Peritumoural Region Research Articles

Spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive breast cancer

Deregulation of lipid composition in adipose tissue adjacent to breast tumour is observed in ex vivo and animal models. Novel non-invasive magnetic resonance imaging (MRI) allows rapid lipid mapping of the human whole breast. We set out to elucidate the spatial heterogeneity of peri-tumoural lipid composition in postmenopausal patients with oestrogen receptor positive (ER +) breast cancer. Thirteen participants (mean age, 62 ± [SD] 6 years) with ER + breast cancer and 13 age-matched postmenopausal healthy controls were scanned on MRI. The number of double bonds in triglycerides was computed from MRI images to derive lipid composition maps of monounsaturated, polyunsaturated, and saturated fatty acids (MUFA, PUFA, SFA). The spatial heterogeneity measures (mean, median, skewness, entropy and kurtosis) of lipid composition in the peri-tumoural region and the whole breast of participants and in the whole breast of controls were computed. The Ki-67 proliferative activity marker and CD163 antibody on tumour-associated macrophages were assessed histologically. Mann Whitney U or Wilcoxon tests and Spearman’s coefficients were used to assess group differences and correlations, respectively. For comparison against the whole breast in participants, peri-tumoural MUFA had a lower mean (median (IQR), 0.40 (0.02), p < .001), lower median (0.42 (0.02), p < .001), a negative skewness with lower magnitude (− 1.65 (0.77), p = .001), higher entropy (4.35 (0.64), p = .007) and lower kurtosis (5.13 (3.99), p = .001). Peri-tumoural PUFA had a lower mean (p < .001), lower median (p < .001), a positive skewness with higher magnitude (p = .005) and lower entropy (p = .002). Peri-tumoural SFA had a higher mean (p < .001), higher median (p < .001), a positive skewness with lower magnitude (p < .001) and lower entropy (p = .012). For comparison against the whole breast in controls, peri-tumoural MUFA had a negative skewness with lower magnitude (p = .01) and lower kurtosis (p = .009), however there was no difference in PUFA or SFA. CD163 moderately correlated with peri-tumoural MUFA skewness (rs = − .64), PUFA entropy (rs = .63) and SFA skewness (rs = .59). There was a lower MUFA and PUFA while a higher SFA, and a higher heterogeneity of MUFA while a lower heterogeneity of PUFA and SFA, in the peri-tumoural region in comparison with the whole breast tissue. The degree of lipid deregulation was associated with inflammation as indicated by CD163 antibody on macrophages, serving as potential marker for early diagnosis and response to therapy.

DUAL PET IMAGING OF NEOPLASTIC AND INflAMMATORY BIOMARKERS IN HIGH GRADE GLIOMA

Abstract AIMS To characterize spatial distributions of 18kDa translocator protein (TSPO) and amino acid uptake as measured through dynamic [11C] (R)-PK11195 and [11C]-methionine PET in high-grade glioma. METHOD Twelve patients with newly diagnosed high-grade glioma underwent dual PET studies. [11C] (R)-PK11195 binding potential (BPND) maps were generated using simplified reference tissue model with grey-matter cerebellar time- activity-curve as tissue input-function. [11C]-methionine uptake was calculated as tumour-to-background ratio (TBR). Volumes of interest (VOIs) were defined on T1W post-contrast MRI as contrast-enhancing (CE) tumour and a ‘peritumoural’ region 5mm from the CE edge. Voxel-wise comparison of the tracers used Pearson’s correlation coeffcient. VOIs were furthermore subdivided into higher/lower TSPO (BPND &amp;gt; 0.17) and higher/lower methionine (TBR &amp;gt; 1.7). RESULTS Within the CE, voxel-wise comparison showed a strong correlation (r &amp;gt; 0.7) in eight subjects, moderate correlation (r &amp;gt; 0.5) in three subjects and weak correlation (r &amp;lt; 0.5) in one subject. Volume (mm3) with high BPND/high TBR accounted for 62±29% whereas high BPND/low TBR accounted for 13±16%. Within the peritumoral area, there was a small reduction in the proportion of subjects in the strong correlation group (6/12) but the pro- portion of voxels with higher TSPO binding and lower methionine uptake was substantially greater (35±16%). CONCLUSIONS Within high-grade glioma, there is a strong overall correlation between TSPO expression and amino acid uptake. However, in the peritumoural region there are areas of elevated TSPO expression without increased methionine uptake which could represent an inflammatory component or a discrete neoplastic cell population that may not be adequately defined with standard imaging biomarkers.

