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Shannon L Taylor¹, Michael D Pridmore², Maria E Garza^2,3, Vanessa Crain², Sheau-Chiann Chen⁴, Alaina J Brown⁵, Yu Luo², Paula MC Donahue^6,7, Manus J Donahue^3,8, and Rachelle L Crescenzi^1,2
1Biomedical Engineering, Vanderbilt University, Nashville, TN, United States, ²Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States, ³Neurology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁴Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States, ⁵Obstetrics and Gynecology; Division of Gynecologic Oncology, Vanderbilt University Medical Center, Nashville, TN, United States, ⁶Physical Medicine and Rehabilitation, Vanderbilt University Medical Center, Nashville, TN, United States, ⁷Dayani Center for Health and Wellness, Vanderbilt University Medical Center, Nashville, TN, United States, ⁸Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, United States

Keywords: Cancer, Non-Proton, lymphedema, fibrosis, sodium, inflammation, adipose, lymph

Advanced-stage lymphedema is a disease of chronic fluid accumulation and tissue inflammation that results in fibrosis and subcutaneous fat expansion in affected limbs. Assessment of fat and fibrosis currently relies on biopsies or non-specific bedside measures. Noninvasive MRI could be important for understanding pathophysiology and evaluating emerging therapies. We cross-sectionally evaluated multi-nuclear proton and sodium MRI in patients with lower-extremity secondary lymphedema. Results reveal subcutaneous adipose tissue expansion throughout the affected calf, locoregional fibrosis, and elevated sodium content in fibroadipose tissues particularly in the anterior calf, thereby highlighting the relevance of these technologies for interrogating tissue health in lymphedema.

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James Dion Barnett¹, Marie-France Penet^1,2, Santosh Kumar Bharti¹, Yelena Mironchik¹, Balaji Krishnamachary¹, and Zaver M Bhujwalla^1,2,3
1The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, United States, ³Department of Radiation Oncology and Molecular Radiation Sciences, Baltimore, MD, United States

Keywords: Cancer, Preclinical, Spleen, MDSC, Cancer, COX-2

 Tumor COX-2 expression in breast cancer has been associated with increased aggressiveness.  Here we have investigated the effects of tumor COX-2 levels on spleen metabolism and on the expansion of myeloid derived suppressor cells (MDSCs) that potentiate cancer progression. Studies were performed with human triple negative breast cancer cells in immune suppressed mice and mouse breast cancer cells in immune competent mice. COX-2 levels significantly altered splenic metabolism and MDSC expansion in immune suppressed mice but not in immune competent mice that may have implications for breast cancer patients that are immune compromised.

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Wenjin Bian¹, Jinliang Niu¹, jianting Li¹, and Wenqi Wu^1
1The Second Hospital of Shanxi Medical University, Taiyuan, China

Keywords: Cancer, Diffusion/other diffusion imaging techniques, spleen

This study investigated the values of standardized volume and intravoxel incoherent motion (IVIM) parameters of spleen in evaluating tumor burden and prognosis in newly diagnosed acute leukemia (AL). Eighty-five AL patients and seventy-four healthy volunteers were recruited and underwent IVIM in the abdomen on a 3.0T MRI system. The results showed standardized volume and IVIM parameters of spleen were associated with tumor burden and treatment response in AL, and 218.1cm³ was the threshold of standardized spleen volume for predicting treatment response, which indicated that they can be potentially useful in tailoring the individualized treatment plan for each patient.

Gaofeng Shi¹, Qi Wang¹, Hui Feng¹, Hui Liu¹, Mengyu Song¹, Xinyue Liang², and Yongming Dai^2
1Department of Radiology, Fourth Hospital of Hebei Medical University, Shijiazhuang, China, ²Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Cancer, Lung

As a promising approach to analyze tumor heterogeneity through image features, habitat analysis has been applied for a variety of tumors, such as lung adenocarcinoma (LUAD). To our knowledge, the previous applications, mainly based on CT and PET images, would potentially hold a limited impact on the power of habitats analysis. In this study, our goal is to extend the applications of habitat analysis in LUAD with multi-parametric MR images, which would be obtained routinely longitudinally and to guide specific therapies.

