Volumetric Tumor Research Articles

Abstract A040: A novel stroma-mediated positive interaction between resistant and sensitive cells in non-small cell lung cancer facilitates drug resistance

Abstract Resistance to targeted therapies, particularly those inhibiting oncogenic tyrosine kinases (TKIs), presents a significant challenge in the treatment of advanced non-small cell lung cancer (NSCLC). Despite initial robust clinical responses, tumors often relapse due to the emergence of resistant cell populations. The origins of this resistance—whether it is pre-existing in a subset of tumor cells or arises de novo during treatment—remains a critical question in understanding and combating therapeutic failure. To investigate this, we developed in silico mathematical models that integrate both pre-existing and de novo resistance mechanisms within a framework that explicitly integrates the stromal niche. Our study focused on the evolutionary dynamics of NSCLC in response to therapy, using a hybrid agent-based spatial modeling framework (HAL) to simulate tumor behavior under treatment. These models were designed to simulate the complex interactions between resistant and sensitive tumor cells, mediated by the stroma, and to predict the timing of relapse based on these interactions. Our findings suggest that stroma-mediated interactions play a crucial role in the evolutionary dynamics of therapy responses, influencing the fitness and survival of both resistant and sensitive cells. Specifically, our models predict that pre-existing resistance is unlikely to be compatible with the long remission periods observed in a substantial subset of patients treated with advanced anaplastic lymphoma kinase (ALK) inhibitors. This conclusion was supported by the observed relapse times and volumetric tumor growth data from subsequent animal models. Moreover, our analyses highlight the importance of ecological factors within the tumor microenvironment in shaping the response to therapy. The spatial models we developed reveal that the stroma can foster positive interactions between resistant and sensitive cells, thereby accelerating the emergence of resistance. These insights underscore the need to consider the stromal niche when developing new therapeutic strategies and when interpreting the dynamics of drug resistance. In conclusion, our study provides new insights into the role of stroma in mediating resistance to TKIs in NSCLC. By combining mathematical modeling with experimental data, we have elucidated how stroma-mediated interactions between tumor cells can influence the evolution of resistance, challenging the assumption that pre-existing resistance alone drives relapses in TKI-treated patients. Our findings emphasize the need for adaptive therapeutic approaches that anticipate and disrupt these microenvironmental interactions to improve patient outcomes. Citation Format: Rishi M. Shah, Sagnik Yarlagadda, Mark Robertson-Tessi, Bina Desai, Tatiana Miti, Daria Miroshnychenko, David Basanta, Andriy Marusyk, Alexander Anderson. A novel stroma-mediated positive interaction between resistant and sensitive cells in non-small cell lung cancer facilitates drug resistance [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A040.

Development and Evaluation of Automated Artificial Intelligence-Based Brain Tumor Response Assessment in Patients with Glioblastoma.

To develop and evaluate an automated, AI-based, volumetric brain tumor MRI response assessment algorithm on a large cohort of patients treated at a high-volume brain tumor center. We retrospectively analyzed data from 634 patients treated for glioblastoma at a single brain tumor center over a 5-year period (2017-2021). The mean age was 56 +/-13 years. 372/634 (59%) patients were male, and 262/634 (41%) patients were female. Study data consisted of 3,403 brain MRI exams and corresponding standardized, radiologist-based brain tumor response assessments (BT-RADS). An artificial intelligence (AI)-based brain tumor response assessment algorithm was developed using automated, volumetric tumor segmentation. AI-based response assessments were evaluated for agreement with radiologist-based response assessments and ability to stratify patients by overall survival. Metrics were computed to assess the agreement using BTRADS as the ground-truth, fixed-time point survival analysis was conducted to evaluate the survival stratification, and associated P-values were calculated. For all BT-RADS categories, AI-based response assessments showed moderate agreement with radiologists' response assessments (F1 = 0.587-0.755). Kaplan-Meier survival analysis revealed statistically worse overall fixed time point survival for patients assessed as image worsening equivalent to RANO progression by human alone compared to by AI alone (log-rank P=0.007). Cox proportional hazard model analysis showed a disadvantage to AI-based assessments for overall survival prediction (P=0.012). AI-based volumetric glioblastoma MRI response assessment following BT-RADS criteria yielded moderate agreement for replicating human response assessments and slightly worse stratification by overall survival. GBM＝ Glioblastoma; RANO＝ Response Assessment in Neuro-Oncology; BTRADS＝ Brain Tumor Reporting and Data System; NLP = Natural Language Processing.

Intravenous and intracranial GD2-CAR T cells for H3K27M+ diffuse midline gliomas.

