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Keni Zheng¹, Mengsi Li², Yi Sui¹, Xiang Shan¹, Emi Hojo¹, Armando Manduca³, Richard Ehman¹, Jin Wang², and Ziying Yin^1
1Radiology, Mayo Clinic, Rochester, MN, United States, ²The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China, ³Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, United States

Keywords: Liver, Liver, MRI, MR Elastography (MRE), HCC, VETC, Radiomics, texture features

Motivation: Vessels encapsulating tumor clusters (VETC) is a powerful indicator of aggressive Hepatocellular carcinoma (HCC) associated with recurrence, which is expected to be noninvasively identified using imaging techniques.

Goal(s): This study aims to develop a potential mechanical biomarker utilizing radiomics features extracted from  MR Elastography (MRE)-assesed peritumoral shear strain to predict VETC in HCC.

Approach: Radiomics features were extracted from the tumor boundary on an octahedral shear strain (OSS) map. Feature selection techniques were used to identify relevant features to construct a radiomics score.

Results: Three radiomics features were utilized to construct a radiomics score, demonstrating potential for MRE-based strain analysis in predicting VETC.

Impact: A radiomics score constructed by radiomics features derived from MRE-based strain analysis in the peritumoral region show promise in predicting VETC in HCC patients. This study results show non-invasive VETC prediction potential in HCC, impacting personalized treatment and patient outcomes.

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Longyu Sun¹, Yikun Wang², Xumei Hu¹, Xueqin Xia³, Mengting Sun¹, Qing Li¹, Meng Liu¹, Yinghua Chu⁴, Xinyu Zhang¹, Ruokun Li², and Chengyan Wang^1
1Human Phenome Institute, Fudan university, Shanghai, China, ²Shanghai Jiao Tong University School of Medicine, Shanghai, China, ³Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China, ⁴Simens Healthineers Ltd, Shanghai, China

Keywords: Liver, Liver, DWI, MRE, Liver fibrosis, RegGAN, CBAM

Motivation: Liver biopsy is the standard clinical approach for diagnosing liver fibrosis. However, its invasiveness and possibility of sampling errors impose inherent limitations.

Goal(s): To evaluate the reproducibility and reliability of the virtual MRE based on DWI and compare it with MRE to assess its efficiency in diagnosing liver fibrosis.

Approach: RegGAN was employed to forecast MRE and rectify the outcomes. And CBAM was utilized to quantify the influence of diverse b-values in DWI.  

Results: RegGAN-CBAM demonstrates favorable performance in both image and stiffness prediction of MRE. The liver fibrosis grading based on stiffness predictions demonstrates a high level of accuracy and sensitivity.

Impact: The prediction methodology of 3D MRE based on DWI exhibits a notable level of diagnostic efficiency and reliability. This approach serves as a non-invasive method with practical applicability in the clinical assessment of liver fibrosis.

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Zhiying Wang¹, Minghui Zhou¹, Chen Pan¹, Baihe Luo¹, Jialin Li¹, Qiang Liu¹, and Yu Shi^1
1Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China

Keywords: Liver, Elastography

Motivation: Hepatic venous pressure gradient (HVPG) is the gold standard for diagnosing portal hypertension, but it is invasiveness, cost, and feasibility.

Goal(s): To develop a non-invasive model based on Three-dimensional (3D) MR elastography (3D-MRE) to detect portal hypertension.

Approach: Spearman correlation analysis between 3D-MRE parameters and HVPG; Multivariable linear regression analysis between mechanical parameters and HVPG; Logistic regression analysis and establish a model to diagnose portal hypertension. 
 
 

Results: 3D-MRE is a non-invasive, rapid, and highly accurate tool for predicting portal hypertension. Especially Spleen stiffness at 60Hz was the independent parameters associating HVPG.

Impact: This indicates that the use of 3D-MRE can provide more personalized evaluation and better medical experience for patients with cirrhosis and provides a new method for non-invasive diagnosis of portal hypertension.

