Tong San Koh's research while affiliated with National Cancer Centre Singapore and other places

Objective: The variable flip angle (VFA) method for longitudinal relaxation time (T1) measurement is inherently sensitive to inaccuracies in the radiofrequency transmit field (B1) and incomplete spoiling of transverse magnetization. The objective of this study is to devise a computational method that addresses the problems of incomplete spoiling and B1 inhomogeneity in the estimation of T1 using VFA method. Approach: Using an analytical expression of the gradient echo signal with account of incomplete spoiling, we first showed that ill-posedness in the simultaneous estimation of B1 and T1 can be lifted with the use of flip angles larger than the Ernst angle. We then devised a nonlinear optimization method based on this signal model of incomplete spoiling for simultaneous estimation of B1 and T1. Main results: We evaluated the proposed method on a graded-concentration phantom to show that the derived T1 estimates offers an improvement over the regular VFA method and compares well with reference values measured by inversion recovery. Reduction of the number of flip angles from 17 to 5 yielded consistent results indicating that the proposed method is numerically stable. T1 estimates derived from in-vivo brain imaging were consistent with literature values for gray and white matter tissues. Significance: Contrary to the common notion that B1 correction in the VFA method for T1 mapping should be performed separately, we show that combined estimation of B1 and T1 is feasible by the proposed method simply with the acquisition of 5 flip angles, as demonstrated on both phantom and in-vivo imaging data.

Background: To compare two tracer kinetic models in predicting of preoperative risk types in endometrial carcinoma (EC) using DCE-MRI. Methods: A prospective study of patients with EC was conducted with institutional ethics approval and written informed consent. DCE-MRI data was analyzed using the extended Tofts (ET) and the distributed parameter (DP) models. DCE parameters blood flow (F), mean transit time, blood volume (Vp), extravascular extracellular volume (Ve), permeability surface area product (PS), extraction fraction, transfer constant (Ktrans), and efflux rate (Kep) between high- and low-risk EC were compared using the Mann-Whitney test. Bland-Altman analysis was utilized to compare parameter consistency and Spearman test to assess parameter correlation. Diagnostic performance of DCE parameters was analyzed by receiver-operating characteristic curve and compared with traditional MRI assessment. Results: Fifty-one patients comprised the study group. Patients with high-risk EC exhibited significantly lower Ktrans, Kep, F, Vp and PS (P < 0.001). ET-derived Ktrans and DP-derived F attained AUC of 0.92 and 0.91, respectively. Bland-Altman analysis showed that the consistency of Ve or Vp between the two models was low (P < 0.001) while Spearman test showed a strong correlation (r = 0.719, 0.871). Both Ktrans and F showed higher accuracy in predicting EC risk types than traditional MRI assessment. Conclusions: Kinetic parameters derived from DCE-MRI revealed a more hypovascular microenvironment for high risk EC than to low- risk ones, providing potential imaging biomarkers in preoperative risk assessment that might improve individualized surgical planning and management of EC.

Background To compare different fitting methods for determining IVIM (Intravoxel Incoherent Motion) parameters and to determine whether the use of different IVIM fitting methods would affect differentiation of cervix cancer from normal cervix tissue. Methods Diffusion-weighted echo-planar imaging of 30 subjects was performed on a 3.0 T scanner with b -values of 0, 30, 100, 200, 400, 1000 s/mm ² . IVIM parameters were estimated using the segmented (two-step) fitting method and by simultaneous fitting of a bi-exponential function. Segmented fitting was performed using two different cut-off b -values (100 and 200 s/mm ² ) to study possible variations due to the choice of cut-off. Friedman’s test and Student’s t-test were respectively used to compare IVIM parameters derived from different methods, and between cancer and normal tissues. Results No significant difference was found between IVIM parameters derived from the segmented method with b -value cutoff of 200 s/mm ² and the simultaneous fitting method ( P >0.05). Tissue diffusivity ( D ) and perfusion fraction ( f ) were significantly lower in cervix cancer than normal tissue ( P < 0.05). Conclusions IVIM parameters derived using fitting methods with small cutoff b -values could be different, however, the segmented method with b -value cutoff of 200 s/mm ² are consistent with the simultaneous fitting method and both can be used to differentiate between cervix cancer and normal tissue.