Ning Zhang¹, Bei Liu¹, Gaofeng Shi¹, Qian Xu¹, Hui Feng¹, Hui Liu¹, Chen Zhang², and Fan Yang^1
1Hebei Medical University Fourth Affiliated Hospital, Shijiazhuang, China, ²MR Scientific Marketing, Siemens Healthineers, Beiing, China

Keywords: Cancer, Lung

This study investigated the clinical value of MRI T2WI for VPI in early-stage NSCLC. Four MRI signs of VPI in lung cancer were summarized by  MR T2WI of patients with early-stage NSCLC suspected of VPI. Among them, category 3 signs and category 4 signs had higher positive predictive values. This indicates that T2WI is useful in the prediction of VPI in early-stage NSCLC.

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Zhan Xu¹, Sanaz Pashapoor², Bikash Panthi¹, Jong Bum Son¹, Ken-Pin Hwang¹, Beatriz Elena Adrada², Rosalind Pitpitan Candelaria², Mary Saber Guirguis², Miral Mahesh Patel², Huong Le-Petross², Jessica Leung², Marion Elizabeth Scoggins², Gary Whitman², Rania Mohamed², Deanna Lynn Lane², Tanya Moseley², Frances Perez², Jason White³, Elizabeth Ravenberg³, Alyson Clayborn³, Huiqin Chen⁴, Jia Sun⁴, Peng Wei⁴, Alastair Thompson⁵, Anil Korkut⁶, Lei Huo⁷, Kelly Hunt⁸, Stacy Moulder³, Jennifer Litton³, Vicente Valero³, Debu Tripathy⁹, Wei Yang², Clinton Yam³, Gaiane Margishvili Rauch², and Jingfei Ma^1
1Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, United States, ²Departments of Breast Imaging, MD Anderson Cancer Center, Houston, TX, United States, ³Departments of Breast Medical Oncology, MD Anderson Cancer Center, Houston, TX, United States, ⁴Departments of Biostatistics, MD Anderson Cancer Center, Houston, TX, United States, ⁵Section of Breast Surgery, Baylor College of Medicine, Houston, TX, United States, ⁶Departments of Bioinformatics & Computational Biology, MD Anderson Cancer Center, Houston, TX, United States, ⁷Departments of Pathology, MD Anderson Cancer Center, Houston, TX, United States, ⁸Departments of Breast Surgical Oncology, MD Anderson Cancer Center, Houston, TX, United States, ⁹Department of Breast Medical Oncology, MD Anderson Cancer Center, Houston, TX, United States

Keywords: Cancer, Breast, TNBC

Quantitative image analysis of cancers requires accurate tumor segmentation that is often performed manually. In this study, we developed a deep learning model with the self-configurable nnU-Net for automated tumor segmentation on dynamic contrast enhanced MR images of triple negative breast cancers. Our results on an independent testing dataset demonstrated that this nnU-Net based deep learning model can perform automated tumor segmentation with high sensitivity and Dice coefficient.

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Yoshiharu Ohno^1,2, Kaori Yamamoto³, Masato Ikedo³, Masao Yui³, Akiyoshi Iwase⁴, Yuka Oshima⁵, Nayu Hamabuchi⁵, Satomu Hanamatsu⁵, Hiroyuki Nagata², Takahiro Ueda¹, Hirotaka Ikeda¹, Takeshi Yoshikawa^1,6, Daisuke Takenaka^1,6, Yoshiyuki Ozawa¹, and Hiroshi Toyama^1
1Radiology, Fujita Health University School of Medicine, Toyoake, Japan, ²Joint Research Laboratory of Advanced Medical Imaging, Fujita Health University School of Medicine, Toyoake, Japan, ³Canon Medical Systems Corporation, Otawara, Japan, ⁴Fujita Health University Hospital, Toyoake, Japan, ⁵Fujita Health University School of Medicine, Toyoake, Japan, ⁶Diagnostic Radiology, Hyogo Cancer Center, Akashi, Japan