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Yongjian Zhu¹, Wei Cai¹, Yueluan Jiang², and Liming Jiang^1
1Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, ²MR Research Collaboration, Siemens Healthineers, Beijing, China

Keywords: Digestive, Elastography, gastrointestinal; Cancer

Motivation: Although neoadjuvant chemotherapy (NAC) was recommended for gastric cancer (GC), only about 40% patients could achieve pathological response. Accurate prediction of the NAC response is crucial for patients’ benefits.

Goal(s): To evaluate the predictive performance of virtual MR elastography (vMRE) and extracellular volume (ECV) for predicting the response to of NAC in GC patients.

Approach: Patients underwent DWI-based elastography, pre- and post-contrast T1 mapping before treatment. DWI-based virtual shear modulus (μDiff) and ECV were calculated from DWI and T1 mapping.

Results: Both μDiff (AUC: 0.833) and ECV (AUC: 0.794) were independent predictors for NAC response, their combination could improve the AUC to 0.968.

Impact: Our result revealed that DWI-based virtual shear modulus (μDiff) and ECV exhibited a promising predictive ability for predicting response to NAC. This would aid in identifying responders before treatment, reducing unnecessary toxicity and side effects, and guiding individualized treatment.

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Zhiyong Chen¹, Zhangli Xing², Enshuang Zheng¹, Mingcong Luo², Caixia Fu³, Guijin Li⁴, Thomas Benkert⁵, Yunjing Xue¹, and Bin Sun^1
1Radiology, Fujian Medical University Union Hospital, Fuzhou, China, ²Fujian Medical University Union Hospital, Fuzhou, China, ³MR Application Development, Siemens Shenzhen magnetic Resonance Ltd., Shenzhen, China, Shenzhen, China, ⁴MR application, Siemens Healthineers Ltd，Guangzhou，China, Guangzhou, China, ⁵MR Application Predevelopment, Siemens Healthineers AG, Erlangen, Germany, Erlangen, Germany

Keywords: Liver, Diffusion/other diffusion imaging techniques

Motivation: A free-breathing motion-insensitive and high-quality DWI is desired for abdominal DWI in clinical practice.

Goal(s): This study is to propose and evaluate a novel ss-EPI DWI sequence incorporating motion correction , complex averaging , and a combination of a reparametrized sinc fatsat pulse with an optimized water excitation pulse  for abdominal DWI.

Approach: Four different DWI sequecnes were performed on volunteers. The overall image qualities and the SNR on the liver papernchyma were evaluated and compared.

Results: The SNRs of the liver and the image qualities were significantly higher with the proposed Free-breathing motion-corrected DWI ( MOCO-DWI ) sequence compared to other EPI DWI sequences.

Impact: The free-breathing MOCO-DWI sequences with complex-averaging and new fat suppression scheme is recommended for high-quality liver DWI in clinical routine. 

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Ping Liu¹, Yingying Song², Wanyi Zhen¹, and Guihua Jiang^1
1Department of Medical Imaging,, Guangdong Second Provincial General Hospital, Guangzhou, China, ²Department of Radiology, Affiliated Hospital of Jianghan University, Wuhan, China

Keywords: Endocrine, Pancreas, Type 2 diabetes mellitus

Motivation: Early detection of damaged β-cell function may help timely protect and stop the progression of hyperglycemia to type 2 diabetes mellitus (T2DM). The impaired β-cell function may be associated with damaged pancreatic microstructure. 

Goal(s): The pancreatic microstructural changes may serve as the biomarker for β-cell dysfunction. 

Approach: We evaluate the microstructural changes of the pancreas in patients with hyperglycemic employing intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and explore its correlation with the β-cell function. 

Results: IVIM-DWI can effectively distinguish T2DM from hyperglycemia, it has the potential for identifying damaged b-cell function for patients with early-stage hyperglycemia but without obvious clinical manifestation of DM.

Impact: IVIM-DWI of pancreases is a reliable and non-invasive tool with great potential in detecting the early damaged β-cell function when the DM is still in the insidious stage, and help for improving diabetes diagnosis and management.

