, which are beneficial for further study and clinical research.

Amide chemical exchange saturation transfer at 7 T: a possible biomarker for detecting early response to neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

The purpose of this work was to investigate noninvasive early detection of treatment response of breast cancer patients to neoadjuvant chemotherapy (NAC) using chemical exchange saturation transfer (CEST) measurements sensitive to amide proton transfer (APT) at 7 T.

METHODS

CEST images were acquired in 10 tumors of nine breast cancer patients treated with NAC. APT signals in the tumor, before and after the first cycle of NAC, were quantified using a three-pool Lorentzian fit of the z-spectra in the region of interest. The changes in APT were subsequently related to pathological response after surgery defined by the Miller-Payne system.

RESULTS

Significant differences (P <  0.05, unpaired Mann-Whitney test) were found in the APT signal before and after the first cycle of NAC in six out of 10 lesions, of which two showed a pathological complete response. Of the remaining four lesions, one showed a pathological complete response. No significant difference in changes of APT signal were found between the different pathological responses to NAC treatment (P > 0.05, Kruskal-Wallis test).

CONCLUSIONS

This preliminary study shows the feasibility of using APT CEST magnetic resonance imaging as a noninvasive biomarker to assess the effect of NAC in an early stage of NAC treatment of breast cancer patients.

TRIAL REGISTRATION

Registration number, NL49333.041.14/ NTR4980 . Registered on 16 October 2014.

Imaging biomarkers from multiparametric magnetic resonance imaging are associated with survival outcomes in patients with brain metastases from breast cancer

OBJECTIVES

The aim of this study is to investigate the correlation of survival outcomes with imaging biomarkers from multiparametric magnetic resonance imaging (MRI) in patients with brain metastases from breast cancer (BMBC).

METHODS

This study was approved by the institutional review board. Twenty-two patients with BMBC who underwent treatment involving bevacizumab on day 1, etoposide on days 2-4, and cisplatin on day 2 in 21-day cycles were prospectively enrolled for a phase II study. Three brain MRIs were performed: before the treatment, on day 1, and on day 21. Eight imaging biomarkers were derived from dynamic contrast-enhanced MRI (Peak, IAUC₆₀, K^trans, k_ep, v_e), diffusion-weighted imaging [apparent diffusion coefficient (ADC)], and MR spectroscopy (choline/N-acetylaspartate and choline/creatine ratios). The relative changes (Δ) in these biomarkers were correlated with the central nervous system (CNS)-specific progression-free survival (PFS) and overall survival (OS) using the Kaplan-Meier and Cox proportional hazard models.

RESULTS

There were no significant differences in the survival outcomes as per the changes in the biomarkers on day 1. On day 21, those with a low ΔK^trans (p = 0.024) or ΔADC (p = 0.053) reduction had shorter CNS-specific PFS; further, those with a low ΔPeak (p = 0.012) or ΔIAUC₆₀ (p = 0.04) reduction had shorter OS compared with those with high reductions. In multivariate analyses, ΔK^trans and ΔPeak were independent prognostic factors for CNS-specific PFS and OS, respectively, after controlling for age, size, hormone receptors, and performance status.

CONCLUSIONS

Multiparametric MRI may help predict the survival outcomes in patients with BMBC.

KEY POINTS

• Decreased angiogenesis after chemotherapy on day 21 indicated good survival outcome. • ΔK ^trans was an independent prognostic factors for CNS-specific PFS. • ΔPeak was an independent prognostic factors for OS. • Multiparametric MRI helps clinicians to assess patients with BMBC. • High-risk patients may benefit from more intensive follow-up or treatment strategies.

Advanced Imaging Techniques in Evaluation of Colorectal Cancer

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ^©RSNA, 2018.

Quantitative contrast-enhanced ultrasound evaluation of pathological complete response in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy

PURPOSE

To clarify whether the quantitative parameters of contrast-enhanced ultrasound (CEUS) can be used to predict pathological complete response (pCR) in patients with locally advanced breast cancer receiving neoadjuvant chemotherapy (NAC).

MATERIAL AND METHODS

Fifty-one patients with histologically proved locally advanced breast cancer scheduled for NAC were enrolled. The quantitative data for CEUS and the tumor diameter were collected at baseline and before surgery, and compared with the pathological response. Multiple logistic regression analysis was performed to examine quantitative parameters at CEUS and the tumor diameter to predict the pCR, and receiver operating characteristic (ROC) curve analysis was used as a summary statistic.

