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Fan Y, Li X, Zhong P, Guo H, Han D, Tian W, Fan J. Radiological Features for Predicting the Status of CD8-Positive Lymphocytes in HER2 Positive Breast Cancer. Balkan Med J 2024; 41:213-221. [PMID: 38700366 PMCID: PMC11077930 DOI: 10.4274/balkanmedj.galenos.2024.2024-2-64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Accepted: 04/04/2024] [Indexed: 05/05/2024] Open
Abstract
Background The level of tumor-infiltrating lymphocytes (TILs) in human epidermal growth factor receptor type 2 (HER2)-positive breast cancer (BC) is positively correlated with pathological complete response. Aims To investigate the relationship between ultrasound (US) and magnetic resonance imaging (MRI) features and the level of CD8-positive TILs (CD8+-TILs) in patients with HER2-positive BC. Study Design Retrospective cohort study. Methods This retrospective study included 155 consecutive women with HER2-positive BC. Patients were divided into two groups: CD8+-TILlow (< 35%) and CD8+-TILhigh (≥ 35%) groups. US and MRI features were evaluated using the BI-RADS lexicon, and the apparent diffusion coefficient (ADC) value was calculated using RadiAnt software. Univariate and multivariate analyses revealed the optimal US and MRI features for predicting CD8+-TIL levels. Receiver operating characteristic analysis and the Delong test were used to compare the diagnostic performance of US and MRI features. Furthermore, implementing a nomogram will increase clinical utility. Results Univariate analysis of US features showed significant differences in shape, orientation, and posterior echo between the two groups; however, there were no significant differences in margins, internal echo, and microcalcification. Multifactorial analysis revealed that shape, orientation, and posterior echo were independent risk factors, with odds ratios of 11.62, 2.70, and 0.16, respectively. In terms of MRI features, ADC was an independent predictor of CD8+-TIL levels. These three US features and the ADC performed well, with area under the curve (AUC) values of 0.802 and 0.705, respectively. The combination of US and ADC values had higher predictive efficacy (AUC = 0.888) than either US or ADC alone (p = 0.009, US_ADC vs. US; p < 0.001, US_ADC vs. ADC). Conclusion US features (shape, orientation, and posterior echo) and ADC value may be a valuable tool for estimating CD8+-TIL levels in HER2-positive BC. The nomogram may help clinicians in making decisions.
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Affiliation(s)
- Yuhong Fan
- Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China
| | - Xiaoguang Li
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Peng Zhong
- Department of Pathology, Daping Hospital, Army Medical University, Chongqing, China
| | - Hong Guo
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Dong Han
- Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China
| | - Wuguo Tian
- Department of Breast and Thyroid Surgery, Daping Hospital, Army Medical University, Chongqing, China
| | - Jingqin Fan
- Department of Ultrasound Diagnosis, Daping Hospital, Army Medical University, Chongqing, China
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Sun Z, Zhang T, Ahmad MU, Zhou Z, Qiu L, Zhou K, Xiong W, Xie J, Zhang Z, Chen C, Yuan Q, Chen Y, Feng W, Xu Y, Yu L, Wang W, Yu J, Li G, Jiang Y. Comprehensive assessment of immune context and immunotherapy response via noninvasive imaging in gastric cancer. J Clin Invest 2024; 134:e175834. [PMID: 38271117 PMCID: PMC10940098 DOI: 10.1172/jci175834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/22/2024] [Indexed: 01/27/2024] Open
Abstract
BACKGROUNDThe tumor immune microenvironment can provide prognostic and therapeutic information. We aimed to develop noninvasive imaging biomarkers from computed tomography (CT) for comprehensive evaluation of immune context and investigate their associations with prognosis and immunotherapy response in gastric cancer (GC).METHODSThis study involved 2,600 patients with GC from 9 independent cohorts. We developed and validated 2 CT imaging biomarkers (lymphoid radiomics score [LRS] and myeloid radiomics score [MRS]) for evaluating the IHC-derived lymphoid and myeloid immune context respectively, and integrated them into a combined imaging biomarker [LRS/MRS: low(-) or high(+)] with 4 radiomics immune subtypes: 1 (-/-), 2 (+/-), 3 (-/+), and 4 (+/+). We further evaluated the imaging biomarkers' predictive values on prognosis and immunotherapy response.RESULTSThe developed imaging biomarkers (LRS and MRS) had a high accuracy in predicting lymphoid (AUC range: 0.765-0.773) and myeloid (AUC range: 0.736-0.750) immune context. Further, similar to the IHC-derived immune context, 2 imaging biomarkers (HR range: 0.240-0.761 for LRS; 1.301-4.012 for MRS) and the combined biomarker were independent predictors for disease-free and overall survival in the training and all validation cohorts (all P < 0.05). Additionally, patients with high LRS or low MRS may benefit more from immunotherapy (P < 0.001). Further, a highly heterogeneous outcome on objective response rate was observed in 4 imaging subtypes: 1 (-/-) with 27.3%, 2 (+/-) with 53.3%, 3 (-/+) with 10.2%, and 4 (+/+) with 30.0% (P < 0.0001).CONCLUSIONThe noninvasive imaging biomarkers could accurately evaluate the immune context and provide information regarding prognosis and immunotherapy for GC.