NANO-OMICS FOR BLOOD BIOMARKER DISCOVERY IN GLIOBLASTOMA

Abstract AIMS Glioblastoma (GBM) remains one of the most aggressive cancers with an average survival of only 12-15 months. Current diagnostic methods are burdensome and ineffective, underscoring an urgent need to improve early detection and monitoring. The application of ‘liquid-biopsy’ markers holds great promises however, despite technological advances, to date there are no clinically used blood-based biomarkers for GBM. We have recently introduced the ‘Nano-Omics’ paradigm, which utilises nanoparticles (NPs) as scavenging platforms to capture and enrich disease-associated analytes from biological fluids for omics analyses. In this study, we utilised the Nano-Omics pipeline coupled with brain tissue mass spectrometry-based proteomics, to unveil GBM specific protein biomarker candidates. METHOD We exploited the spontaneous interaction of nanoparticles with blood proteins to comprehensively analyse the blood proteome of GL261 glioma-bearing C57BL/6J mice. Tumoural and peritumoural brain tissue originated from the same animals were also collected, micro-dissected and analysed by label free mass spectrometry in order to correlate the nanoparticle-enriched blood-circulating proteome with the tumour tissue proteome. RESULTS Our data revealed a number of GBM-specific biomarker candidates in blood even at low tumour burden, followed by validation in a human GBM clinical cohort. Furthermore, the peritumoural region of glioblastoma GBM displayed unique proteomic profiling, shedding light on the mechanism behind GBM infiltration and recurrence. CONCLUSIONS Our work highlights the importance of integrating blood and brain tissue proteomics and opens up new possibilities for further validation of the proteomic signatures discovered with the potential for further development of a much-needed blood test for GBM early detection and monitoring.

Correlation study between apparent diffusion coefficients and the prognostic factors in breast cancer

To analyse the correlation between apparent diffusion coefficients (ADC) derived from intratumoural and peritumoural regions with prognostic factors and immune-inflammatory markers in breast cancer (BC). In this retrospective study, 89 patients (age range, 28-66 years; median, 45 years) with a diagnosis of invasive BC who underwent routine blood tests and multiparametric magnetic resonance imaging (MRI) were enrolled. The study cohort was stratified according to tumour maximum cross-section ≥20 mm, lymph node metastasis (LNM), time-signal intensity curve (TIC) type, and receptor status. Minimum, maximum, mean, and heterogeneity values of tumour ADC (ADCtmin, ADCtmax, ADCtmean, and ADCheter), maximum values of peritumoural ADC (ADCpmax), and the ratio of peritumoural-tumour ADC (ADCratio) were obtained on the ADC maps. Linear regression analyses were performed to investigate the correlation between immune-inflammatory markers, prognostic factors and ADC values. HER-2 was positively associated with ADCtmax, ADCtmean, and ADCpmax values (β=0.306, p=0.004; β=0.283, p=0.007; β=0.262, p=0.007, respectively), while platelet-to-lymphocyte ratio (PLR) was positively associated with ADCpmax and ADCratio values (β=0.227, p=0.020; β=0.231, p=0.020, respectively). Among ADC parameters, ADCpmax showed the highest predictive values for evaluating the presence of LNM (AUC, 0.751; sensitivity, 70.4%; specificity, 77.1%). The ADCpmax value could provide additional assistance in predicting prognostic factors of BC.

Decrypting the information captured by MRI-radiomic features in predicting the response to neoadjuvant chemotherapy in breast cancer.

MRI-based radiomic models have shown promises in predicting the response to neoadjuvant chemotherapy in breast cancer. However, it is difficult to determine which information from the images contributes the most to the prediction: the distribution of gray-levels, the tumour heterogeneity, the shape of the lesions or the intensities of peritumoural regions. The purpose of this study is to dissociate the different sources of information to improve prediction results. Based on pre-treatment MR images from 103 patients, four types of 3D Volumes Of Interest were defined and arranged in multiple combinations. Combining features extracted from different regions proved to increase prediction performances. Clinical relevance- This study proposes a method based on analyses of MRI tumor heterogeneity, margins and peritumoral regions to improve the prediction of the response to neoadjuvant chemotherapy in breast cancer, which would help personalize patient treatment.