H3K27M-mutant diffuse midline gliomas (DMGs) express high levels of the disialoganglioside GD2(ref. 1). Chimeric antigen receptor-modified T cells targeting GD2 (GD2-CART) eradicated DMGs in preclinical models2. Arm A of Phase I trial no. NCT04196413 (ref.3) administered one intravenous (IV) dose of autologous GD2-CART to patients with H3K27M-mutant pontine (DIPG) or spinal DMG (sDMG) at two dose levels (DL1, 1 × 106 kg-1; DL2, 3 × 106 kg-1) following lymphodepleting chemotherapy. Patients with clinical or imaging benefit were eligible for subsequent intracerebroventricular (ICV) intracranial infusions (10-30 × 106 GD2-CART). Primary objectives were manufacturing feasibility, tolerability and the identification of maximally tolerated IV dose. Secondary objectives included preliminary assessments of benefit. Thirteen patients enroled, with 11 receiving IV GD2-CART on study (n = 3 DL1 (3 DIPG); n = 8 DL2 (6 DIPG, 2 sDMG)). GD2-CART manufacture was successful for all patients. No dose-limiting toxicities occurred on DL1, but three patients experienced dose-limiting cytokine release syndrome on DL2, establishing DL1 as the maximally tolerated IV dose. Nine patients received ICV infusions, with no dose-limiting toxicities. All patients exhibited tumour inflammation-associated neurotoxicity, safely managed with intensive monitoring and care. Four patients demonstrated major volumetric tumour reductions (52, 54, 91 and 100%), with a further three patients exhibiting smaller reductions. One patient exhibited a complete response ongoing for over 30 months since enrolment. Nine patients demonstrated neurological benefit, as measured by a protocol-directed clinical improvement score. Sequential IV, followed by ICV GD2-CART, induced tumour regressions and neurological improvements in patients with DIPG and those with sDMG.

Recommendation for Clinical T Staging in Patients with Non-Small Cell Lung Cancer: Volumetric Measurement: A Retrospective Study from Turkey.

Currently, clinical T staging in non-small cell lung cancer (NSCLC) is based on the largest radiological diameter observed on computed tomography (CT). Under this system, tumors with varying shapes-such as spherical, amorphous, or spiculated tumors- can be assigned the same T stage even with different volumes. We aimed to propose a 3-dimensional (3D) volumetric staging system for NSCLC as an alternative to diameter-based T staging and to conduct comparative survival analyses between these methods. We retrospectively analyzed data from patients who underwent surgery for pT1-4N0M0 primary NSCLC between January 2018 and May 2022. Digital Imaging and Communications in Medicine data from patient CT scans were uploaded to 3D Slicer software for volumetric tumor measurement. Using the paired samples t-test or the Wilcoxon test, we compared the expected tumor volumes, calculated by tumor diameter, with the actual volumes measured by 3D Slicer. Receiver operating characteristic analysis was employed to determine the cut-off value for tumor volume. Kaplan-Meier analysis was utilized to assess overall survival, while the log-rank method was applied to compare survival differences between groups. The significance of changes in T stage was evaluated using the marginal homogeneity test. The study included 136 patients. Significant differences were observed between expected and actual tumor volumes (p=0.01), and associated changes in T stage were also significant (p=0.04). The survival analysis performed using tumor volume (p=0.009) yielded superior results compared to that based on diameter (p=0.04) in paients with early tumor stage. T-factor staging based on tumor volume could represent an alternative staging method for NSCLC.

Perfusion-weighted imaging with dynamic contrast enhancement (PWI/DCE) morphologic, qualitative, semiquantitative, and radiomics features predicting undifferentiated pleomorphic sarcoma (UPS) treatment response

Undifferentiated pleomorphic sarcoma (UPS) is the largest subgroup of soft tissue sarcomas. This study determined the value of perfusion-weighted imaging with dynamic-contrast-enhancement (PWI/DCE) morphologic, qualitative, and semiquantitative features for predicting UPS pathology-assessed treatment effect (PATE). This retrospective study included 33 surgically excised extremity UPS patients with pre-surgical MRI. Volumetric tumor segmentation from PWI/DCE was obtained at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The surgical specimens’ PATE separated cases into Responders (R) (≥ 90%, 16 patients), Partial-Responders (PR) (89 − 31%, 10 patients), and Non-Responders (NR) (≤ 30%, seven patients). Seven semiquantitative kinetic parameters and maps were extracted from time-intensity curves (TICs), and 107 radiomic features were derived. Statistical analyses compared R vs. PR/NR. At PRT, 79% of R displayed a “Capsular” morphology (P = 1.49 × 10-7), and 100% demonstrated a TIC-type II (P = 8.32 × 10-7). 80% of PR showed “Unipolar” morphology (P = 1.03 × 10-5), and 60% expressed a TIC-type V (P = 0.06). Semiquantitative wash-in rate (WiR) was able to separate R vs. PR/NR (P = 0.0078). The WiR radiomics displayed significant differences in the first_order_10 percentile (P = 0.0178) comparing R vs. PR/NR at PRT. The PWI/DCE TIC-type II curve, low WiR, and “Capsular” enhancement represent PRT patterns typically observed in successfully treated UPS and demonstrate potential for UPS treatment response assessment.

