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.

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.

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.

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.

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 K^trans 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 K^trans, with SSM K^trans 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 K^trans 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.

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.

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.

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.