Objective To investigate the utility of quantitative susceptibility mapping (QSM) and diffusion kurtosis imaging (DKI) as complementary tools in characterizing pathological changes in the deep grey nuclei in early Parkinson’s disease (PD) and their clinical correlates to aid in diagnosis of PD. Method Patients with a diagnosis of PD made within a year and age-matched healthy controls were recruited. All participants underwent clinical evaluation using the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS-3) and Hoehn & Yahr stage (H&Y), and brain 3T MRI including QSM and DKI. Regions-of-interest (ROIs) in the caudate nucleus, putamen, globus pallidus, and medial and lateral substantia nigra (SN) were manually drawn to compare the mean susceptibility (representing iron deposition) and DKI indices (representing restricted water diffusion) between PD patients and healthy controls and in correlation with MDS-UPDRS-3 and H&Y, focusing on iron deposition, mean diffusivity (MD) and mean kurtosis (MK). Results There were forty-seven PD patients (aged 68.7 years, 51% male, disease duration 0.78 years) and 16 healthy controls (aged 67.4 years, 63% male). Iron deposition was increased in PD in all ROIs except the caudate, and was significantly different after multiple comparison correction in the putamen (PD: 64.75 ppb, HC: 44.61 ppb, p=0.004). MD was significantly higher in PD in the lateral SN, putamen and caudate, the regions with the lowest iron deposition. In PD patients, we found significant association between the MDS-UPDRS-3 score and iron deposition in the putamen after correcting for age and sex (β=0.21, p=0.003). A composite DKI-QSM diagnostic marker based on the findings successfully differentiated the groups (p<0.0001) and had “good” classification performance (AUC=0.88). Conclusions QSM and DKI are complementary tools allowing a better understanding of the complex interplay of iron deposition and microstructural changes in the pathophysiology of PD.