OS03.5.A Corelation between longitudinal t2 MRI radiomic primary texture feature values and radiation dose in non-tumoral regions of the brain in paediatric brain tumours

Abstract BACKGROUND Paediatric brain tumour survivors may have treatment toxicity associated with signal change on follow-up MRI. Quantitative MRI texture features can potentially be used as surrogates of the underlying tissue changes following radiation therapy. MATERIAL AND METHODS Longitudinal retrospective study in 51 paediatric primary brain tumours treated with photon (N=30) and proton (N=21) radiotherapy (RT). T2 MRI scans at baseline and multiple time point from the date of surgery to 2 years following radiotherapy were selected for the textural analysis. Scans were bias corrected, registered with the CT dose maps and with baseline scan for each patient using 3Dslicer. Regions of interest (ROI) of fixed diameter were drawn in 11 predetermined non-tumoral regions of brain including in peri-tumoural region (PTV). ROIs were placed in homogenous white/grey matter. Radiation dose was calculated in each of these 11 ROIs and texture features were extracted using pyradiomics. Data were analysed using machine learning and statistical analysis. General linear multivariate model was used to corelate primary texture features over period of 24 months and radiation dose, time, effect of dose*time together at each ROI separately. RESULTS There were Brainstem 4, Cerebellar19, Hemispheric cerebral 7 and Supratentorial midline 10 tumours. Median age at diagnosis was 8.26 years (range: 0–20). Median RT treatment dose was 28.52Gy (0-60Gy). Multivariate analysis shows significant corelation (p &amp;lt; 0.001) between radiation dose and longitudinal primary texture features in all 11ROIs. Time showed corelation with feature values only in 3 ROIs and dose* time showed corelation in 5ROIs. Primary (statistical) feature values showing consistent correlation with dose in all 11 ROIs over 24 months are total energy, 10%, 90%, energy, entropy, mean, median, and minimum. CONCLUSION Radiomic texture analysis is a promising modality to understand dose related textural changes in the normal part of brain in paediatric brain tumour patients treated with radiation therapy. Radiomic changes need to be related to neurological outcomes in future research.

Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma.

Advances in imaging technology have the potential to transform the early diagnosis and treatment of hepatocellular carcinoma (HCC) through quantitative image analysis. Computational "radiomic" techniques extract biomarker information from images which can be used to improve diagnosis and predict tumour biology. To perform a systematic review on radiomic features in HCC diagnosis and prognosis, with a focus on reporting metrics and methodologic standardisation. We performed a systematic review of all full-text articles published from inception through December 1, 2019. Standardised data extraction and quality assessment metrics were applied to all studies. A total of 54 studies were included for analysis. Radiomic features demonstrated good discriminatory performance to differentiate HCC from other solid lesions (c-statistics 0.66-0.95), and to predict microvascular invasion (c-statistic 0.76-0.92), early recurrence after hepatectomy (c-statistics 0.71-0.86), and prognosis after locoregional or systemic therapies (c-statistics 0.74-0.81). Common stratifying features for diagnostic and prognostic radiomic tools included analyses of imaging skewness, analysis of the peritumoural region, and feature extraction from the arterial imaging phase. The overall quality of the included studies was low, with common deficiencies in both internal and external validation, standardised imaging segmentation, and lack of comparison to a gold standard. Quantitative image analysis demonstrates promise as a non-invasive biomarker to improve HCC diagnosis and management. However, standardisation of protocols and outcome measurement, sharing of algorithms and analytic methods, and external validation are necessary prior to widespread application of radiomics to HCC diagnosis and prognosis in clinical practice.

Impact of axisymmetric deformation on MR elastography of a nonlinear tissue-mimicking material and implications in peri-tumour stiffness quantification.

