ISSN# 1545-4428 | Published date: 19 April, 2024
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At-A-Glance Session Detail
   
Cancer & Treatment Response
Oral
Body
Thursday, 09 May 2024
Room 331-332
08:15 -  10:15
Moderators: Ralph Mason & Natalie Serkova
Session Number: O-12
CME Credit

08:15 Introduction
Ralph Mason
UT Southwestern, Dallas, TX, United States
08:271162.
Multi-Omics Integration of MRI Habitat, Pathology, and Clinical Parameters for Predicting Platinum Resistance of HGSOC
Qiu Bi1, Jinwei Qiang2, Yang Song3, and Yunzhu Wu3
1the First People’s Hospital of Yunnan Province, Kunming, China, 2Jinshan Hospital, Fudan University, Shanghai, China, 3MR Research Collaboration Team, Siemens Healthineers Ltd., Shanghai, China

Keywords: Cancer, Cancer

Motivation: Platinum resistance of high-grade serous ovarian carcinoma (HGSOC) is related to tumor heterogeneity. Multi-omics integration can complement tumor heterogeneity at multiple scales and  enhance the predictive power of single models.

Goal(s): We aimed to explore a range of diverse multi-omics models to predict platinum resistance of HGSOC.

Approach: Multi-omics models were  developed and validated using MRI-based habitat radiomics, pathomics based on haematoxylin and eosin (H&E)-stained whole slide images (WSIs), and clinical parameters.

Results: Among the array of single and composite models, the Clinic_Habitat model exhibited the most promising predictive performance, with the Clinic_Habitat_Pathology model ranking as the second-best performer.

Impact: This study carries the potential to equip clinicians with treatment strategies aimed at enhancing the efficacy of individualized therapy.

08:39 WITHDRAWN
08:511164.
Intra-tumor heterogeneity based on synthetic MRI in predicting Ki-67 status of nasopharyngeal carcinoma
Huanhuan Ren1, Jiuquan Zhang1, Junhao Huang1, Yao Huang1, Daihong Liu1, Jing Zhang1, Yong Tan1, Hong Yu1, and Lisha Nie2
1Chongqing University Cancer Hospital, Chongqing, China, 2GE HealthCare MR Research, Beijing, China

Keywords: Cancer, Cancer

Motivation: The staging and prognosis of nasopharyngeal carcinoma (NPC) remain significant challenges, with potential correlations to the Ki-67 proliferation status.

Goal(s): To assess and analyze an ITH model based on pre-treatment synthetic MRI (SyMRI) for predicting Ki-67 status in patients with pathologically confirmed NPC.

Approach: Twenty-eight NPC patients who underwent pre-treatment SyMRI. SyMRI data were processed to generate T1, T2, and PD maps. The ITHscore, derived from quantitative parameter maps, was utilized to establish models for predicting Ki-67 status based on clinical data.

Results: The ITHscore, based on quantitative parameter maps, demonstrated promise as an imaging marker for predicting Ki-67 status in NPC.
 

Impact: The ITHscore derived from SyMRI holds potential as a non-invasive imaging marker for predicting Ki-67 status, which can have clinical implications in the management of NPC.

09:031165.
The Impact of Pancreatic Cancer Glutamine Transporter Downregulation on Cachexia and Visceral Organ Metabolism
Raj Kumar Sharma1, Balaji Krishnamachary1, Paul Winnard1, Yelena Mironchik1, Marie France Penet1, and Zaver M. Bhujwalla1
1Department of Radiology, Division of Cancer Imaging and Reserach, The Johns Hopkins University School of Medicine, Baltimore, MD, United States., Baltimore, MD, United States

Keywords: Cancer, Cancer, PDAC, Pancreatic cancer, Glutamine tarnsporter

Motivation: We previously identified alterations in brain and plasma glutamine/ glutamate with cachexia that led us to downregulate the glutamine transporter, SLC1A5, in the cachexia-inducing patient derived Pa04C PDAC cells.

Goal(s): Targeting the glutamine transporter represents a promising approach to delay tumor progression and establish a novel treatment strategy in PDAC cachexia. 

Approach: We performed 1H MRS to determine the metabolic changes in multiple organs of mice.

Results: We identified metabolic differences in organs of mice bearing SLC1A5 downregulated tumors compared to wild type or empty vector tumors. Our data identify SLC1A5 as a  target to reduce PDAC induced cachexia and  associated pathways   

Impact: By detecting these visceral organ metabolic changes we identified potential  metabolic pathways that can be targeted to reduce cachexia.