RESULTS

Multiple logistic regression analysis revealed that PEAK (the maximum intensity of the time-intensity curve during bolus transit), PEAK%, TTP% (time to peak), and diameter% were significant independent predictors of pCR, and the area under the ROC curve was 0.932(Az₁), and the sensitivity and specificity to predict pCR were 93.7% and 80.0%. The area under the ROC curve for the quantitative parameters was 0.927(Az₂), and the sensitivity and specificity to predict pCR were 81.2% and 94.3%. For diameter%, the area under the ROC curve was 0.786 (Az₃), and the sensitivity and specificity to predict pCR were 93.8% and 54.3%. The values of Az₁ and Az₂ were significantly higher than that of Az₃ (P = 0.027 and P = 0.034, respectively). However, there was no significant difference between the values of Az₁ and Az₂ (P = 0.825).

CONCLUSION

Quantitative analysis of tumor blood perfusion with CEUS is superior to diameter% to predict pCR, and can be used as a functional technique to evaluate tumor response to NAC.

Correlation Between Tumor Metabolism and Semiquantitative Perfusion Magnetic Resonance Imaging Metrics in Non-Small Cell Lung Cancer

PURPOSE

To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ¹⁸F-fluorodeoxyglucose positron emission tomography (¹⁸F-FDG-PET) for non-small cell lung cancer (NSCLC).

METHODS AND MATERIALS

Twenty-four NSCLC patients who underwent pretreatment ¹⁸F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhanced MRI was obtained on a 3T MRI scanner using 4-dimensional T1-weighted high-resolution imaging with a volume excitation sequence. The DCE-MRI parameters, consisting of mean, median, standard deviation (SD), and median absolute deviation (MAD) of peak enhancement, time to peak (TTP), time to half peak (TTHP), wash-in slope (WIS), wash-out slope (WOS), initial gradient, wash-out gradient, signal enhancement ratio, and initial area under the relative signal enhancement curve taken up to 30, 60, 90, 120, 150, and 180 seconds, TTP, and TTHP (IAUCtthp), were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator in combination with leave-one-out cross-validation (LOOCV).

RESULTS

In UVA, mean(WOS) (ρ = -0.456, P = .025), mean(IAUCtthp) (ρ = -0.439, P = .032), median(IAUCtthp) (ρ = -0.543, P = .006), and MAD(IAUCtthp) (ρ = -0.557, P = .005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R² = 0.298, P = .0006). A decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp) and an increase in SD(TTP) were associated with a significant increase in SUVmax.

CONCLUSION

An association was found between SUVmax, the SD, and MAD of DCE-MRI metrics derived during contrast uptake in NSCLC, reflecting that intratumoral heterogeneity in wash-in contrast kinetics is associated with tumor metabolism. Although MAD(IAUCtthp) was a significant feature in both UVA and MVA, the LASSO-based multivariate regression model yielded better predictability of SUVmax than a univariate regression model using MAD(IAUCtthp). This study will facilitate understanding of the complex relationship between tumor vascularization and metabolism and eventually help in guiding targeted therapy.

Improving immune-vascular crosstalk for cancer immunotherapy

The vasculature of tumours is highly abnormal and dysfunctional. Consequently, immune effector cells have an impaired ability to penetrate solid tumours and often exhibit compromised functions. Normalization of the tumour vasculature can enhance tissue perfusion and improve immune effector cell infiltration, leading to immunotherapy potentiation. However, recent studies have demonstrated that the stimulation of immune cell functions can also help to normalize tumour vessels. In this Opinion article, we propose that the reciprocal regulation between tumour vascular normalization and immune reprogramming forms a reinforcing loop that reconditions the tumour immune microenvironment to induce durable antitumour immunity. A deeper understanding of these pathways could pave the way for identifying new biomarkers and developing more effective combination treatment strategies for patients with cancer.

A Novel Unsupervised Segmentation Approach Quantifies Tumor Tissue Populations Using Multiparametric MRI: First Results with Histological Validation

Purpose

We aimed to precisely estimate intra-tumoral heterogeneity using spatially regularized spectral clustering (SRSC) on multiparametric MRI data and compare the efficacy of SRSC with the previously reported segmentation techniques in MRI studies.

Procedures

Six NMRI nu/nu mice bearing subcutaneous human glioblastoma U87 MG tumors were scanned using a dedicated small animal 7T magnetic resonance imaging (MRI) scanner. The data consisted of T2 weighted images, apparent diffusion coefficient maps, and pre- and post-contrast T2 and T2* maps. Following each scan, the tumors were excised into 2–3-mm thin slices parallel to the axial field of view and processed for histological staining. The MRI data were segmented using SRSC, K-means, fuzzy C-means, and Gaussian mixture modeling to estimate the fractional population of necrotic, peri-necrotic, and viable regions and validated with the fractional population obtained from histology.