Keywords: Cancer, Lung, Staging

Deep learning reconstruction (DLR) has been applied in routine clinical practice and started to demonstrate its’ potential in different MR examinations. However, no one have evaluated the utility of DLR for chest MRI, yet.  We hypothesize that DLR method is useful for chest MRI and improve image quality and diagnostic performance for T and N factor evaluations in non-small cell lung cancer (NSCLC) patients.  The purpose of this study was to determine the influence of DLR method on image quality and diagnostic performance for T and N factor evaluations at chest MRI in NSCLC patients.  

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Dexuan Li¹, Chenglong Wang¹, Funing Chu², Jinrong Qu², Yang Song³, and Guang Yang^1
1Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ²Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University &Henan Cancer Hospital, Zhengzhou, China, ³MR Scientific Marketing, Siemens Healthcare, Shanghai, China

Keywords: Cancer, Tumor

We enrolled 478 patients with esophageal squamous carcinoma (ESCC) split them into a training and a test cohort with a ratio of 7 to 3. Radiomic features were extracted from lesions on both MR and CT images and used to build models for predicting disease-free survival (DFS) and overall survival (OS). For both MR and CT images, the radiomic signature combined with clinical variables achieved performance comparable to radiological signature. Over the test cohort, MRI-based models achieved C-index values of 0.707 and 0.663 for DFS and OS predictions, respectively; CT-based models achieved 0.731 and 0.68 for DFS and OS, respectively.

Keke Zhao¹, Shaoyu Wang², Hongkai Zhang¹, Jinrong Qu¹, and Hailiang Li^1
1Henan Tumor Hosptial, Zhengzhou, China, ²MR Scientific Marketing, Siemens Healthineers, Shanghai, China

Keywords: Cancer, Cancer, esophageal squamous cell carcinoma

A total of 50 patients with locally advanced ESCC were prospectively enrolled in this study. Diffusion Weighted Imaging (DWI) with multiple high b-values was performed before surgery, and a new set of parameters (D, β and m) from a fractional order calculus (FROC) diffusion model were acquired. The grade of differentiation about ESCC was assessed. We found that  β and D value exhibited non-significant difference, and m value showed a significant difference between different grades ESCC (P =0.029).

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Ye Li¹, Ailian Liu¹, and Jiazheng Wang^2
1The First Affiliated Hospital of Dalian Medical University, Dalian, China, ²Clinical and Technical Support, Philips Healthcare, Beijing, China

Keywords: Cancer, fMRI

A total of 9 lesions from 7 EAOC patients and 21 lesions from 19 OC patients were collected to compare the differences in R2* and ADC values between the EAOC and OC groups. The results showed that the ADC values were able to discriminate between the two and there was no difference in R2* values.

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Shangxuan Li¹, Yanshu Fang², Guangyi Wang³, Lijuan Zhang⁴, and Wu Zhou^1
1School of Medical Information Engineering, Guangzhou University of Chinese Medicine, Guangzhou, China, ²First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangxhou, China, ³Department of Radiology, Guangdong Provincial People’s Hospital, Guangzhou, China, ⁴Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Keywords: Cancer, Machine Learning/Artificial Intelligence

Multimodal medical imaging plays an important role in the diagnosis and characterization of lesions. Transformer pays more attention to global relationship modeling in data, which has obtained promising performance in lesion characterization. We propose a multi-modal fusion network with jointly conditional transformer to realize adaptive fusion of multimodality information and mono-modality feature learning constrained by other modal conditions. The experimental results of the clinical hepatocellular carcinoma (HCC) dataset show that the proposed method is superior to the previously reported multimodal fusion methods for HCC grading.