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Chenglin Zhu¹, Xinzi He², Zhongxiu Hu¹, Hreedi Dev¹, Dominick J. Romano¹, Arman Sharbatdaran¹, Anna Prince¹, Andrea Soto Figueroa¹, Sophie J. Wang¹, Hui Yi Ng He¹, Jon D. Blumenfeld³, and Martin R. Prince^1,4
1Weill Cornell Medicine, New York City, NY, United States, ²Cornell University and Cornell Tech, New York City, NY, United States, ³The Rogosin Institute, New York City, NY, United States, ⁴Columbia University Vagelos Collage of Physicians and Surgeons, New York City, NY, United States

Keywords: Kidney, Segmentation, ADPKD

Motivation: Abdominal organ volumes are critical MRI biomarkers in many diseases including autosomal dominant polycystic kidney disease.  

Goal(s): We aim to develop a segmentation model with an enhanced ability to generalize across various abdominal organs and MR pulse sequences.

Approach: We construct a multi-modality abdominal foundation model expanding upon our existing ADPKD kidney model which adapts to diverse organs and tissues with minimal new training data.

Results: The model was trained using a model-in-loop methodology and evaluated against radiologist benchmarks, yielding an impressive Dice score of 0.94 for in-distribution sequences and 0.73 for organ segmentations on out-of-distribution sequences.

Impact: This foundational model can seamlessly integrate into clinical workflows, utilizing routine cases to enhance its performance and extending its application to additional organs and tissues. This advance also marks a significant step toward the automation of MRI reporting.

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Yiming Dong¹, David Atkinson², Kirsten Koolstra³, Matthias J.P. van Osch¹, and Peter Börnert^1,4
1C.J. Gorter MRI Center, Department of Radiology, LUMC, Leiden, Netherlands, ²Centre for Medical Imaging, University College London, London, United Kingdom, ³Philips, Best, Netherlands, ⁴Philips Research Hamburg, Hamburg, Germany

Keywords: Prostate, Prostate

Motivation: Conventional single-shot EPI (ssh-EPI) is fast, but often causes geometric distortions, especially near the rectum. 

Goal(s): In this research, a Dixon-msh-EPI technique is validated for prostate Diffusion-weighted Imaging (DWI). 

Approach: Dixon-msh-EPI is proposed to reduce such distortions, while jointly separating water/fat components by a special structured low-rank reconstruction that also corrects shot-to-shot phase variations.

Results: Experiments were conducted on 7 healthy male volunteers using a 3T scanner comparing DW-ssh-EPI and Dixon-msh-EPI performance. The Dixon-msh showed significantly reduced geometric distortion and effective fat signal suppression, as scored by two readers. While both methods provided comparable image quality, Dixon-msh-EPI demonstrated improved motion-correction and geometric accuracy. 

Impact: Dixon-msh-EPI offers improved prostate DWI by significantly reducing geometric distortions and enhancing across b-value registration. This technique could lead to more accurate diagnoses and has the potential to refine clinical MRI protocols, emphasizing precision in prostate diffusion imaging.

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Tsutomu Tamada¹, Yu Ueda², Mitsuru Takeuchi³, Atsushi Higaki⁴, Yuichi Kojima⁴, Yoshihiko Fukukura⁴, and Akira Yamamoto^4
1Radiology, Kawasaki Medical School, Kurashiki, Japan, ²Philips Japan, Tokyo, Japan, ³Radiolonet Tokai, Nagoya, Japan, ⁴Kawasaki Medical School, Kurashiki, Japan

Keywords: Prostate, Prostate

Motivation: The detection rate of csPCa in PI-RADS 3 lesions is only 25-38%. This leads to unnecessary biopsies.

Goal(s): Can multiparametric quantitative maps based on estimated T1 and T2 help differentiate clinically significant prostate cancer (csPCa) from non-csPCa in PI-RADS 3 lesions?

Approach: We compare T1, T2, and ADC obtained from Multiple-Repetition time Multiple- Echo time (MRME) based DWI (MRME-DWI) between csPCas and non-csPCas in PI-RADS 3 lesion using MRI-ultrasound fusion targeted biopsy as the reference standard.