09:151166.
Multi-parametric MRI for Response Assessment in Soft-Tissue Sarcoma; Post-Treatment EF and ADC Correlate with Viable Tumour Percentage
Imogen Thrussell1,2, Jessica M Winfield1,2, Khin Thway3,4, Sadiq Usman2, Jennifer Newman2, Georgina Hopkinson2, Amy Ho Ching Wong5, Andrew Hayes6, Shane Zaidi1,4, Aisha Miah1,4, Christina Messiou1,2, and Matthew David Blackledge1,2
1Department of Radiotherapy and Imaging, The Institute of Cancer Research, London, United Kingdom, 2MRI Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom, 3Division of Molecular Pathology, The Institute of Cancer Research, London, United Kingdom, 4Sarcoma Unit, The Royal Marsden NHS Foundation Trust, London, United Kingdom, 5Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, Hong Kong, 6Department of Surgery, The Royal Marsden NHS Foundation Trust, London, United Kingdom

Keywords: Treatment Response, Cancer, Multi-parametric, Response, Quantitative

Motivation: New biomarkers are needed for response assessment of soft-tissue sarcoma (STS) that reflect underlying biology.

Goal(s): To (i) describe changes in six quantitative MRI biomarkers following radiotherapy treatment, (ii) assess correlation between changes in these markers, and (iii) evaluate correlation of post-treatment values with viable tumour percentage (VTP) after resection.

Approach: We evaluate the Pearson correlation between changes in all six biomarkers in a cohort of 23 patients treated with pre-operative radiotherapy for limb sarcoma.

Results: Large correlations are observed in changes of T2, ADC, fractional-anisotropy, fat-fraction and magnetization-transfer-ratio. Post-treatment values of tumour enhancement and ADC reflect VTP.

Impact: Multiparametric quantitative MR protocols capture heterogeneous changes in soft-tissue sarcomas following treatment. Changes in derived quantitative biomarkers following treatment are correlated, and post-treatment values may reflect viable tumour percentage determined through histopathology.

09:271167.
Technical Considerations for Implementing Multi-Center and Multi-Platform Quantitative DCE-MRI to Predict Breast Cancer Therapy Response
Brendan Moloney1, Xin Li1, Michael Hirano2, Assim Saad Eddin3, Jeong Youn Lim1, Debosmita Biswas2, Anum S. Kazerouni2, Alina Tudorica1, Isabella Li2, Mary Lynn Bryant2, Courtney Wille3, Chelsea Pyle1, Habib Rahbar2, Su Kim Hsieh3, Travis Rice-Stitt1, Suzanne Dintzis2, Amani Bashir3, Evthokia Hobbs1, Alexandra Zimmer1, Jennifer Specht2, Sneha Phadke3, Nicole Fleege3, James H. Holmes3, Savannah C. Partridge2, and Wei Huang1
1Oregon Health & Science University, Portland, OR, United States, 2University of Washington, Seattle, WA, United States, 3University of Iowa, Iowa City, IA, United States

Keywords: Treatment Response, Quantitative Imaging, Multi-Center and Multi-Vendor Platform, DCE-MRI, Breast Cancer, Therapy Response, Ktrans

Motivation: Determine best-practice quantitative DCE-MRI for predicting breast cancer (BC) response to neoadjuvant chemotherapy (NAC) in a multi-center (MC) and multi-vendor platform (MP) setting.

Goal(s): Evaluate effects of different pharmacokinetic analysis approaches on Ktrans and its predictive performance.

Approach: 15 BC patients treated with NAC underwent longitudinal DCE-MRI at 3 sites using 3T systems from 3 vendors. Variations in analysis included Tofts model vs. Shutter-Speed model (SSM), ROI- vs. voxel-based analysis, and using fixed vs. measured R10.

Results: Different analysis approaches resulted in significantly different Ktrans, with SSM Ktrans from voxel-based analysis using fixed R10 showing highest predictive accuracy for response.

Impact: Voxel-based SSM analysis using fixed R10 takes advantage of greater range of SSM Ktrans changes in response to therapy, mitigates R10 measurement errors, and may be the best-practice quantitative DCE-MRI for predicting NAC response in a MC and MP setting.