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Wei Wang¹, Ke Xue², Yongming Dai², and Jianxing Qiu^1
1Department of Radiology, Peking University First Hospital, Beijing, China, ²MR Collaboration, Central Research Institute, Shanghai United Imaging Healthcare, Shanghai, China

Keywords: Cancer, Bladder, diffusion/other diffusion techniques

Accurately differentiation of MIBC from non-muscle-invasive bladder cancer (NMIBC) is essential for selecting the optimal treatment plan and obtaining a better prognosis for BCa patients. In this study, the role of the CTRW model, VI-RADS and their combination in characterizing MIBC was evaluated. The CTRW parameters combined with VI-RADS could provide significantly better diagnostic performance for MIBC determination than the VI-RADS score alone. The CTRW model could serve as a compliment to VI-RADS and provide added value for predicting muscle invasion of bladder cancer.

Xinxin Zhang¹, Yichen Wang¹, Sicong Wang², Jianzhong Shou¹, Yan Chen¹, and Xinming Zhao^1
1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China, Beijing, China, ²GE Healthcare, MR Research China, Beijing, Beijing, China

Keywords: Cancer, Bladder

Local tumor restaging after neoadjuvant chemotherapy (NAC) treatment is urgently needed. Therefore, the aim of this study was to investigate whether VI-RADS could accurately detect muscle invasion in BC patients after NAC treatment. VI-RADS scores of bladder cancer after NAC were independently assessed by two radiologists. With an optimal cut-off value ≥4, the AUC values for the VI-RADS scores for predicting muscle invasion were 0.91, and 0.94 for reader 1 and reader 2 respectively. VI-RADS could potentially be a restaging tool for patients who underwent NAC.

Jianwei Zeng¹, Haini Zhang¹, Yankai Meng¹, Lu Han², Peng Wu², and Kai Xu^1
1Department of Radiology, the Affiliated Hospital of Xuzhou Medical University, Xu zhou, China, ²Philips Healthcare, Shanghai, China

Keywords: Cancer, High-Field MRI

The rFOV IRIS-DWI and rFOV TSE-DWI sequences are newer techniques in rectal magnetic resonance. In this study, we compared the image quality between reduced field-of-view (rFOV) diffusion weighted imaging (DWI) sequence based on image reconstruction using image-space sampling functions (IRIS) and rFOV turbo spin echo-diffusion weighted imaging (TSE-DWI) sequence. Through subjective and objective image quality analysis, we found different application values of rFOV IRIS-DWI and rFOVTSE-DWI sequences in rectal cancer.

Yu Fu¹, Jiayi Gao¹, Mingyang Li¹, and Huimao Zhang^1
1The First Hospital of Jilin University, Changchun, China

Keywords: Cancer, Muscle

The low-risk rectal cancer (RC) has no need for postoperative treatment after total mesorectal excision (TME). However, there are some low-risk RC patients without postoperative-treatments had subsequent metastasis and recurrence. Skeletal muscles are gaining more attention which shown to be associated with morbidity and mortality in caners. Therefore, this study established a radiomics model based on pelvic skeletal muscles on MRI to identify the low-risk rectal cancer (RC) with poor prognosis. Our research shows that the novel radiomic signatures could be used to predict disease-free survival (DFS) in low-risk RC to help clinicians improve the treatment decision making followed TME.

WEN JUN HU¹, Anliang Chen¹, and AIlian Liu^1
1The First Affiliated Hospital of Dalian Medical University, Dalian, China

Keywords: Cancer, DSC & DCE Perfusion

Tumor deposits (TD) and N stage are powerful prognostic factors for predicting the overall disease-free survival of rectal cancer patients. However, rectal cancer patients with TD are not the same as those with metastatic lymph node(MLN) in terms of treatment measures and prognosis. TD has similar performance to MLN on conventional CT/MRI images, which makes it difficult to distinguish them. Results of this study indicate DCE-MRI can effectively differentiate TD and MLN adjacent to rectal cancer, and the higher Kep and lower Ve values of rectal cancer with MLN may indicate its’ more aggressive. 