Studies on the thermal sensitivity of lung cancer cells exposed to an alternating magnetic field and magnesium-doped maghemite nanoparticles

BackgroundMagnetic fluid hyperthermia (MFH) represents a promising therapeutic strategy in cancer utilizing the heating capabilities of magnetic nanoparticles when exposed to an alternating magnetic field (AMF). Because the efficacy and safety of MFH treatments depends on numerous intrinsic and extrinsic factors, therefore, the proper MFH setups should focus on thermal energy dosed into the cancer cells.MethodsIn this study, we performed MFH experiments using human lung cancer A549 cells (in vitro) and NUDE Balb/c mice bearing human lung (A549) cancer (in vivo). In these two experimental models, the heat was induced by magnesium-doped iron(III) oxide nanoparticles coated with mPEG-silane (Mg0.1-γ-Fe2O3(mPEG-silane)0.5) when exposed to an AMF.ResultsWe observed that the lung cancer cells treated with Mg0.1-γ-Fe2O3(mPEG-silane)0.5 (0.25 mg·mL−1) and magnetized for 30 min at 14.4 kA·m−1 yielded a satisfactory outcome in reducing the cell viability up to ca. 21% (in vitro). The activation energy calculated for this field strength was estimated for 349 kJ·mol−1. Both volumetric measurements and tumor mass assessments confirmed by magnetic resonance imaging (MRI) showed a superior thermal effect in mice bearing human lung cancer injected intratumorally with Mg0.1-γ-Fe2O3(mPEG-silane)0.5 nanoparticles (3 mg·mL−1) and subjected to an AMF (18.3 kA·m−1) for 30 min four times at weekly intervals. Research demonstrated that mice undergoing MFH exhibited a marked suppression of tumor growth (V = 169 ± 94 mm3; p < 0.05) in comparison to the control group of untreated mice. The CEM43 (cumulative number of equivalent minutes at 43 °C) value for these treatments were estimated for ca. 9.6 min with the specific absorption rate (SAR) level ranging from 100 to 150 W·g−1.ConclusionsThe as-obtained results, both cytotoxic and those related to energy calculations and SAR, may contribute to the advancement of thermal therapies, concurrently indicating that the proposed magnetic fluid hyperthermia holds a great potential for further testing in the context of medical applications.Graphical

SwinVNETR: Swin V-net Transformer with non-local block for volumetric MRI Brain Tumor Segmentation

Brain Tumor Segmentation (BTS) and classification are important and growing research fields. Magnetic resonance imaging (MRI) is commonly used in the diagnosis of brain tumours owing to its low radiation exposure and high image quality. One of the current subjects in the field of medical imaging is how to quickly and precisely segment MRI scans of brain tumours. Unfortunately, most existing brain tumour segmentation algorithms use inadequate 2D picture segmentation methods and fail to capture the spatial correlation between features. In this study, we propose a segmentation model (SwinVNETR) Swin V-NetTRansformer-based architecture with a non-local block. This model was trained using the Brain Tumor Segmentation Challenge BraTS 2021 dataset. The Dice similarity coefficients for the enhanced tumour (ET), whole tumour (WT), and tumour core (TC) are 0.84, 0.91, and 0.87, respectively. By leveraging this methodology, we can segment brain tumours more accurately than ever before. In conclusion, we present the findings of our model through the application of the Grad-CAM methodology, an eXplainable Artificial Intelligence (XAI) technique utilized to elucidate the insights derived from the model, which helped in better understanding; doctors can better diagnose and treat patients with brain tumours.

Defining tumor growth in vestibular schwannomas: a volumetric inter-observer variability study in contrast-enhanced T1-weighted MRI

PurposeFor patients with vestibular schwannomas (VS), a conservative observational approach is increasingly used. Therefore, the need for accurate and reliable volumetric tumor monitoring is important. Currently, a volumetric cutoff of 20% increase in tumor volume is widely used to define tumor growth in VS. The study investigates the tumor volume dependency on the limits of agreement (LoA) for volumetric measurements of VS by means of an inter-observer study.MethodsThis retrospective study included 100 VS patients who underwent contrast-enhanced T1-weighted MRI. Five observers volumetrically annotated the images. Observer agreement and reliability was measured using the LoA, estimated using the limits of agreement with the mean (LOAM) method, and the intraclass correlation coefficient (ICC).ResultsThe 100 patients had a median average tumor volume of 903 mm3 (IQR: 193–3101). Patients were divided into four volumetric size categories based on tumor volume quartile. The smallest tumor volume quartile showed a LOAM relative to the mean of 26.8% (95% CI: 23.7–33.6), whereas for the largest tumor volume quartile this figure was found to be 7.3% (95% CI: 6.5–9.7) and when excluding peritumoral cysts: 4.8% (95% CI: 4.2–6.2).ConclusionAgreement limits within volumetric annotation of VS are affected by tumor volume, since the LoA improves with increasing tumor volume. As a result, for tumors larger than 200 mm3, growth can reliably be detected at an earlier stage, compared to the currently widely used cutoff of 20%. However, for very small tumors, growth should be assessed with higher agreement limits than previously thought.