Solid tumour growth is often associated with the accumulation of mechanical stresses acting on the surrounding host tissue. Due to tissue nonlinearity, the shear modulus of the peri-tumoural region inherits a signature from the tumour expansion which depends on multiple factors, including the soft tissue constitutive behaviour and its stress/strain state. Shear waves used in MR-elastography (MRE) sense the apparent change in shear modulus along their propagation direction, thereby probing the anisotropic stiffness field around the tumour. We developed an analytical framework for a heterogeneous shear modulus distribution using a thick-shelled sphere approximation of the tumour and soft tissue ensemble. A hyperelastic material (plastisol) was identified to validate the proposed theory in a phantom setting. A balloon-catheter connected to a pressure sensor was used to replicate the stress generated from tumour pressure and growth while MRE data were acquired. The shear modulus anisotropy retrieved from the reconstructed elastography data confirmed the analytically predicted patterns at various levels of inflation. An alternative measure, combining the generated deformation and the local wave direction and independent of the reconstruction strategy, was also proposed to correlate the analytical findings with the stretch probed by the waves. Overall, this work demonstrates that MRE in combination with non-linear mechanics, is able to identify the apparent shear modulus variation arising from the strain generated by a growth within tissue, such as an idealised model of tumour. Investigation in real tissue represents the next step to further investigate the implications of endogenous forces in tissue characterisation through MRE.

Abstract PS1-62: Two decades of experience with sentinel node staging of axilla - is false negative no longer a worry?

Abstract Aim: To review current literature and standard practices in various techniques of sentinel node biopsy for staging the axilla for treating early breast cancer. The purpose is to provide evidence based recommendation as guidance platform for optimal true sentinel node retrieval irrespective of surgeon expertise. Background: Axillary conservation is the way forward after game changing trials like ACOSOG Z0011, surrogate trials like IBCSG,AMAROS, ALMANAC, ongoing POSNOC, and newbie ATNEC have progressively or plan to decrease the need to unnecessarily fiddle with the axilla thereby increasing the chances of arm related and other morbidities. Axillary sentinel node biopsy entails retrieval of first draining lymph node in the breast-axilla pathway to plan treatment in breast cancer by appropriate staging of axilla. Current standard is the utilisation of double agent technique with radioactive isotope and blue dye injection to decrease false negative rates for true sentinel node retrieval. Novel techniques like magnetic and infrared tracing are still being investigated for validation. Method: PMC, Medline, EMBASE, PubMed and Cochrane library searched for clinical trials, randomised trials, systematic reviews and meta-analysis on techniques of axillary sentinel node biopsy in early breast cancer. This covered the last 25 years literature on the topic. Results: The search yielded 197 publications which were subjected to a meticulous review and narrowed to a select pertinent body of evidence to extrapolate suggested guidance rationally, the bibliography of which is provided at the end. Conclusion: Single agent preferably radioisotope for lymphatic mapping is recommended in palpable and good biology tumours. Use of single agent blue dye can be standardised in axillary tail tumours. It is also recommended as being effective when isotope mapping is logistically not feasible or during pandemics like COVID 19 where looming infrastructure challenges are prevalent. Dual agent technique should be considered in previously treated breast and axilla, neoadjuvant chemotherapy cohort, bad tumour biology, high BMI and macromastia groups for true nodal retrieval. Optimal number of nodes taken out should not be more than three (n=3). Lower axillary sampling of not more than 3 nodes is recommended for troubleshooting with any localising agent technique. Triple site injection at peri-tumoural, subcutaneous and sub areolar regions and larger volume of blue dye agent injection of up to 8mls increases the localisation success in the dual technique group for lymphatic mapping. Magnetic tracing can be used as an adjunct to either single agent radioactive isotope or blue dye (RI/BD) technique when there is failure to localise the sentinel node. Citation Format: Tahera Arif, Mohammed Shamim Absar. Two decades of experience with sentinel node staging of axilla - is false negative no longer a worry? [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS1-62.

Cell-Scale Degradation of Peritumoural Extracellular Matrix Fibre Network and Its Role Within Tissue-Scale Cancer Invasion

Local cancer invasion of tissue is a complex, multiscale process which plays an essential role in tumour progression. During the complex interaction between cancer cell population and the extracellular matrix (ECM), of key importance is the role played by both bulk two-scale dynamics of ECM fibres within collective movement of the tumour cells and the multiscale leading edge dynamics driven by proteolytic activity of the matrix-degrading enzymes (MDEs) that are secreted by the cancer cells. As these two multiscale subsystems share and contribute to the same tumour macro-dynamics, in this work we develop further the model introduced in Shuttleworth and Trucu (Bull Math Biol 81:2176–2219, 2019. https://doi.org/10.1007/s11538-019-00598-w) by exploring a new aspect of their interaction that occurs at the cell scale. Specifically, here we will focus on understanding the cell-scale cross talk between the micro-scale parts of these two multiscale subsystems which get to interact directly in the peritumoural region, with immediate consequences both for MDE micro-dynamics occurring at the leading edge of the tumour and for the cell-scale rearrangement of the naturally oriented ECM fibres in the peritumoural region, ultimately influencing the way tumour progresses in the surrounding tissue. To that end, we will propose a new modelling that captures the ECM fibres degradation not only at macro-scale in the bulk of the tumour but also explicitly in the micro-scale neighbourhood of the tumour interface as a consequence of the interactions with molecular fluxes of MDEs that exercise their spatial dynamics at the invasive edge of the tumour.