Results: The T1, T2, and ADC were significantly lower for csPCas than non-csPCas. Combining these indices yields an AUC of 0.82.

Impact: MRME-DWI can simultaneously obtain T1, T2, and ADC from a single region of interest in a single imaging session. Combining these quantitative measures is expected to improve the detection rate of clinically significant prostate cancer in PI-RADS 3 lesions.

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Sheng Yu¹, Antonio Candito¹, Konstantinos Zormpas-Petridis ^1,2, Ana Ribeiro^1,3, Georgina Hopkinson³, Jessica M Winfield^1,3, Nina Tunariu^1,3, Christina Messiou^1,3, Dow-Mu Koh^1,3, and Matthew David Blackledge^1
1Institute of Cancer Research, London, United Kingdom, ²Fondazione Policlinico Universitario Agostino Gemelli, Rome, Italy, ³The Royal Marsden NHS Foundation Trust, London, United Kingdom

Keywords: Cancer, Whole Body, Metastasis, Fractional Anisotropy

Motivation: Whole-body diffusion-weighted imaging in oncology assumes isotropic diffusion therefore that data probing the directional dependence of diffusion in body cancer applications is scarce.
 

Goal(s): To report the distribution of fractional anisotropy (FA) values in metastatic bone disease for a cohort of patients who underwent multi-directional diffusion weighted imaging.
 

Approach: Deep learning was used to auto-segment metastatic lesions; FA values are calculated for each segmented region and the distributions reported.
 

Results: FA distributions from 85 patients with metastatic disease (50 prostate, 33 breast) are reported and are deemed to be low (mean FA range breast group: 0.2179 - 0.3132; prostate group: 0.2866 - 0.3717).
 

Impact: We have conducted diffusion tensor imaging analysis for whole body DWI data and carried out the first bone lesion FA comparison study. This work provides a rationale for choice of diffusion-weighting gradient schemes in clinical imaging. 

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Rie Ota¹, Masako Kataoka², Mami Iima², Maya Honda³, Aika Okazawa², Mizue Suzuki¹, Shotaro Kanao¹, Takeshi Kubo¹, Yosuke Yamada⁴, Yasuhide Takeuchi⁴, Masahiro Takada⁵, and Yuji Nakamoto^2
1Department of Radiology, Tenri Hospital, Nara, Japan, ²Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University graduate school of medicine, Kyoto, Japan, ³Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka, Japan, ⁴Department of Diagnostic Pathology, Kyoto University Hospital, Kyoto, Japan, ⁵Department of Breast Surgery, Kyoto University Hospital, Kyoto, Japan

Keywords: Breast, Breast

Motivation:   MRI is expected to be a new surrogate marker of prognosis that can replace pathological complete response (pCR) for breast cancer patients who underwent neoadjuvant systemic treatment (NST).

Goal(s):   MRI is expected to be a new surrogate marker of prognosis that can replace pathological complete response (pCR) for breast cancer patients who underwent neoadjuvant systemic treatment (NST)

Approach:   Survival analysis was performed for disease-free survival using Kaplan-Meier method.

Results: DWI score after NST of breast cancer was associated with DFS, in particular triple-negative, HER2-positive, and luminal-HER2 subtype. 

Impact: The DWI / kinetic score obtained from MRI after neoadjuvant systemic treatment (NST) were associated with disease-free survival (DFS), especially among triple-negative, HER2-positive, and luminal/HER2 subtype. The DWI score may be a biomarker for prognosis of breast cancer patients.