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Shujian Li¹, Jieliang Lin², and Jingliang Cheng^1
1the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²Advanced Technical Support, Philips Healthcare, Beijing, China

Keywords: Cancer, CEST & MT, Cervical cancer

This study compared the diagnostic performances of mean, minimum, and maximum values of APT SI for preoperative identifying the prognostic factors (tumor stage, subtype, grade and LVSI status) of cervical cancer with those of multi-model DWI, and examined the additive value of 3D-APT imaging combined with multi-model DWI for the preoperative characterization of cervical cancer. The results showed that 3D-APT imaging was comparable with multi-model DWI for cervical cancer typing and grading. Adding APT to DWI can significantly improve the diagnostic performance with an AUC of 0.908 to predict tumor subtype, and an AUC of 0.903 to predict histological grade.

Xing Meng¹, Ailian Liu², and Shifeng Tian^2
1Dalian Women and Children’s Medical Group, Dalian, China, ²the First Affiliated Hospital of Dalian Medical University, Dalian, China

Keywords: Cancer, Uterus

APTw and mDixon-Quant imaging were evaluated in this study for discrimination of the differention degree of CSC by histopathology. The APTw and R2* values of the poorly differentiated group were significantly higher than those of the well/moderately differentiated group. And the combination of APTw and R2* values showed a high diagnostic efficacy in discrimination of CSC with different differention degrees.

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Leo L. Cheng^1,2, Florian Rumpf ³, Matteo Sanchez-Dahl Gonzalez⁴, and Adam S. Feldman^5
1Pathology, Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ²Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Charlestown, MA, United States, ³Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Pathology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, ⁵Urology, Massachusetts General Hospital, Boston, MA, United States

Keywords: Cancer, Prostate, Spectroscopy

HRMAS 1H MRS was employed for an untargeted investigation of metabolism in prostate cancer patients on AS (n=52). Serum samples from two matched patient groups, progressive (n=26) and non-progressive (n=26) were measured with short and long T2 filter to investigate metabolites in the lipoprotein and LMWM regions respectively. PCA on 46 ROI from data acquired by HRMAS 1H MRS with short T2 resulted in a metabolomic profile, PC7, that separated the progressive and non-progressive groups (p = 0.0323). The findings of such studies could revolutionize healthcare by improving the diagnosis of diseases and contributing to multi-omics integration for precision medicine.

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Yuko Someya^1,2, Mami Iima¹, Sawako Hayami³, Hirohiko Imai⁴, Hiroaki Takishima³, Denis Le Bihan^5,6, Tomomi Nobashi¹, Tsuyoshi Ohno¹, and Yuji Nakamoto^1
1Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medi, Kyoto, Japan, ²Diagnostic Radiology, Kobe City Medical Center General Hospital, Kobe, Japan, ³Kyoto University Faculty of Medicine, Kyoto, Japan, ⁴Department of Systems science, Graduate School of Informatics, Kyoto University Graduate School of Medi, Kyoto, Japan, ⁵CEA-Saclay Center, Paris-Saclay, NeuroSpin, Gif-sur-Yvette, France, ⁶Human Brain Research Center, Kyoto University Graduate School of Medicine, Kyoto, Japan

Keywords: Cancer, Diffusion/other diffusion imaging techniques

The optimal fitting method (1-step or 2-step) and b-value threshold (200 or 600 s/mm²) in analyzing IVIM/diffusion data was investigated using two different mice xenograft models (breast and colon cancer). The threshold of b=600 rather than 200s/mm² was found better in these models, as many cases showed non negligible residual IVIM components left with b=200s/mm². From simulations, it appears that a too low b value threshold leads to an overestimation of ADC₀ and underestimation of K and f_IVIM .Those results suggest that the b-value threshold must be checked depending on the tissue types.