Radiomics by Quantitative Diffusion-weighted MRI for Predicting Response in Patients with Extremity Soft-tissue Undifferentiated Pleomorphic Sarcoma

Purpose: This study aimed to determine the relevance of first- and high-order radiomic features derived from Diffusion-Weighted Imaging (DWI) and Apparent Diffusion Coefficient (ADC) maps for predicting treatment response in patients with Undifferentiated Pleomorphic Sarcoma (UPS). Methods: This retrospective study included 33 extremity UPS patients with pre-surgical DWI/ADC and surgical resection. Manual volumetric tumor segmentation was performed on DWI/ADC maps acquired at Baseline (BL), Post-Chemotherapy (PC), and Post-Radiation Therapy (PRT). The percentage of pathology-assessed treatment effect (PATE) in surgical specimens categorized patients into responders (R; PATE ≥ 90%; 16 patients), partial-responders (PR; 89% - 31% PATE; 10 patients), and non-responders (NR; PATE ≤ 30%; 7 patients). 107 radiomic features were extracted from BL, PC, and PRT ADC maps. Statistical analyses compared R vs. PR/NR. Results: Pseudo-progression at PC and universal stability at PRT were observed in R and PR/NR based on RECIST, WHO, and volumetric assessments. At PRT, responders displayed a 35% increase in ADC mean (p = 0.0034), a 136% decrease in skewness (p = 0.0001), and a 363% increase in the 90th percentile proportion (p = 0.0009). Comparing R vs. PR/NR at BL, statistically significant differences were observed in glrlm_highgraylevelrunemphasis (p = 0.0081), glrlm_shortrunhighgraylevelemphasis (p = 0.0138), gldm_highgraylevelemphasis (p = 0.0138), glcm_sumaverage (p = 0.0164), glcm_jointaverage (p = 0.0164), and glcm_autocorrelation (p = 0.0193). At PC, firstorder_meanabsolutedeviation (p = 0.0078), firstorder_interquartilerange (p = 0.0109), firstorder_variance (p = 0.0109), and firstorder_robustmeanabsolutedeviation (p = 0.0151) provided statistically significant differences. Conclusion: Observing a high post-therapeutic ADC mean, low skewness, and high 90th percentile proportion with respect to baseline is predictive of successfully treated UPS patients presenting &gt; 90% PATE. Highly significant higher-order radiomic results include glrlm-highgraylevelrunemphasis (BL) and first-order-mean absolute deviation (PC).

Sequential intravenous and intracerebroventricular GD2-CAR T-cell therapy for H3K27M-mutated diffuse midline gliomas.

H3K27M-mutant diffuse midline gliomas (DMGs) express high levels of the GD2 disialoganglioside and chimeric antigen receptor modified T-cells targeting GD2 (GD2-CART) eradicate DMGs in preclinical models. Arm A of the Phase I trial NCT04196413 administered one IV dose of autologous GD2-CART to patients with H3K27M-mutant pontine (DIPG) or spinal (sDMG) diffuse midline glioma at two dose levels (DL1=1e6/kg; DL2=3e6/kg) following lymphodepleting (LD) chemotherapy. Patients with clinical or imaging benefit were eligible for subsequent intracerebroventricular (ICV) GD2-CART infusions (10-30e6 GD2-CART). Primary objectives were manufacturing feasibility, tolerability, and identification of a maximally tolerated dose of IV GD2-CART. Secondary objectives included preliminary assessments of benefit. Thirteen patients enrolled and 11 received IV GD2-CART on study [n=3 DL1(3 DIPG); n=8 DL2(6 DIPG/2 sDMG). GD2-CART manufacturing was successful for all patients. No dose-limiting toxicities (DLTs) occurred on DL1, but three patients experienced DLT on DL2 due to grade 4 cytokine release syndrome (CRS). Nine patients received ICV infusions, which were not associated with DLTs. All patients exhibited tumor inflammation-associated neurotoxicity (TIAN). Four patients demonstrated major volumetric tumor reductions (52%, 54%, 91% and 100%). One patient exhibited a complete response ongoing for >30 months since enrollment. Eight patients demonstrated neurological benefit based upon a protocol-directed Clinical Improvement Score. Sequential IV followed by ICV GD2-CART induced tumor regressions and neurological improvements in patients with DIPG and sDMG. DL1 was established as the maximally tolerated IV GD2-CART dose. Neurotoxicity was safely managed with intensive monitoring and close adherence to a management algorithm.

A Neuroradiologist's Guide to Operationalizing the Response Assessment in Neuro-Oncology (RANO) Criteria Version 2.0 for Gliomas in Adults.