Deep Learning Improves Diagnostic Performance of Lymph Node Metastasis in Cervical Cancer by Using Magnetic Resonance Imaging

Background: Accurate identification of lymph node metastasis (LNM) preoperatively in patients with cervical cancer (CC) can avoid unnecessary surgical intervention and benefit treatment planning. In this study, we aimed to develop a deep learning (DL) model using magnetic resonance imaging (MRI) for non-invasive and preoperative LNM prediction in CC. Methods: This research retrospectively enrolled 479 patients with CC from three centres, and divided patients into a primary cohort (n = 338 from two centres) and an independent validation cohort (n = 141 from another centre). We proposed an end-to-end DL model to identify LNM in CC using MRI, compared the performance among three MRI sequences, and explored the effect of intratumoural and peritumoural regions. Moreover, we built a hybrid model to combine the DL model and clinical information, and assessed its prognostic value in predicting disease-free survival (DFS) of CC by Kaplan-Meier analysis. Findings: Among the three sequences, the DL model which used contrast-enhanced T1-weighted imaging and combined both intratumoural and peritumoural regions (defined as CET1WItumour+peri) showed the best performance (AUC = 0.844 in the validation cohort, P < 0.0001 between the positive and negative LNM patients). These results were further improved in the hybrid model that combined CET1WItumour+peri; and clinical lymph node status (AUC = 0.933 in the validation cohort). Moreover, the H-score from the hybrid model was significantly associated with DFS of CC. Interpretation: DL improves the diagnostic performance of LNM in patients with CC. The hybrid model can be served as an easy-to-use tool to diagnose LNM preoperatively and non-invasively. Funding Statement: This paper is supported by the National Natural Science Foundation of China [Grant No. 81922040, 81930053, 81227901, 61702087]; the Beijing Natural Science Foundation [Grant No. 7182109]; the National Key RD the Strategic Priority Research Program of Chinese Academy of Sciences [Grant No. XDB32030200, XDB01030200]; the Youth Innovation Promotion Association CAS [Grant No. 2019136]. Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: Ethical approval was obtained for this retrospective study at every participating centre, and the informed consent from patients was waived.

Multi-parametric arterial spin labelling and diffusion-weighted magnetic resonance imaging in differentiation of grade II and grade III gliomas.

PurposeTo assess arterial spin labelling (ASL) perfusion and diffusion MR imaging (DWI) in the differentiation of grade II from grade III gliomas.Material and methodsA prospective cohort study was done on 36 patients (20 male and 16 female) with diffuse gliomas, who underwent ASL and DWI. Diffuse gliomas were classified into grade II and grade III. Calculation of tumoural blood flow (TBF) and apparent diffusion coefficient (ADC) of the tumoral and peritumoural regions was made. The ROC curve was drawn to differentiate grade II from grade III gliomas.ResultsThere was a significant difference in TBF of tumoural and peritumoural regions of grade II and III gliomas (p = 0.02 and p =0.001, respectively). Selection of 26.1 and 14.8 ml/100 g/min as the cut-off for TBF of tumoural and peritumoural regions differentiated between both groups with area under curve (AUC) of 0.69 and 0.957, and accuracy of 77.8% and 88.9%, respectively. There was small but significant difference in the ADC of tumoural and peritumoural regions between grade II and III gliomas (p = 0.02 for both). The selection of 1.06 and 1.36 × 10-3 mm2/s as the cut-off of ADC of tumoural and peritumoural regions was made, to differentiate grade II from III with AUC of 0.701 and 0.748, and accuracy of 80.6% and 80.6%, respectively. Combined TBF and ADC of tumoural regions revealed an AUC of 0.808 and accuracy of 72.7%. Combined TBF and ADC for peritumoural regions revealed an AUC of 0.96 and accuracy of 94.4%.ConclusionTBF and ADC of tumoural and peritumoural regions are accurate non-invasive methods of differentiation of grade II from grade III gliomas.