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Patrick J Bolan¹, Wen Li², Bonnie N Joe², Nu Le², Elissa Price², Jessica Gibbs², Lisa J Wilmes², Debsmita Biswas³, Anum Kazerouni³, An L Church⁴, Elizabeth S McDonald⁵, Stephane Loubrie⁶, Rebecca Rakow-Penner⁶, Hon J Yu⁶, Dariya Malyarenko⁷, Thomas L Chenervert⁷, Beatriu Reig⁸, Nola M Hylton², and Savannah Partridge^3
1Center for MR Research / Radiology, University of Minnesota, MINNEAPOLIS, MN, United States, ²Radiology, University of California San Francisco, San Francisco, CA, United States, ³University of Washington, Seattle, WA, United States, ⁴Radiology, University of Minnesota, MINNEAPOLIS, MN, United States, ⁵Radiology, University of Pennsylvania, Philadelphia, PA, United States, ⁶Radiology, University of Californa San Diego, San Diego, CA, United States, ⁷Radiology, University of Michigan, Ann Arbor, MI, United States, ⁸Radiology, New York University, New York, NY, United States

Keywords: Breast, Treatment, Cancer, Treatment Response

Motivation: Accurate imaging markers to establish pathologic complete response (pCR) during neoadjuvant chemotherapy (NACT) could enable therapy de-escalation to avoid excessive systemic treatments

Goal(s): To determine if quantitative diffusion-weighted MRI (DWI) can accurately detect pCR following NACT.

Approach: In the ACRIN 6698/I-SPY 2 multicenter trial dataset, tumor region apparent diffusion coefficient (ADC) from DWI was measured on post-NACT/presurgical MRIs. The accuracy of ADC for predicting pCR, alone and in combination with functional tumor volume (FTV) from contrast-enhanced MRI, was assessed. 

Results: In multivariate models ADC accurately predicts pCR, and is influenced by field strength, spatial resolution, and lesion morphology.

Impact: Apparent diffusion coefficient measured by DWI shows promise for determining absence of residual disease following chemotherapy and may provide a non-contrast option for tailoring therapies and enabling patients to avoid unnecessary prolongation of treatment.

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Wanli Zhang^1,2, Fangrong Liang^1,2, Jiamin Li^1,2, Yongzhou Xu³, Xin Zhen⁴, Ruimeng Yang^1,2, and Xinqing Jiang^1,2
1Department of Radiology, The Second Affiliated Hospital, School of Medicine, South China University of Technology, Guangzhou, China, ²Department of Radiology, Guangzhou First People's Hospital, Guangzhou, China, ³Philips Healthcare, Guangzhou, China, ⁴School of Biomedical Engineering, Southern Medical University, Guangzhou, China

Keywords: Breast, Cancer, Breast cancer, MRI, Triple-negative breast cancer, Tumor habitat

Motivation:  Investigated the role of quantitative stratified apparent diffusion coefficient (ADC)-defined tumor habitats in differentiating triple-negative breast cancer (TNBC) from non-TNBC using a multiparametric MRI (mpMRI)-based feature fusion radiomics (R_FF) approach.

Goal(s): To develop an R_FF-Stratified_ADC model using an R_FF strategy and reveal distinct ADC map–based tumor habitats for distinguishing TNBC.

Approach:  R_FF (predominant MRI sequence–based fused features), R_ADC (ADC radiomics features), Stratified_ADC (stratified ADC–defined tumor habitat parameters), and combined R_FF-Stratified_ADC models were constructed to identify TNBC.

Results: Stratified ADC parameters helped evaluate the underlying biological proliferation and cellularity within tumor habitats. The integrated R_FF-Stratified_ADC model was effective and reliable for TNBC identification.

Impact: Stratified ADC–defined tumor habitat parameters derived from whole-tumor ADC maps, along with fused radiomics features from dominant mpMRI sequences (T2WI, DWI, ADC maps, and DCE₂), can serve as potential biomarkers for differentiating TNBC from non-TNBC.

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Xiaoyan Wang¹, Yan Zhang¹, Jingliang Cheng¹, Liangjie Lin², Zhigang Wu², Peng Sun², Ying Hu¹, Anfei Wang¹, Ruhua Wang¹, Yong Zhang¹, Ying Li¹, Kun Zhang¹, and Wenhua Zhang^1
1The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²Philips Healthcare, Beijing, China

Keywords: Breast, Breast

Motivation: Breast cancer is a complex and heterogeneous disease, and its different subtypes show very different biological characteristics, which will also affect the choice of treatment.

Goal(s): The objective of this study was to explore the value of APTWI combined with td-MRI in evalution of breast tumors.