Radiographic assessment plays a crucial role in the management of patients with central nervous system (CNS) tumors, aiding in treatment planning and evaluation of therapeutic efficacy by quantifying response. Recently, an updated version of the Response Assessment in Neuro-Oncology (RANO) criteria (RANO 2.0) was developed to improve upon prior criteria and provide an updated, standardized framework for assessing treatment response in clinical trials for gliomas in adults. This article provides an overview of significant updates to the criteria including (1) the use of a unified set of criteria for high and low grade gliomas in adults; (2) the use of the post-radiotherapy MRI scan as the baseline for evaluation in newly diagnosed high-grade gliomas; (3) the option for the trial to mandate a confirmation scan to more reliably distinguish pseudoprogression from tumor progression; (4) the option of using volumetric tumor measurements; and (5) the removal of subjective non-enhancing tumor evaluations in predominantly enhancing gliomas (except for specific therapeutic modalities). Step-by-step pragmatic guidance is hereby provided for the neuroradiologist and imaging core lab involved in operationalization and technical execution of RANO 2.0 in clinical trials, including the display of representative cases and in-depth discussion of challenging scenarios.

BIOM-14. UTILITY OF MIRNA-BASED LIQUID BIOPSY AND RADIOGRAPHIC IMAGING IN PEDIATRIC BRAIN TUMOR PATIENTS

Abstract BACKGROUND MRI is the current gold standard imaging technique for diagnostic evaluation and monitoring of pediatric CNS tumors; however, MRI measures can only provide inference into tumor biology and molecular stratification. miRNAs are an abundant and stable nucleic acid found circulating in blood and cerebrospinal fluid (CSF) and can be utilized as a tumor biomarker. METHODS We hypothesized that correlating miRNA biomarkers with radiological tumor measurements may provide improved diagnostic and monitoring for patients with pediatric brain tumors. Using a cohort of 54 pediatric brain tumors including low grade glioma, ependymoma, germ cell tumor, medulloblastoma, atypical teratoid rhabdoid tumor and high-grade glioma we attempted to combine MRI findings and circulating miRNA data. The miRNA expression was profiled in 33 CSF and 52 plasma samples using the HTG EdgeSeq platform. Clinically acquired, multi-parametric MRI scans at time-points close to liquid biopsy collection were collected retrospectively and used to generate volumetric tumor segmentations. Machine learning methods were implemented to evaluate combinatory features. RESULTS We identified unique miRNA targets that significantly correlated with MRI features, clinical findings, and patient outcomes. In both CSF and plasma, miRNA expression was found to correlate with diagnosis and clinical features including tumor grade and disease burden (p &amp;lt; 0.05). In CSF, miRNA expression was correlated with MRI features including cystic core presence, edema, leptomeningeal disease and tumor location (p &amp;lt;0.05). In plasma, differences in miRNA expression were related to MRI measurements including cystic core volume, location, and leptomeningeal disease (p &amp;lt; 0.05). Combination of miRNA targets for plasma or CSF with imaging-based features significantly improved histology specific multimodal diagnosis predictions. CONCLUSIONS These results demonstrate the potential utility of miRNAs as a pediatric brain tumor biomarker which when combined with imaging features can improve minimally and non-invasive diagnostics and the subsequent management of pediatric brain tumor patients.

Influence of Phase Change Droplet Activation and Microbubble Cavitation on the Microenvironment of Hepatocellular Carcinoma

ObjectiveBoth microbubble ultrasound contrast agents and acoustic phase change droplets (APCD) have been explored in hepatocellular carcinoma (HCC). This work aimed to evaluate changes to the HCC microenvironment following either microbubble or APCD destruction in a syngeneic pre-clinical model. MethodsMouse RIL-175 HCC tumors were grown in the right flank of 64 immunocompetent mice. Pre-treatment, photoacoustic volumetric tumor oxygenation, and power Doppler measurements were obtained using a Vevo 3100 system (VisualSonics, Toronto, Canada). The experimental groups received a 0.1 mL bolus injection of either Definity ultrasound contrast agent (Lantheus Medical Imaging) or APCD fabricated by condensing Definity. Following injection, ultrasound destruction was performed using flash-replenishment sequences on a Sequoia with a 10L4 probe (Siemens) for the duration of enhancement. Tumor oxygenation and power Doppler measurements were then repeated immediately post-ultrasound treatment. Twenty-four hours post-treatment, animals were euthanized, and tumors were harvested and stained for CD31, Cleaved Caspase 3 and CD45. ResultsImaging biomarkers demonstrated a significant reduction in percent vascularity following either microbubble or APCD destruction in the tumor microenvironment ( p < 0.022) but no significant changes in tumor oxygenation (p = 0.12). Similarly, immunohistochemistry data demonstrated a significant decrease in CD31 expression (p < 0.042) and an increase in apoptosis (p < 0.014) in tumors treated with destroyed microbubbles or APCD relative to controls. Finally, a significant increase in CD45 expression was observed in tumors treated with APCD (p = 0.046), indicating an increase in tumor immune response. ConclusionUltrasound-triggered destruction of both microbubbles and APCD reduces vascularity, increases apoptosis, and may also increase immune response in this HCC model.

Three-Direction Fusion for Accurate Volumetric Liver and Tumor Segmentation.