Immune Cell Infiltration in the Microenvironment of Liver Oligometastasis from Colorectal Cancer: Intratumoural CD8/CD3 Ratio Is a Valuable Prognostic Index for Patients Undergoing Liver Metastasectomy.

Background: A comprehensive investigation into immune cell infiltration provides more accurate and reliable prognostic information for patients with colorectal liver oligometastases (CLO) after liver metastasectomy. Methods: Simultaneous detection of the immune constituents CD3+, CD8+, Foxp3+ T, and α-SMA+ cells in the liver oligometastasis of 133 patients was conducted using a four-colour immunohistochemical multiplex technique. Immune cells were quantified, and tumour-infiltrating lymphocyte (TIL) ratios were subsequently calculated. Correlation analysis was performed using Pearson’s correlation. Recurrence-free survival (RFS) and overall survival (OS) for TIL ratios were analysed using the Kaplan–Meier method and Cox regression models. Results: Significantly fewer CD3+, CD8+, and Foxp3+ T cells were observed in the intratumoural region than in the peritumoural region of liver metastases. CD3+, CD8+, Foxp3+ T, and α-SMA+ cells showed significantly positive correlations with each other both in the intratumoural and peritumoural regions of liver metastases. Only the CD8/CD3 TIL ratio demonstrated a positive correlation between intratumoural and peritumoural regions of liver metastases (r = 0.541, p < 0.001). Patients with high intratumoural CD8/CD3 ratios had significantly longer 3-year RFS (59.0% vs. 47.4%, p = 0.035) and 3-year OS rates (83.3% vs. 65.8%, p = 0.007) than those with low intratumoural CD8/CD3 ratios. Multivariate analyses revealed that the intratumoural CD8/CD3 ratio was independently associated with RFS (HR = 0.593; 95% CI = 0.357–0.985; p = 0.043) and OS (HR = 0.391; 95% CI = 0.193–0.794; p = 0.009). Conclusion: These findings offer a better understanding of the prognostic value of immune cell infiltration on liver oligometastasis from colorectal cancer.

CBMT-33. VISUALIZING THE METABOLISM OF GLIOBLASTOMA PATIENT-DERIVED ORTHOTOPIC XENOGRAFTS BY MASS SPECTROMETRY IMAGING

Abstract INTRODUCTION. Glioblastoma multiforme (GBM) has a dismal prognosis, which has been attributed to extensive inter- and intra-tumoural heterogeneity. To investigate the metabolic heterogeneity of GBM, we employed desorption electrospray ionization (DESI) imaging on snap frozen samples of GBM patient-derived orthotopic xenografts (PDOX) in rat brains, following infusion of [U-13C]glucose. METHODS. Athymic nude rats (n=3) were infused with [U-13C]glucose (0.4 mg/g body weight bolus, 0.012 mg/g/min at 300 µL/h infusion [1]) for 40, 80 and 120 min, followed by cardiac puncture to measure blood [U-13C]glucose levels using 13C NMR spectroscopy. A second set of rats (n=2) were implanted intracranially with patient-derived GBM cells. The presence of tumour was confirmed by T2-weighted MRI. One animal was infused with [U-13C]glucose for 2 h, while another with [12C]glucose. The brains were snap frozen in liquid nitrogen followed by analysis with DESI imaging in negative ion mode (35 µm resolution). RESULTS. After a 2 h infusion, 43% of the circulating glucose was 13C labelled. Unsupervised spatial clustering of the MSI data resulted in segmentation of normal brain from tumour. Levels of [12C and 13C]glucose were high in the peri-tumoural region and low within the tumour. [12C and 13C]lactate showed the opposite distribution, indicating that the rate of glucose utilization within the tumour exceeded the rate of delivery. The peri-necrotic and peri-tumoural areas had higher levels of glutamate compared to the rest of the tumour. CONCLUSIONS. The results demonstrate that the PDOX is metabolically heterogeneous and differs from surrounding brain. While the tumour was predominately glycolytic, the presence of glutamate in peri-necrotic and peri-tumoral regions indicates the presence of increased TCA cycle activity. Comparison of MSI data with histological staining of brain sections should yield additional information about the relationship between the tumour and its microenvironment.