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Affiliation(s)
- Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | | | - Zixia Zhou
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Liang Qiu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California, USA
| | - Kangneng Zhou
- College of Computer Science, Nankai University, Tianjin, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Jingjing Xie
- Graduate Group of Epidemiology, UCD, Davis, California, USA
| | - Zhicheng Zhang
- JancsiTech and Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yan Chen
- Shenzhen Hospital of Integrated Traditional Chinese and Western Medicine, Shenzhen, China
| | - Wanying Feng
- Department of Pathology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lequan Yu
- The Department of Statistics and Actuarial Science, The University of Hong Kong, HKSAR, Hong Kong, China
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jiang Yu
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
- Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China
| | - Yuming Jiang
- Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA
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Zhou J, Jin Y, Miao H, Lu S, Liu X, He Y, Liu H, Zhao Y, Zhang Y, Liu YL, Pan Z, Chen JH, Wang M, Su MY. Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes. Cancers (Basel) 2023; 15:5672. [PMID: 38067374 PMCID: PMC10705181 DOI: 10.3390/cancers15235672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 11/23/2023] [Accepted: 11/27/2023] [Indexed: 01/19/2024] Open
Abstract
A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients (p < 0.001). MRI features such as morphology as mass or non-mass enhancement (NME), shape, margin, internal enhancement, presence of peritumoral edema, and the DCE kinetic pattern were assessed. Tumor sizes were smaller in the HR+/HER2- group (p < 0.001); HER2+ was more likely to present as NME (p = 0.031); homogeneous enhancement was mostly seen in HR+ (p < 0.001); and the peritumoral edema was present in 45% HR+, 71% HER2+, and 80% TN (p < 0.001). In each subtype, the MR features between the high- vs. low-TIL groups were compared. In HR+/HER2-, peritumoral edema was more likely to be present in those with high TILs (70%) than in those with low TILs (40%, p < 0.001). In TN, those with high TILs were more likely to present a regular shape (33%) than those with low TILs (13%, p = 0.029) and more likely to present the circumscribed margin (19%) than those with low TILs (2%, p = 0.009).
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Affiliation(s)
- Jiejie Zhou
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Yi Jin
- Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (Y.J.); (S.L.)
| | - Haiwei Miao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Shanshan Lu
- Department of Pathology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (Y.J.); (S.L.)
| | - Xinmiao Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Yun He
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Huiru Liu
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Youfan Zhao
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Yang Zhang
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Zhifang Pan
- Zhejiang Engineering Research Center of Intelligent Medicine, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China;
| | - Jeon-Hor Chen
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
| | - Meihao Wang
- Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China; (J.Z.); (H.M.); (X.L.); (Y.H.); (H.L.); (Y.Z.)
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, Irvine, CA 92697, USA; (Y.Z.); (Y.-L.L.); (J.-H.C.)