Previous studies using contrast-enhanced imaging for glioma isocitrate dehydrogenase (IDH) mutation assessment showed promising yet inconsistent results, and this study attempts to explore this problem by using an advanced tracer kinetic model, the distributed parameter model (DP). Fifty-five patients with glioma examined using dynamic contrast-enhanced imaging sequence at a 3.0 T scanner were retrospectively reviewed. The imaging data were processed using DP, yielding the following parameters: blood flow F, permeability-surface area product PS, fractional volume of interstitial space Ve, fractional volume of intravascular space Vp, and extraction ratio E. The results were compared with the Tofts model. The Wilcoxon test and boxplot were utilized for assessment of differences of model parameters between IDH-mutant and IDH-wildtype gliomas. Spearman correlation r was employed to investigate the relationship between DP and Tofts parameters. Diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis and quantified using the area under the ROC curve (AUC). Results showed that IDH-mutant gliomas were significantly lower in F ( = 0.018), PS (), Vp (), E (), and Ve ( = 0.002) than IDH-wildtype gliomas. In differentiating IDH-mutant and IDH-wildtype gliomas, Vp had the best performance (AUC = 0.92), and the AUCs of PS and E were 0.82 and 0.80, respectively. In comparison, Tofts parameters were lower in Ktrans ( = 0.013) and Ve () for IDH-mutant gliomas. No significant difference was observed in Kep ( = 0.525). The AUCs of Ktrans, Ve, and Kep were 0.69, 0.79, and 0.55, respectively. Tofts-derived Ve showed a strong correlation with DP-derived Ve (r > 0.9, ). Ktrans showed a weak correlation with F (r < 0.3, > 0.16) and a very weak correlation with PS (r < 0.06, > 0.8), both of which were not statistically significant. The findings by DP revealed a tissue environment with lower vascularity, lower vessel permeability, and lower blood flow in IDH-mutant than in IDH-wildtype gliomas, being hostile to cellular differentiation of oncogenic effects in IDH-mutated gliomas, which might help to explain the better outcomes in IDH-mutated glioma patients than in glioma patients of IDH-wildtype. The advantage of DP over Tofts in glioma DCE data analysis was demonstrated in terms of clearer elucidation of tissue microenvironment and better performance in IDH mutation assessment. 1. Introduction As the most common primary tumor in the brain, diffuse glioma arises from the glial cells which provide support functions to neurons and presents with high morbidity and variable outcomes [1]. The 2016 World Health Organization (WHO) classification of tumors of the central nervous system included well-established molecular signatures, such as isocitrate dehydrogenase (IDH) mutation status, expression of the transcription regulator ATRX, and 1p/19q codeletion status [2], where IDH is a small molecule protein involved in a number of cellular processes, including mitochondrial oxidative phosphorylation, glutamine metabolism, lipogenesis, glucose sensing, and regulation of cellular redox status [3–5]. IDH gene mutation testing is an important prognostic biomarker in gliomas and is relevant for glioma patient management and glioma stratification [6, 7]. Previous studies showed that gene expression can significantly affect the disease course, and gliomas of IDH-wildtype appear to rapidly acquire multiple complex genetic alterations and become glioblastomas very early in their development, and glioma patients with mutant IDH had significantly longer overall survival than patients without IDH mutation [6–10]. The prognostic importance of IDH mutation is independent of other known prognostic factors, including age, grade, and MGMT methylation status [6]. Hence, IDH mutations could serve as an ideal target of therapy, and imaging parameters are highly potential to capture the biologic complexity underlying molecular phenotypes in gliomas. However, conventional methods for assessment of IDH mutation status are through stereotactic biopsy invasive and prone to sampling error [11, 12]. Recently, noninvasive detection of IDH mutation status using functional imaging methods has received increasing attention [13–25]. In addition to tissue anatomic structure, functional imaging measures tissue microenvironment and provides in vivo physiologic information about brain tumors. An important functional imaging method is contrast-enhanced magnetic resonance imaging (MRI), which includes T1-weighted dynamic contrast-enhanced imaging (DCE) and T2-weighted dynamic susceptibility contrast-enhanced imaging (DSC), both of which have been applied to IDH mutation assessment in gliomas [20–25]. The tissue microenvironment of frequent study includes tumor vascularity and vessel permeability. The former is modeled as cerebral blood volume (CBV) in DSC and plasma fractional volume (Vp) in DCE. The value of CBV is often normalized with respect to a reference tissue, as denoted by relative CBV (rCBV) or normalized CBV (nCBV). Among existing studies using DSC or DCE for IDH mutation status assessment, discrepancies between different studies were evident. A significantly higher rCBV in IDH-wildtype compared with IDH-mutant type gliomas in all histological grades was reported in [20], whereas rCBV between IDH-wildtype and IDH-mutant gliomas did not differ significantly in histological subtypes of astrocytomas and oligodendrogliomas in [21]. Tissue vascularity was found to be significantly higher in IDH-wildtype gliomas than in IDH-mutant gliomas using DSC in [23]. However, tissue microenvironment parameters showed no correlates with glioma IDH mutation status using DCE in [24, 25] or DSC in [25]. The apparent conflicting results could be due to difference in imaging protocol, patient cohort, or tracer kinetic models for analyzing the acquired contrast-enhanced imaging data. Existing studies mostly utilized conventional tracer kinetic models such as the Tofts or extended Tofts model [26, 27], which does not differentiate the intravascular transport of tracer molecular with respect to the exchange process of tracer molecular between intravascular and interstitial spaces. There has been progress in the development of more advanced techniques in analyzing DCE data, such as the conventional compartment model (CC) [28], the adiabatic approximated tissue homogeneity model (ATH) [29], and the distributed parameter model (DP) [30]. The aforementioned two transports were separately accounted in these models, where blood flow (F) is utilized to characterize the intravascular transport and permeability-surface area product (PS) to describe the exchange between intravascular and interstitial spaces. In comparison, these two transports are modeled using one parameter, transfer constant (Ktrans), in the Tofts or extended Tofts model. Interested readers could refer to [31, 32] for a review on the topic. So far, few studies have been carried out on the investigation of these advanced tracer kinetic models in glioma molecular subtype characterization. Because advanced tracer kinetic models provide more realistic description of tracer transport in tissue microenvironment, it is expected that the derived parameters could be more interpretable with respect to tumor tissue microenvironment. This study hypothesizes that IDH mutations reduce the enzymatic activity of the encoded protein [7], leading to change in tissue microenvironment, and parameters derived using advanced tracer kinetic models would be more closely associated with glioma molecular signatures. Using DP as example, this study attempts to explore its application to glioma IDH mutation differentiation. 2. Materials and Methods 2.1. Subjects This retrospective study was approved by the institutional review board. Sixty-one patients were included in this study between August 2017 and September 2019. All patients diagnosed with gliomas of grade II–IV according to the 2016 WHO guideline on brain tumor classification after craniotomy and tumor resection. Patients in the study did not have a history of previous surgery for brain tumor. Six patients were excluded due to inadequate MRI quality. A total of 55 patients (23 men, 32 women; age range, 25–72 years; mean age, 46.45 ± 10.23 years) were included in the study. There were 7 oligodendrogliomas (WHO grade II), 11 astrocytomas (WHO grade II), 2 anaplastic oligodendrogliomas (WHO grade III), 8 anaplastic astrocytomas (WHO grade III), and 27 glioblastomas (WHO grade IV). Molecular pathological findings of IDH were determined by Sanger sequencing for IDH hotspot mutations. There were 24 patients with IDH mutation. A representative case is given in Figure 1. (a)