Biomedical image segmentation of organs, tissues and lesions has gained increasing attention in clinical treatment planning and navigation, which involves the exploration of two-dimensional (2D) and three-dimensional (3D) contexts in the biomedical image. Compared to 2D methods, 3D methods pay more attention to inter-slice correlations, which offer additional spatial information for image segmentation. An organ or tumor has a 3D structure that can be observed from three directions. Previous studies focus only on the vertical axis, limiting the understanding of the relationship between a tumor and its surrounding tissues. Important information can also be obtained from sagittal and coronal axes. Therefore, spatial information of organs and tumors can be obtained from three directions, i.e. the sagittal, coronal and vertical axes, to understand better the invasion depth of tumor and its relationship with the surrounding tissues. Moreover, the edges of organs and tumors in biomedical image may be blurred. To address these problems, we propose a three-direction fusion volumetric segmentation (TFVS) model for segmenting 3D biomedical images from three perspectives in sagittal, coronal and transverse planes, respectively. We use the dataset of the liver task provided by the Medical Segmentation Decathlon challenge to train our model. The TFVS method demonstrates a competitive performance on the 3D-IRCADB dataset. In addition, the t-test and Wilcoxon signed-rank test are also performed to show the statistical significance of the improvement by the proposed method as compared with the baseline methods. The proposed method is expected to be beneficial in guiding and facilitating clinical diagnosis and treatment.

MRI-based volumetric tumor parameters before and during chemoradiation predict tumor recurrence and patient survival in locally advanced cervical cancer: a subgroup analysis of a phase II prospective trial.

This subgroup analysis of a prospective phase II trial aimed to identify valuable and accessible prognostic factors for overall survival (OS) and progression-free survival (PFS) of patients with locally advanced cervical cancer (LACC). Patients with FIGO II to IVA cervical cancer were assessed in this study. All patients underwent concurrent chemoradiotherapy (CCRT) followed by brachytherapy. Tumor parameters based on MRI scans before and during CCRT were evaluated for Overall survival (OS) and Progression-free survival (PFS). A total of 86 patients were included in this analysis with a median follow-up period of 31.7months. Three-year OS and PFS rates for all patients were 87.1% and 76.5%, respectively. Univariate Cox regression analysis showed that restaging tumor size (rTS) over 2.55cm (p < 0.001), initial tumor volume (iTV) over 55.99cc (p < 0.001), downstaging (p = 0.042), and restaging tumor volume (rTV) over 6.25cc (p = 0.006) were significantly associated with OS. rTS (p < 0.001), iTV (p < 0.001), downstaging (p = 0.027), and rTV (p < 0.001) were identified as significant prognostic factors for PFS. In the stepwise multivariable analysis, only rTS > 2.55cm showed statistically significant with OS (HR: 5.47, 95% CI 1.80-9.58, p = 0.035) and PFS (HR: 3.83, 95% CI 1.50-11.45; p = 0.025). Initial tumor size and restaging tumor volume that are easily accessible during radiotherapy provide valuable prognostic information for cervical cancer. MRI-based measurable volumetric scoring system can be readily applied in real-world practice of cervical cancer. This study is a subgroup analysis of prospective trial registered at ClinicalTrials.gov Identifier: NCT02993653.

NaMa: Neighbor-Aware Multi-Modal Adaptive Learning for Prostate Tumor Segmentation on Anisotropic MR Images

Accurate segmentation of prostate tumors from multi-modal magnetic resonance (MR) images is crucial for diagnosis and treatment of prostate cancer. However, the robustness of existing segmentation methods is limited, mainly because these methods 1) fail to adaptively assess subject-specific information of each MR modality for accurate tumor delineation, and 2) lack effective utilization of inter-slice information across thick slices in MR images to segment tumor as a whole 3D volume. In this work, we propose a two-stage neighbor-aware multi-modal adaptive learning network (NaMa) for accurate prostate tumor segmentation from multi-modal anisotropic MR images. In particular, in the first stage, we apply subject-specific multi-modal fusion in each slice by developing a novel modality-informativeness adaptive learning (MIAL) module for selecting and adaptively fusing informative representation of each modality based on inter-modality correlations. In the second stage, we exploit inter-slice feature correlations to derive volumetric tumor segmentation. Specifically, we first use a Unet variant with sequence layers to coarsely capture slice relationship at a global scale, and further generate an activation map for each slice. Then, we introduce an activation mapping guidance (AMG) module to refine slice-wise representation (via information from adjacent slices) for consistent tumor segmentation across neighboring slices. Besides, during the network training, we further apply a random mask strategy to each MR modality to improve feature representation efficiency. Experiments on both in-house and public (PICAI) multi-modal prostate tumor datasets show that our proposed NaMa performs better than state-of-the-art methods.

Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

The extended acquisition times required for MRI limit its availability in resource-constrained settings. Consequently, accelerating MRI by undersampling k-space data, which is necessary to reconstruct an image, has been a long-standing but important challenge. We aimed to develop a deep convolutional neural network (dCNN) optimisation method for MRI reconstruction and to reduce scan times and evaluate its effect on image quality and accuracy of oncological imaging biomarkers. In this multicentre, retrospective, cohort study, MRI data from patients with glioblastoma treated at Heidelberg University Hospital (775 patients and 775 examinations) and from the phase 2 CORE trial (260 patients, 1083 examinations, and 58 institutions) and the phase 3 CENTRIC trial (505 patients, 3147 examinations, and 139 institutions) were used to develop, train, and test dCNN for reconstructing MRI from highly undersampled single-coil k-space data with various acceleration rates (R=2, 4, 6, 8, 10, and 15). Independent testing was performed with MRIs from the phase 2/3 EORTC-26101 trial (528 patients with glioblastoma, 1974 examinations, and 32 institutions). The similarity between undersampled dCNN-reconstructed and original MRIs was quantified with various image quality metrics, including structural similarity index measure (SSIM) and the accuracy of undersampled dCNN-reconstructed MRI on downstream radiological assessment of imaging biomarkers in oncology (automated artificial intelligence-based quantification of tumour burden and treatment response) was performed in the EORTC-26101 test dataset. The public NYU Langone Health fastMRI brain test dataset (558 patients and 558 examinations) was used to validate the generalisability and robustness of the dCNN for reconstructing MRIs from available multi-coil (parallel imaging) k-space data. In the EORTC-26101 test dataset, the median SSIM of undersampled dCNN-reconstructed MRI ranged from 0·88 to 0·99 across different acceleration rates, with 0·92 (95% CI 0·92-0·93) for 10-times acceleration (R=10). The 10-times undersampled dCNN-reconstructed MRI yielded excellent agreement with original MRI when assessing volumes of contrast-enhancing tumour (median DICE for spatial agreement of 0·89 [95% CI 0·88 to 0·89]; median volume difference of 0·01 cm3 [95% CI 0·00 to 0·03] equalling 0·21%; p=0·0036 for equivalence) or non-enhancing tumour or oedema (median DICE of 0·94 [95% CI 0·94 to 0·95]; median volume difference of -0·79 cm3 [95% CI -0·87 to -0·72] equalling -1·77%; p=0·023 for equivalence) in the EORTC-26101 test dataset. Automated volumetric tumour response assessment in the EORTC-26101 test dataset yielded an identical median time to progression of 4·27 months (95% CI 4·14 to 4·57) when using 10-times-undersampled dCNN-reconstructed or original MRI (log-rank p=0·80) and agreement in the time to progression in 374 (95·2%) of 393 patients with data. The dCNN generalised well to the fastMRI brain dataset, with significant improvements in the median SSIM when using multi-coil compared with single-coil k-space data (p<0·0001). Deep-learning-based reconstruction of undersampled MRI allows for a substantial reduction of scan times, with a 10-times acceleration demonstrating excellent image quality while preserving the accuracy of derived imaging biomarkers for the assessment of oncological treatment response. Our developments are available as open source software and hold considerable promise for increasing the accessibility to MRI, pending further prospective validation. Deutsche Forschungsgemeinschaft (German Research Foundation) and an Else Kröner Clinician Scientist Endowed Professorship by the Else Kröner Fresenius Foundation.

Laser interstitial thermal therapy for deep-seated perivascular brain tumors is not associated with distal ischemia.

Laser interstitial thermal therapy (LITT) is a minimally invasive cytoreductive treatment option for brain tumors with a risk of vascular injury from catheter placement or thermal energy. This may be of concern with deep-seated tumors that have surrounding end-artery perforators and critical microvasculature. The purpose of this study was to assess the risk of distal ischemia following LITT for deep-seated perivascular brain tumors. A retrospective review of a multi-institution database was used to identify patients who underwent LITT between 2013 and 2022 for tumors located within the insula, thalamus, basal ganglia, and anterior perforated substance. Demographic, clinical and volumetric tumor characteristics were collected. The primary outcome was radiographic evidence of distal ischemia on post-ablation magnetic resonance imaging (MRI). 61 LITT ablations for deep-seated perivascular brain tumors were performed. Of the tumors treated, 24 (39%) were low-grade gliomas, 32 (52%) were high-grade gliomas, and 5 (8%) were metastatic. The principal location included 31 (51%) insular, 14 (23%) thalamic, 13 (21%) basal ganglia, and 3 (5%) anterior perforated substance tumors. The average tumor size was 19.6 cm3 with a mean ablation volume of 11.1cm3. The median extent of ablation was 92% (IQR 30%, 100%). Two patients developed symptomatic intracerebral hemorrhage after LITT. No patient had radiographic evidence of distal ischemia on post-operative diffusion weighted imaging. We demonstrate that LITT for deep-seated perivascular brain tumors has minimal ischemic risks and is a feasible cytoreductive treatment option for otherwise difficult to access intracranial tumors.