Results

While the aforementioned methods overestimated peri-necrotic and underestimated viable fractions, SRSC accurately predicted the fractional population of all three tumor tissue types and exhibited strong correlations (r_necrotic = 0.92, r_{peri-necrotic} = 0.82 and r_viable = 0.98) with the histology.

Conclusions

The precise identification of necrotic, peri-necrotic and viable areas using SRSC may greatly assist in cancer treatment planning and add a new dimension to MRI-guided tumor biopsy procedures.

Electronic supplementary material

The online version of this article (doi:10.1007/s11307-016-1009-y) contains supplementary material, which is available to authorized users.

Multiparametric PET/MR imaging biomarkers are associated with overall survival in patients with pancreatic cancer

PURPOSE

To correlate the overall survival (OS) with the imaging biomarkers of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy, and glucose metabolic activity derived from integrated fluorine 18 fluorodeoxyglucose positron emission tomography (¹⁸F-FDG PET)/MRI in patients with pancreatic cancer.

METHODS

This prospective study was approved by the institutional review board and informed consent was obtained from all participants. Sixty-three consecutive patients (mean age, 62.7 ± 12 y; men/women, 40/23) with pancreatic cancer underwent PET/MRI before treatment. The imaging biomarkers were comprised of DCE-MRI parameters (peak, IAUC ₆₀ , K ^trans , k _ep , v _e ), the minimum apparent diffusion coefficient (ADC_min), choline level, standardized uptake values, metabolic tumor volume, and total lesion glycolysis (TLG) of the tumors. The relationships between these imaging biomarkers with OS were evaluated with the Kaplan-Meier and Cox proportional hazard models.

RESULTS

Seventeen (27%) patients received curative surgery, with the median follow-up duration being 638 days. Univariate analysis showed that patients at a low TNM stage (≦3, P = 0.041), high peak (P = 0.006), high ADC_min (P = 0.002) and low TLG (P = 0.01) had better OS. Moreover, high TLG/peak ratio was associated with poor OS (P = 0.016). Multivariate analysis indicated that ADC_min (P = 0.011) and TLG/peak ratio (P = 0.006) were independent predictors of OS after adjustment for age, gender, tumor size, and TNM stage. The TLG/peak ratio was an independent predictor of OS in a subgroup of patients who did not receive curative surgery (P = 0.013).

CONCLUSION

The flow-metabolism mismatch reflected by the TLG/peak ratio may better predict OS than other imaging biomarkers from PET/MRI in pancreatic cancer patients.

Monitoring tumor response to the vascular disrupting agent CKD-516 in a rabbit VX2 intramuscular tumor model using PET/MRI: Simultaneous evaluation of vascular and metabolic parameters

OBJECTIVES

To determine whether the CKD-516 produces a significant change in vascular and metabolic parameters in PET/MRI.

MATERIALS AND METHODS

With institutional Animal Care and Use Committee approval, 18 VX2 carcinoma tumors implanted in bilateral back muscles of 9 rabbits were evaluated. Serial PET/MRI were performed before, 4 hours after and 1-week after vascular disrupting agent, CKD-516 at a dose of 0.7 mg/kg (treated group, n = 10) or saline (control group, n = 8) administration. PET/MRI-derived parameters and their interval changes were compared between the treated and control group by using the linear mixed model. Each parameter within each group was also compared by using the linear mixed model.

RESULTS

Changes of the volume transfer coefficient (Ktrans) and the initial area under the gadolinium concentration-time curve until 60 seconds (iAUC) in the treated group were significantly larger compared with those in the control group at 4-hour follow-up (mean, -39.91% vs. -6.04%, P = 0.018; and -49.71% vs. +6.23%, P = 0.013). Change of metabolic tumor volume (MTV) in the treated group was significantly smaller compared with that in the control group at 1-week follow-up (mean, +118.34% vs. +208.87%, P = 0.044). Serial measurements in the treated group revealed that Ktrans and iAUC decreased at 4-hour follow-up (P < 0.001) and partially recovered at 1-week follow-up (P = 0.001 and 0.024, respectively). MTV increased at a 4-hour follow-up (P = 0.038) and further increased at a 1-week follow-up (P < 0.001), while total lesion glycolysis (TLG) did not show a significant difference between the time points. SUVmax and SUVmean did not show significant interval changes between time points (P > 0.05).

CONCLUSIONS

PET/MRI is able to monitor the changes of vascular and metabolic parameters at different time points simultaneously, and confirmed that vascular changes precede the metabolic changes by VDA, CKD-516.