Purpose: Overexpression of fibroblast growth factor receptor (FGFR) contributes to tumorigenesis, metastasis, and poor prognosis of hepatocellular carcinoma (HCC). Infigratinib-a pan-FGFR inhibitor-potently suppresses the growth of high-FGFR-expressing HCCs in part via alteration of the tumor microenvironment and vessel normalization. In this study, we aim to assess the utility of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) as a non-invasive imaging technique to detect microenvironment changes associated with infigratinib and sorafenib treatment in high-FGFR-expressing HCC xenografts. Procedures: Serial DCE-MRIs were performed on 12 nude mice bearing high-FGFR-expressing patient-derived HCC xenografts to quantify tumor microenvironment pre- (day 0) and post-treatment (days 3, 6, 9, and 15) of vehicle, sorafenib, and infigratinib. DCE-MRI data were analyzed using extended generalized kinetic model and two-compartment distributed parameter model. After treatment, immunohistochemistry stains were performed on the harvested tumors to confirm DCE-MRI findings. Results: By treatment day 15, infigratinib induced tumor regression (70 % volume reduction from baseline) while sorafenib induced relative growth arrest (185 % volume increase from baseline versus 694 % volume increase from baseline of control). DCE-MRI analysis revealed different changes in microcirculatory parameters upon exposure to sorafenib versus infigratinib. While sorafenib induced microenvironment changes similar to those of rapidly growing tumors, such as a decrease in blood flow (F), fractional intravascular volume (vp), and permeability surface area product (PS), infigratinib induced the exact opposite changes as early as day 3 after treatment: increase in F, vp, and PS. Conclusions: Our study demonstrated that DCE-MRI is a reliable non-invasive imaging technique to monitor tumor microcirculatory response to FGFR inhibition and VEGF inhibition in high-FGFR-expressing HCC xenografts. Furthermore, the microcirculatory changes from FGFR inhibition manifested early upon treatment initiation and were reliably detected by DCE-MRI, creating possibilities of combinatorial therapy for synergistic effect.