09:391168.
Predicting the response of I-SPY 2 breast cancer patients to treatment using a biology-based mathematical model calibrated with quantitative MRI
Reshmi J. S. Patel1, Chengyue Wu2,3,4,5,6, Casey E. Stowers3, Rania M. Mohamed7, Jingfei Ma2, Gaiane M. Rauch4,8, and Thomas E. Yankeelov1,2,3,9,10,11
1Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX, United States, 2Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 3Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, United States, 4Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 5Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 6Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 7Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 8Department of Abdominal Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, United States, 9Department of Diagnostic Medicine, Dell Medical School, Austin, TX, United States, 10Department of Oncology, Dell Medical School, Austin, TX, United States, 11Livestrong Cancer Institutes, Dell Medical School, Austin, TX, United States

Keywords: Cancer, Modelling, Computational Oncology, Breast Cancer, Treatment Response, Tumor Prediction

Motivation: Optimizing treatment to improve outcomes necessitates a robust tool to accurately predict breast cancer response on a patient-specific basis.

Goal(s): We are applying our biology-based mathematical model to I-SPY 2 breast cancer patients to test if its predictive ability generalizes to multi-site data.

Approach: Quantitative contrast-enhanced and diffusion-weighted MRI data collected early during treatment were used to calibrate a mathematical model describing tumor cell movement, proliferation, and response. After calibration, the model predicts tumor status after the treatment regimen.

Results: The concordance correlation coefficient between the measured and predicted 9-week change was 0.91 for tumor cellularity and 0.88 for tumor volume.

Impact: The high degree of agreement between measured and predicted changes in tumor cellularity and volume in the I-SPY 2 dataset indicates that our biology-based mathematical model can potentially make accurate predictions using MRI data from multiple clinical sites.

09:511169.
Prospective Determination of Tumor Regression Grade with Magnetic Resonance Imaging in Neoadjuvant Chemotherapy for Rectal Adenocarcinoma
Yu Shen1, Xiaoling Gong2, Meng Qiu1, Wenjian Meng1, and Ziqiang Wang1
1Colorectal Cancer Center, Department of General Surgery, West China Hospital, Chendu, China, 2Department of Radiology, West China Hospital, Chendu, China

Keywords: Cancer, Cancer, Rectal cancer; Neoadjuvant chemotherapy; Complete response; Magnetic resonance tumor regression grade; Diffusion-weighted imaging.

Motivation: The role of MRI in evaluating the tumor response following neoadjuvant chemotherapy (NCT) in rectal cancers remains pending.

Goal(s): To investigate the reliability of MRI in assessing the pathological clinical response (pCR) in rectal cancer patients with NCT.

Approach: In two consecutive prospective clinical trials (Clinicaltrials.gov NCT03666442 and NCT04922853), tumor responses to NCT were evaluated using MRI-based models.

Results: 224 patients were enrolled. MR-TRG, DWI, DWImodMR-TRG mriCR, and rNAR score were all associated with pCR. DWImodMR-TRG achieved the highest area under the curve (AUC) of 0.940, with the highest sensitivity of 0.905 and the highest PPV of 0.976.

Impact: MRI-based models were feasible in determining the tumor response in LARC patients following NCT. DWI may improve the predictive performance of MR-TRG. Our findings provide evidence for the determination of tumor response for rectal cancer patients who underwent NCT.

10:031170.
The feasibility study of multiple functional imaging modalities in the differential diagnosis of benign and malignant bone tumors
Ying Li 1, Cuiping Ren1, Wenhua Zhang1, Yong Zhang1, Jingliang Cheng1, Dandan Zheng2, and Liangjie Lin2
1The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, 2Clinical & Technical Support, Philips Healthcare, Beijing, 100102, China, Beijing, China

Keywords: Cancer, Tumor

Motivation: The combination of multi-MRI techniques in the differential diagnosis of benign and malignant bone tumors represents a novel endeavor.

Goal(s): To investigate the utility of combining DWI, IVIM, DKI and APTWI in the differential diagnosis of benign and malignant bone tumors.

Approach: Relevant parameters of 45 patients were statistically compared through either the independent samples t-test or Mann-Whitney U test. Diagnostic performance was assessed using ROC curves for both individual examinations and their combined analysis in distinguishing between benign and malignant tumors.  

Results: The combination of multi-MRI techniques proves to be a more effective approach in distinguishing between benign and malignant bone tumors. 

Impact: Multimodal MRI provides biological and pathological information about the tumor cell microenvironment, and their combination proves to be a more effective approach in distinguishing between benign and malignant bone tumors.