Intra-tumoural perfusion habitats showed prognostic value in glioblastoma patients

Abstract The perfusion within glioblastoma is associated with tumour microenvironment and may create invasive tumor habitats that could potentially be revealed by perfusion imaging. The purpose of this study is to characterize the peritumoural habitats of glioblastoma for treatment target. Dynamic susceptibility contrast-enhancement (DSC) MRI was acquired pre-operatively on 115 newly-diagnosed glioblastoma patients. All images were co-registered to post-contrast T1-weighted images. The relative cerebral blood volume (rCBV), mean transit time (MTT) and relative cerebral blood flow (rCBF) maps were generated from the DSC images. The contrast-enhanced and peritumoural tumor regions were semi-automatically segmented from the post-contrast T1-weighted and FLAIR images. To delineate the habitats of different perfusion levels, a two clusters mixture model with Gaussian distribution was fitted to the rCBV, rCBF, and MTT within both contrast-enhanced and peritumoural regions. Perfusion parameters of the identified habitats were compared, and the prognostic values of habitats were investigated using survival analysis. The results showed that although non-enhanced, the peritumoral high perfusion (PHP) habitat demonstrated similar perfusion level with the contrast high perfusion (CHP) habitat, with similar rCBV (PHP: 1.13 ± 0.18, 95% CI [1.10, 1.15]; CHP: 1.21 ± 0.25, 95% CI [1.16, 1.21]) and rCBF (PHP: 1.08 ± 0.23, 95% CI [1.05, 1.08]; CHP: 1.08 ± 0.19, 95% CI [1.05, 1.08]). Multivariate Cox regression showed that the volumes of both habitats were associated with worse patient overall survival (PHP: P = 0.032; HR= 7.09; CHP: P = 0.008; HR= 12.01). Our results suggest that the intra-tumoural perfusion habitats may potentially offer treatment targets.

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

BackgroundWe aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). MethodsA total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve. FindingsThe combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p < .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used. InterpretationThis study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

Radiomic Analysis for Pretreatment Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Cervical Cancer: A Multicentre Study

Background: We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC). Methods: A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to primary and independent validation cohorts (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve. Findings: The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images ,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in primary cohort and 0.999 in validation cohort, which was significantly better (p<0.05) than the other radiomic models. Moreover, no significant variation in performance was found if different primary cohorts were used. Interpretation: This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment. Funding: This work was supported by the National Key Research and Development Plan of China [Grant No. 2017YFA0205200]; the National Natural Science Foundation of China [Grant No. 81772012, 81227901, 81527805, and 66161010]; the Nature Science Foundation of Guizhou province [Grant No. 20152044]; the Chinese Academy of Sciences [Grant No. GJJSTD20170004, XDB32030200, and QYZDJ-SSW-JSC005]; the Beijing Natural Science Foundation [Grant No. 7182109]; the Youth Innovation Promotion Association CAS [Grant No. 2019136]; the National Natural Science Foundation of Guangdong [Grant No. 2015A030311024]; the Health and Medical Cooperation Innovation Special Program of Guangzhou Municipal Science and Technology [No. 201508020264]; the National Key Technology Program of the Ministry of Science and Technology [863 program, Grant No. 2014BAI05B03]; the Medical Scientific Research Foundation of Guangdong Province of China [Grant No. A2015063]. Declaration of Interest: The authors declare no potential conflicts of interest. Ethical Approval: This study design was approved by an institutional review board and waived the informed consent requirement.

Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI.

Preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular cancer (HCC) is important for surgery strategy making. We aimed to develop and validate a combined intratumoural and peritumoural radiomics model based on gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI) for preoperative prediction of MVI in primary HCC patients. This study included a training cohort of 110 HCC patients and a validating cohort of 50 HCC patients. All the patients underwent preoperative Gd-EOB-DTPA-enhanced MRI examination and curative hepatectomy. The volumes of interest (VOIs) around the hepatic lesions including intratumoural and peritumoural regions were manually delineated in the hepatobiliary phase of MRI images, from which quantitative features were extracted and analysed. In the training cohort, machine-learning method was applied for dimensionality reduction and selection of the extracted features. The proportion of MVI-positive patients was 38.2% and 40.0% in the training and validation cohort, respectively. Supervised machine learning selected ten features to establish a predictive model for MVI. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity of the combined intratumoural and peritumoural radiomics model in the training and validation cohort were 0.85 (95% confidence interval (CI), 0.77-0.93), 88.2%, 76.2%, and 0.83 (95% CI, 0.71-0.95), 90.0%, 75.0%, respectively. We evaluate quantitative Gd-EOB-DTPA-enhanced MRI image features of both intratumoural and peritumoural regions and provide an effective radiomics-based model for the prediction of MVI in HCC patients, and may therefore help clinicians make precise decisions regarding treatment before the surgery. • An effective radiomics model for prediction of microvascular invasion in HCC patients is established. • The radiomics model is superior to the radiologist in prediction of MVI. • The radiomics model can help clinicians in pretreatment decision making.