A variety of tracer kinetic methods have been employed to assess tumor angiogenesis. The Standard two-Compartment model (SC) used in cervix carcinoma was less frequent, and Adiabatic Approximation to the Tissue Homogeneity (AATH) and Distributed Parameter (DP) model are lacking. This study compares two-compartment exchange models (2CXM) (AATH, SC, and DP) for determining dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters in cervical cancer, with the aim of investigating the potential of various parameters derived from 2CXM for tumor diagnosis and exploring the possible relationship between these parameters in patients with cervix cancer. Parameters (tissue blood flow, Fp ; tissue blood volume, Vp ; interstitial volume, Ve ; and vascular permeability, PS) for regions of interest (ROI) of cervix lesions and normal cervix tissue were estimated by AATH, SC, and DP models in 36 patients with cervix cancer and 17 healthy subjects. All parameters showed significant differences between lesions and normal tissue with a P value less than 0.05, except for PS from the AATH model, Fp from the SC model, and Vp from the DP model. Parameter Ve from the AATH model had the largest AUC ( r = 0.85). Parameters Fp and Vp from SC and DP models and Ve and PS from AATH and DP models were highly correlated, respectively, ( r > 0.8) in cervix lesions. Cervix cancer was found to have a very unusual microcirculation pattern, with over-growth of cancer cells but without evident development of angiogenesis. Ve has the best performance in identifying cervix cancer. Most physiological parameters derived from AATH, SC, and DP models are linearly correlated in cervix cancer.

Purpose To evaluate whether the analysis of high‐temporal resolution DCE‐MRI by various tracer kinetic models could yield useful radiomic features in discriminating cervix carcinoma and normal cervix tissue. Methods Forty‐three patients (median age 51 yr; range 26–78 yr) diagnosed with cervical cancer based on postoperative pathology were enrolled in this study with informed consent. DCE‐MRI data with temporal resolution of 2 s were acquired and analyzed using the Tofts (TOFTS), extended Tofts (EXTOFTS), conventional two‐compartment (CC), adiabatic tissue homogeneity (ATH), and distributed parameter (DP) models. Ability of all kinetic parameters in distinguishing tumor from normal tissue was assessed using Mann–Whitney U test and receiver operating characteristic (ROC) curves. Repeatability of parameter estimates due to sampling of arterial input functions (AIFs) was also studied using intraclass correlation (ICC) analysis. Results Fractional extravascular, extracellular volume (Ve) of all models were significantly smaller in cervix carcinoma than normal cervix tissue, and were associated with large values of area under ROC curve (AUC 0.884–0.961). Capillary permeability PS derived from the ATH, CC, and DP models also yielded large AUC values (0.730, 0.860, and 0.797). Transfer constant Ktrans derived from TOFTS and EXTOFTS models yielded smaller AUC (0.587 and 0.701). Repeatability of parameters derived from all models was robust to AIF sampling, with ICC coefficients typically larger than 0.80. Conclusions With the use of high‐temporal resolution DCE‐MRI, all tracer kinetic models could reflect pathophysiological differences between cervix carcinoma and normal tissue (with significant differences in Ve and PS) and potentially yield radiomic features with diagnostic value.

Purpose: To evaluate whether the analysis of high-temporal resolution DCE-MRI by various tracer kinetic models could yield useful radiomic features in discriminating cervix carcinoma and normal cervix tissue. Methods: Forty-three patients (median age 51 yr; range 26-78 yr) diagnosed with cervical cancer based on postoperative pathology were enrolled in this study with informed consent. DCE-MRI data with temporal resolution of 2 s were acquired and analyzed using the Tofts (TOFTS), extended Tofts (EXTOFTS), conventional two-compartment (CC), adiabatic tissue homogeneity (ATH), and distributed parameter (DP) models. Ability of all kinetic parameters in distinguishing tumor from normal tissue was assessed using Mann-Whitney U test and receiver operating characteristic (ROC) curves. Repeatability of parameter estimates due to sampling of arterial input functions (AIFs) was also studied using intraclass correlation (ICC) analysis. Results: Fractional extravascular, extracellular volume (Ve) of all models were significantly smaller in cervix carcinoma than normal cervix tissue, and were associated with large values of area under ROC curve (AUC 0.884-0.961). Capillary permeability PS derived from the ATH, CC, and DP models also yielded large AUC values (0.730, 0.860, and 0.797). Transfer constant Ktrans derived from TOFTS and EXTOFTS models yielded smaller AUC (0.587 and 0.701). Repeatability of parameters derived from all models was robust to AIF sampling, with ICC coefficients typically larger than 0.80. Conclusions: With the use of high-temporal resolution DCE-MRI, all tracer kinetic models could reflect pathophysiological differences between cervix carcinoma and normal tissue (with significant differences in Ve and PS) and potentially yield radiomic features with diagnostic value.