Approach: The performance of APTWI  was compared to conventional td-MRI in differentiation between benign and malignant breast tumors.

Results: APTWI overperform td-MRI in differentiation between benign and malignant breast tumors. And ADC values derived from td-MRI at different gradient oscillation frequencies show potential diagnostic values in predicting different risk factors in breast cancer.

Impact: These may provide new ideals for the diagnosis, treatment and prognosis evaluation of breast cancer.

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Lisa J Wilmes¹, Wen Li¹, Judith Zimmermann^1,2, David C Newitt¹, Dariya I Malyarenko³, Jiachao Liang¹, Patrick J Bolan⁴, Savannah C Partridge⁵, I-SPY 2 Investigator Network⁶, Thomas L Chenevert⁷, and Nola M Hylton^1
1Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, United States, ²Radiology, Stanford University, Stanford, CA, United States, ³Radiology, University of Michigan, Ann Arbor, MI, United States, ⁴Radiology, University of Minnesota, Minneapolis, MN, United States, ⁵Radiology, University of Washington, Seattle, WA, United States, ⁶Quantum Leap Healthcare, San Francisco, CA, United States, ⁷University of Michigan, Ann Arbor, MI, United States

Keywords: Breast, Diffusion/other diffusion imaging techniques, gradient nonlinearity correction, breast cancer, ADC

Motivation: MRI scanner gradient non-linearity (GNL) is a known source of variability and spatially-dependent bias in quantitative ADC measurements derived from diffusion-weighted imaging (DWI).

Goal(s): Evaluate the effects of GNL correction on DWI from the ACRIN 6698 multi-center trial, which investigated ADC as a marker breast cancer response in patients receiving neoadjuvant therapy.

Approach: Retrospective GNL correction was performed on the ACRIN 6698 DWI data

Results:  Percent decrease in mean tumor ADC post-GNC ranged from 0.5%-11% for different MRI gradient sets, illustrating that GNL can confer significant bias to ADC measurements and should be corrected in multi-center clinical DWI trials.

Impact: Implementation of gradient non-linearity correction to reduce bias and variability of tumor ADC measurements from DWI data acquired in multi-center, multi-platform oncology trials may improve the quantitative utility of ADC for characterizing response to treatment.

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Jie Lu¹, Xue Li^2,3, Haotian Li¹, Kuiyuan Liu¹, Chunmei Li^2,3, Min Chen^2,3, and Dan Wu^1
1Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ²Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China, ³Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China

Keywords: Breast, Tumor, diffusion-time-dependence

Motivation: Diffusion-time-dependent diffusion MRI (td-dMRI) has potential in noninvasive mapping of breast tumor microstructure. However, its diagnostic value in differentiating benign and malignant tumors remains unclear, especially including transcytolemmal water exchange.

Goal(s): To investigate the clinical value of td-dMRI-based microstructural mapping for discriminating benign and malignant breast tumors.

Approach: Time-dependent dMRI data acquired with OGSE and PGSE sequences were estimated with the JOINT models.

Results: The td-dMRI-based f_in, cellularity and τ_in showed significant group differences between benign and malignant breast tumors. The combination of cellularity and τ_in indices achieved the highest diagnostic performance, with an accuracy of 94% and AUC of 0.958.

Impact: The diagnostic performance of cellularity-τ_in obtained from td-dMRI was comparable with or superior to previous studies. The 7.5-minute protocol is translatable to clinical diagnostics.

Mi Zhou¹, Meining Chen², Qin Zhang³, and Longlin Yin^1
1Department of Radiology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China, ²MR Research Collaboration, Siemens Healthineers, Chengdu, China, ³MRI clinical application, Customer Service Department, Siemens Digital Medical Technology Co., LTD, Shanghai, China

Keywords: Treatment Response, Treatment

Motivation: Although it is challenging to predict LARC responsiveness to nCRT, the potential of emerging non-Gaussian DWI models for this purpose remains unexplored.

Goal(s): To assess the efficacies of mono-exponential ADC and various non-Gaussian DWI models, including SEM, FROC, and CTRW, in predicting LARC responsiveness to nCRT.