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung 840203, Taiwan
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Fan M, Wang K, Zhang Y, Ge Y, Lü Z, Li L. Radiogenomic analysis of cellular tumor-stroma heterogeneity as a prognostic predictor in breast cancer. J Transl Med 2023; 21:851. [PMID: 38007511 PMCID: PMC10675940 DOI: 10.1186/s12967-023-04748-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 11/22/2023] [Indexed: 11/27/2023] Open
Abstract
BACKGROUND The tumor microenvironment and intercellular communication between solid tumors and the surrounding stroma play crucial roles in cancer initiation, progression, and prognosis. Radiomics provides clinically relevant information from radiological images; however, its biological implications in uncovering tumor pathophysiology driven by cellular heterogeneity between the tumor and stroma are largely unknown. We aimed to identify radiogenomic signatures of cellular tumor-stroma heterogeneity (TSH) to improve breast cancer management and prognosis analysis. METHODS This retrospective multicohort study included five datasets. Cell subpopulations were estimated using bulk gene expression data, and the relative difference in cell subpopulations between the tumor and stroma was used as a biomarker to categorize patients into good- and poor-survival groups. A radiogenomic signature-based model utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was developed to target TSH, and its clinical significance in relation to survival outcomes was independently validated. RESULTS The final cohorts of 1330 women were included for cellular TSH biomarker identification (n = 112, mean age, 57.3 years ± 14.6) and validation (n = 886, mean age, 58.9 years ± 13.1), radiogenomic signature of TSH identification (n = 91, mean age, 55.5 years ± 11.4), and prognostic (n = 241) assessments. The cytotoxic lymphocyte biomarker differentiated patients into good- and poor-survival groups (p < 0.0001) and was independently validated (p = 0.014). The good survival group exhibited denser cell interconnections. The radiogenomic signature of TSH was identified and showed a positive association with overall survival (p = 0.038) and recurrence-free survival (p = 3 × 10-4). CONCLUSION Radiogenomic signatures provide insights into prognostic factors that reflect the imbalanced tumor-stroma environment, thereby presenting breast cancer-specific biological implications and prognostic significance.
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Affiliation(s)
- Ming Fan
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Kailang Wang
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - You Zhang
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Yuanyuan Ge
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, China
| | - Zhong Lü
- Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, 322100, China.
| | - Lihua Li
- Institute of Intelligent Biomedicine, Hangzhou Dianzi University, Hangzhou, 310018, China.
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Malhaire C, Selhane F, Saint-Martin MJ, Cockenpot V, Akl P, Laas E, Bellesoeur A, Ala Eddine C, Bereby-Kahane M, Manceau J, Sebbag-Sfez D, Pierga JY, Reyal F, Vincent-Salomon A, Brisse H, Frouin F. Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy. Eur Radiol 2023; 33:8142-8154. [PMID: 37318605 DOI: 10.1007/s00330-023-09797-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/14/2023] [Accepted: 05/13/2023] [Indexed: 06/16/2023]
Abstract
OBJECTIVES To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. RESULTS Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n = 7/37, 19%), triple negative (n = 30/55, 55%), HER2 + (n = 22/37, 59%)). Clinical and biological items associated with pCR were BC subtype (p < 0.001), T stage 0/I/II (p = 0.008), higher Ki67 (p = 0.005), and higher tumor-infiltrating lymphocytes levels (p = 0.016). Univariate analysis showed that the following MRI features, oval or round shape (p = 0.047), unifocality (p = 0.026), non-spiculated margins (p = 0.018), no associated non-mass enhancement (p = 0.024), and a lower MRI size (p = 0.031), were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67), and precision (0.71 versus 0.67) for pCR prediction. CONCLUSION Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. CLINICAL RELEVANCE STATEMENT A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including tumor-infiltrating lymphocytes, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes. KEY POINTS • Unifocality and non-spiculated margins are independently associated with pCR at multivariable logistic regression analysis. • Breast edema score is associated with MR tumor size and TIL expression, not only in TN BC as previously reported, but also in luminal BC. • Adding significant MRI features to clinicobiological variables in machine learning classifiers significantly increased sensitivity, specificity, and precision for pCR prediction.
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Affiliation(s)
- Caroline Malhaire
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France.
- Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France.
| | - Fatine Selhane
- Gustave Roussy, Department of Imaging, Paris-Saclay University, 94805, Villejuif, France
| | | | - Vincent Cockenpot
- Pathology Unit, Centre Léon Bérard, 28 Rue Laennec, 69008, Lyon, France
| | - Pia Akl
- Women Imaging Unit, HCL, Radiologie du Groupement Hospitalier Est, 3 Quai Des Célestins, 69002, Lyon, France
| | - Enora Laas
- Department of Surgical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Audrey Bellesoeur
- Department of Medical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Catherine Ala Eddine
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Melodie Bereby-Kahane
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Julie Manceau
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Delphine Sebbag-Sfez
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Jean-Yves Pierga
- Department of Medical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | - Fabien Reyal
- Department of Surgical Oncology, Institut Curie, 26 Rue d'Ulm, 75005, Paris, France
| | | | - Herve Brisse
- Department of Medical Imaging, Institut Curie, PSL Research University, 26 Rue d'Ulm, 75005, Paris, France
| | - Frederique Frouin
- Institut Curie, Research Center, U1288-LITO, Inserm, Paris-Saclay University, 91401, Orsay, France
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Wu R, Jia Y, Li N, Lu X, Yao Z, Ma Y, Nie F. Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2398-2406. [PMID: 37634979 DOI: 10.1016/j.ultrasmedbio.2023.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/30/2023] [Accepted: 08/04/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images. METHODS We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning. RESULTS MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance. CONCLUSION The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.
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Affiliation(s)
- Ruichao Wu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yingying Jia
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China; Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China; Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
| | - Nana Li
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China; Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China; Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
| | - Xiangyu Lu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zihuan Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Yide Ma
- School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
| | - Fang Nie
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China; Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China; Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
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Kennedy LC, Kazerouni AS, Chau B, Biswas D, Alvarez R, Durenberger G, Dintzis SM, Stanton SE, Partridge SC, Gadi V. Associations of Multiparametric Breast MRI Features, Tumor-Infiltrating Lymphocytes, and Immune Gene Signature Scores Following a Single Dose of Trastuzumab in HER2-Positive Early-Stage Breast Cancer. Cancers (Basel) 2023; 15:4337. [PMID: 37686613 PMCID: PMC10486523 DOI: 10.3390/cancers15174337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 08/17/2023] [Accepted: 08/23/2023] [Indexed: 09/10/2023] Open
Abstract
Dynamic biomarkers that permit the real-time monitoring of the tumor microenvironment response to therapy are an unmet need in breast cancer. Breast magnetic resonance imaging (MRI) has demonstrated value as a predictor of pathologic complete response and may reflect immune cell changes in the tumor microenvironment. The purpose of this pilot study was to investigate the value of breast MRI features as early markers of treatment-induced immune response. Fourteen patients with early HER2+ breast cancer were enrolled in a window-of-opportunity study where a single dose of trastuzumab was administered and both tissue and MRIs were obtained at the pre- and post-treatment stages. Functional diffusion-weighted and dynamic contrast-enhanced MRI tumor measures were compared with tumor-infiltrating lymphocytes (TILs) and RNA immune signature scores. Both the pre-treatment apparent diffusion coefficient (ADC) and the change in peak percent enhancement (DPE) were associated with increased tumor-infiltrating lymphocytes with trastuzumab therapy (r = -0.67 and -0.69, p < 0.01 and p < 0.01, respectively). Low pre-treatment ADC and a greater decrease in PE in response to treatment were also associated with immune-activated tumor microenvironments as defined by RNA immune signatures. Breast MRI features hold promise as biomarkers of early immune response to treatment in HER2+ breast cancer.