Penggunaan Software 3Dd Slicer pada Pemodelan dan Visualisasi Volumetrik Meningioma

Meningioma is a tumor that is often found in Indonesia. Treatment for meningomenia itself can be through surgery, radiotherapy, or a combination of both. The decision to carry out radiotherapy itself must go through a planning stage using a treatment planning system (TPS) which requires accurate 3D volumetric imaging. On the other hand, 3D slicer itself is 3D imaging software that is commonly used in the field of radiology. Therefore, this study aims to find out how to use 3D slicer software in modeling and volumetric visualization of meningioma disease. This study uses MRI data from meningioma patients which are processed for volumetric segmentation with a 3D slicer. Based on the results obtained, it can be concluded that the 3D slicer software is effective for modeling and visualizing volumetric brain tumor disease. In this case the visualization &amp; volumetrics look clear and relatively accurate, namely a volume of 16,8402 cm3 with an accuracy of 98.5%.

Analysis of PET-CT Derived Radiomic Biomarkers with Efficacy, Safety, and Expansion of Axicabtagene Ciloleucel (Axi-Cel) in Patients with Relapsed or Refractory Large B-Cell Lymphoma (LBCL)

Introduction In LBCL, the presence of bulky disease is known to be an adverse prognostic factor, indicating poorer outcomes. The ZUMA-1 clinical trial (Neelapu, NEJM 2017) showed that high tumor burden, as measured by the sum of the product of diameters (SPD), was associated with lower durable response after Chimeric Antigen Receptor T-Cell Therapy (CAR T ). Radiomic biomarkers such as metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been recently studied to assess three-dimensional measurement of tumor activity and metabolic volume in LBCL. There is a paucity of understanding on the impact of radiomic biomarkers on outcomes and CAR T expansion kinetics in LBCL CAR T recipients. We studied the association of these radiomic biomarkers with efficacy, toxicity, and CAR T expansion in this single institution retrospective study of patients with LBCL who received axi-cel. Methods 189 patients with relapsed or refractory LBCL who were treated with axi-cel from 12/2017 to 5/2022 were studied. The closest PET-CT scan to CAR T leukapheresis date was identified for each patient. After excluding cases with unavailable pre-apheresis PET-CT scans, 172 patients were included in the statistical analysis. Two reviewers independently used a semi-automated contouring software (MIM, v. 7.1.2) to measure maximum standardized uptake value (SUV Max), mean SUV, total MTV and TLG values of malignant lesions. CD19 CAR T expansion was measured by real-time flow cytometry with anti-idiotype-FMC63 conjugated to Dylight 65013. Areas under the curve (AUCs) were calculated for CAR T expansion in the 28 days following infusion using the linear trapezoidal method on raw values, and the results were transformed using a base-10 logarithm. Patients were categorized into high and low biomarker groups based on whether their values exceeded or were below the respective median. For univariate analyses, continuous variables were compared using a Mann-Whitney U-test, categorical using a Chi-square test, and time-to-event using a log-rank test. Within a multivariable framework, regressions (linear, logistic, Cox) were fit to control for potential confounders. Results Median duration of follow up was 33 months (IQR: 17.9, 44.7). MTV was highly correlated with TLG (r=0.87). High MTV was associated with bulky disease defined as &amp;gt;10cm (p&amp;lt;0.01), high IPI at apheresis (p&amp;lt;0.04), bridging treatment (p&amp;lt;0.01), pre-lymphodepletion lactate dehydrogenase (p&amp;lt;0.01), and autologous stem cell transplant (p=0.02). Complete response (CR) rate was greater for those in the low MTV group (79.3%) compared to high MTV group (52.3%), (p&amp;lt;0.01), as well as those with high TLG (77.9%) compared to low TLG group (46%), (p&amp;lt;0.01). Low MTV was significantly associated with better progression free survival (PFS) in a univariate analysis (p=0.03); median PFS was 33.5 months (IQR 3.06, 39.55) for low MTV versus 6.1 months (IQR 6.38, NA) for high MTV. Low MTV was also significantly associated with better overall survival (OS) in a univariate analysis (p&amp;lt;0.01); median OS was not reached for the low MTV group but was 31.8 months for the high MTV group (IQR 16.5, NA) (Figure 1). After multivariable regression analysis, PFS was no longer different by MTV biomarker level ( p= 0.07), but OS was still significantly different ( p= 0.01). Neither PFS nor OS were significantly associated with the binary TLG variable. The proportion of cytokine release syndrome (CRS) or immune cell associated neurologic toxicity syndrome (ICANS) of any grade or severe grade ICANS were significantly higher for the high MTV and TLG groups as compared to their respective low groups. Length of stay was significantly longer for those in the high MTV and TLG groups (Table 1). A higher day 14 CAR T cell expansion was seen in the high TLG as compared to the low group (0.89 vs 0.63 cells/μL, p=0.002). However, there was no statistically significant difference in CAR T expansion as measured by AUC between the high and low MTV or TLG cohort. Conclusion Our study demonstrates poor outcomes for efficacy, safety, and healthcare utilization endpoints in patients with high tumor bulk as measured by volumetric tumor assessment on PET-CT. Furthermore, we show a differential peripheral day 14 CAR T expansion with higher expansion seen in patients with higher tumor bulk. Strategies like tumor debulking prior to CAR T therapy and earlier referral in patients with high tumor bulk may improve clinical outcomes.