Approach: This prospective study included 103 LARC patients. Various DWI models were assessed, and post-surgery histopathology was utilized to classify patients based on responsiveness to nCRT.

Results: Non-Gaussian models, especially CTRW parameters, demonstrated robust capacity to predict both pCR and T-downstaging. The combination of CTRW parameters yielded the best diagnostic performance.
 

Impact: This study demonstrated the potential for novel non-Gaussian DWI models to enhance predictions of LARC responsiveness to nCRT, facilitating optimized treatment plans and encouraging further research in precision oncology.

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Xiaoling Gong¹, Yu Shen², Xiaoxiao Zhang³, Bing Wu¹, and Bin Song^1
1Departments of Radiology, West China Hospital, Sichuan University, Guoxue Xiang No. 37, Chengdu, Sichuan, 610041, PR China, Chengdu, China, ²Colorectal Cancer Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China, Chengdu, China, ³Department of Clinical, Philips Healthcare, China, Wuhan, China

Keywords: Pelvis, Diffusion/other diffusion imaging techniques, rectal cancer；neoadjuvant treatment；pathological complete response；oscillatory gradient spin echo

Motivation: Time-dependent (TD) magnetic resonance imaging (MRI), an emerging imaging modality, offers insights into cellular microstructures, but its relevance to rectal cancer's neoadjuvant treatment response remains unknown.

Goal(s): To determine the value of TD MRI-derived metrics for preoperatively predicting pathological complete response (pCR) in rectal cancer.

Approach: Univariate and multivariate logistic regression analyses were used to identify key predictors of pCR.

Results: Extracellular diffusivity independently predicted pCR, with a cutoff of 1.640 effectively distinguishing between pCR and non-pCR tumors.

Impact: Time-dependent magnetic resonance imaging emerges as a promising tool for detecting the pathological complete response in rectal cancer following neoadjuvant therapy, potentially facilitating the selection of patients who may benefit from a watch-and-wait approach.

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Fang Wang¹, Lianhua Cheng¹, Lingyu Chang¹, Dmytro Pylypenko², Weiqiang Dou², Dexin Yu¹, and Qing Wang^1
1Qilu Hospital of Shandong University, Jinan city, Shandong province, China, ²GE Healthcare, Beijing, China, Beijing, China

Keywords: Uterus, Cancer, Amide proton transfer-weighted imaging; T2 mapping ; endometrioid adenocarcinoma; risk stratification

Motivation: Preoperative risk stratification of endometrioid adenocarcinoma (EA) impacts the choice of operative modality and prognosis of patients. 

Goal(s): We aimed to perform an accurate and non-invasive preoperative risk stratification method for EA by MRI sequences.

Approach: APTw imaging as well as T2 mapping were included in this study.

Results: The APTw and T2 values showed significant differences between the low- and non-low-risk groups. Combining APTw with T2 mapping achieved the highest diagnostic efficacy in the preoperative risk stratification. 

Impact: This suggested that the integrated use of APTw and T2 mapping can be an effective method for the preoperative risk assessment of EA.

Yu Zheng¹ and Jing Zhang^1
1Lanzhou University Second Hospital, Lanzhou, China

Keywords: Lung, Cancer

Motivation: Early detection in poor responders to chemo‑immunotherapy for non-small cell lung cancer (NSCLC) facilitates timely adjustment of treatment strategies.

Goal(s): To explore the value of histogram analysis based on intravoxel incoherent motion (IVIM) and difusion kurtosis imaging (DKI) in predicting chemo‑immunotherapy response in advanced NSCLC.

Approach: 72 NSCLC patients underwent pre-treatment MRI examination. Histogram parameters of IVIM and DKI were calculated and compared. 

Results: Compared with non-responders, ADC, D, Dapp were significantly lower and f was higher in responders (all p <0.05). The multivariate logistic regression model performed the best with an AUC of 0.954.

Impact: IVIM and DKI imaging would predict chemo-immunotherapy response in advanced NSCLC at initial state, which could help make clinical individualized treatment strategies.