Multiparametric MRI in differentiating solitary brain metastasis from high-grade glioma: diagnostic value of the combined use of diffusion-weighted imaging, dynamic susceptibility contrast imaging, and magnetic resonance spectroscopy parameters.

The purpose of this study was to determine whether the combined use of diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic susceptibility contrast imaging (DSCI) parameters could provide a more accurate diagnosis in the differentiation of high-grade glioma (HGG) from solitary brain metastasis (SBM) in the enhancing tumour and in the peritumoural region. Fifty-six patients who received DWI, DSCI, and MRS before surgery were assessed. In differentiating SBM from HGG, the cutoff values of the DWI-apparent diffusion coefficient (ADCmin, ADCmax, and ADCmean), DSCI-relative cerebral blood volume (rCBV), and MRS-Cho/Cr, Cho/NAA, and NAA/Cr parameters for the peritumoural region were determined with ROC. The combined ROC curve was used for the different combinations of the peritumoural region DWI, DSCI, and MRS parameters in differentiating between the two tumours, and the best model combination was formed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. This study was approved by the Institutional Review Board at our institutes. In the enhancing tumour, all the parameters except NAA/Cr (P = 0.024) exhibited no statistical difference in differentiating between these two groups (P > 0.05). AUC values for ADCmin, ADCmax, ADCmean, rADCmin, rADCmax, rADCmean, rCBV, Cho/Cr, Cho/NAA, and NAA/Cr parameters in the peritumoural region in differentiating SBM from HGG were 0.860, 0.822, 0.848, 0.822, 0.801, 0.822, 0.906, 0.851, 0.903, and 0.784, respectively. In differentiating HGG from SBM, the best model consisted of the combination of peritumoural ADCmin, rCBV, and Cho/NAA parameters. AUC values were 0.970. The combination of peritumoural region ADCmin, rCBV, and Cho/NAA parameters can help in differentiating SBM from HGG, with a diagnostic accuracy of 97%.

Usefulness of quantitative peritumoural perfusion and proton spectroscopic magnetic resonance imaging evaluation in differentiating brain gliomas from solitary brain metastases.

The purpose of our study was to evaluate whether peritumoural perfusion weighted and proton spectroscopic magnetic resonance imaging can be used in differentiating between primary gliomas and solitary metastases. Ten low-grade gliomas, eight high-grade gliomas and 10 metastases were prospectively evaluated with magnetic resonance imaging, dynamic susceptibility contrast enhanced perfusion imaging and single-voxel proton magnetic resonance spectroscopy before surgical resection or stereotactic biopsy. Maximal relative cerebral blood volume values were calculated drawing three regions of interest of 2 cm(2) in the non-enhancing peritumoural areas. Maximal relative cerebral blood volume values were normalised to that of contralateral normal-appearing white matter. Maximal choline/creatine ratios were calculated from three voxels of 10 cm(3) placed in the peritumoural areas defined as non-enhancing peritumoural white matter surrounding the tumour. The tumour grade presumed with these values was compared to histopathological grading. Differences in the study parameters between groups were assessed using the Mann-Whitney test. A receiver operating characteristic analysis was performed to determine cut-off values. A clear relative cerebral blood volume cut-off value of 1.88 was detected for differentiating low-grade gliomas from high-grade gliomas. A clear relative cerebral blood volume cut-off value of 1.20 was detected for differentiation of metastases from gliomas. The differences in the choline/creatine ratios in the peritumoural regions of high-grade gliomas and of solitary metastasis were statistically significant (P < 0.001) but a clear cut-off value was not found. Our preliminary data support the hypothesis that peritumoural perfusion-weighted imaging can assist in preoperative differentiation between a glioma and a solitary metastasis.