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Affiliation(s)
- Laura C. Kennedy
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Anum S. Kazerouni
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Bonny Chau
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Debosmita Biswas
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Rebeca Alvarez
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | | | - Suzanne M. Dintzis
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Pathology, University of Washington, Seattle, WA 98195, USA
| | - Sasha E. Stanton
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Cancer Immunoprevention Laboratory, Earle A. Chiles Research Institute, Portland, OR 97213, USA
| | - Savannah C. Partridge
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Vijayakrishna Gadi
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
- Department of Medicine, University of Illinois Chicago, Chicago, IL 60612, USA
- Translational Oncology Program, University of Illinois Cancer Center, Chicago, IL 60612, USA
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Jiang Y, Zhou K, Sun Z, Wang H, Xie J, Zhang T, Sang S, Islam MT, Wang JY, Chen C, Yuan Q, Xi S, Li T, Xu Y, Xiong W, Wang W, Li G, Li R. Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics. Cell Rep Med 2023; 4:101146. [PMID: 37557177 PMCID: PMC10439253 DOI: 10.1016/j.xcrm.2023.101146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 06/06/2023] [Accepted: 07/12/2023] [Indexed: 08/11/2023]
Abstract
The tumor microenvironment (TME) plays a critical role in disease progression and is a key determinant of therapeutic response in cancer patients. Here, we propose a noninvasive approach to predict the TME status from radiological images by combining radiomics and deep learning analyses. Using multi-institution cohorts of 2,686 patients with gastric cancer, we show that the radiological model accurately predicted the TME status and is an independent prognostic factor beyond clinicopathologic variables. The model further predicts the benefit from adjuvant chemotherapy for patients with localized disease. In patients treated with checkpoint blockade immunotherapy, the model predicts clinical response and further improves predictive accuracy when combined with existing biomarkers. Our approach enables noninvasive assessment of the TME, which opens the door for longitudinal monitoring and tracking response to cancer therapy. Given the routine use of radiologic imaging in oncology, our approach can be extended to many other solid tumor types.
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Affiliation(s)
- Yuming Jiang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, China; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kangneng Zhou
- School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zepang Sun
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Hongyu Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jingjing Xie
- Graduate Group of Epidemiology, University of California Davis, Davis, CA, USA
| | - Taojun Zhang
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Shengtian Sang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Md Tauhidul Islam
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jen-Yeu Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Sujuan Xi
- The Reproductive Medical Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Tuanjie Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Wenjun Xiong
- Department of Gastrointestinal Surgery, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China
| | - Wei Wang
- Department of Gastric Surgery, and State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
| | - Guoxin Li
- Department of General Surgery & Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, Southern Medical University, Guangzhou, China.
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
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Huang H, Li Z, Xia Y, Zhao Z, Wang D, Jin H, Liu F, Yang Y, Shen L, Lu Z. Association between radiomics features of DCE-MRI and CD8 + and CD4 + TILs in advanced gastric cancer. Pathol Oncol Res 2023; 29:1611001. [PMID: 37342362 PMCID: PMC10277864 DOI: 10.3389/pore.2023.1611001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer. Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density. Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630-0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = -0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = -0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856). Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.
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Affiliation(s)
- Huizhen Huang
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhiheng Li
- Department of Radiology, Anhui Provincial Hospital, Hefei, China
| | - Yue Xia
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hongyan Jin
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Fang Liu
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ye Yang
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Liyijing Shen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
- The First Affiliated Hospital of Shaoxing University, Shaoxing, China
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10
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Jia Y, Zhu Y, Li T, Song X, Duan Y, Yang D, Nie F. Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer: The Role of Conventional Ultrasound and Contrast-Enhanced Ultrasound. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:623-634. [PMID: 35866231 DOI: 10.1002/jum.16058] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 06/21/2022] [Accepted: 06/25/2022] [Indexed: 05/27/2023]
Abstract
OBJECTIVES Tumor-infiltrating lymphocytes (TILs) have emerged as an efficient biomarker predicting treatment response and prognosis of breast cancer (BC). This study aimed to evaluate the association between conventional ultrasound and contrast-enhanced ultrasound (CEUS) imaging features with TIL levels in invasive BC patients. METHODS We retrospectively included 267 women with invasive BC who had undergone conventional ultrasound and CEUS. Patients were divided into low (≤10%) and high (>10%) TIL groups. Conventional ultrasound and CEUS features were analyzed by two sonographers. The associations between the TIL levels and imaging features were evaluated. RESULTS Of the 267 patients, 122 with high TILs and 145 with low TIL levels. High TIL tumors were more likely to have a circumscribed margin, oval or round shape, and enhanced posterior echoes on ultrasonography (p < 0.05). In contrast, low TIL tumors were more likely to have an irregular shape, un-circumscribed, indistinct and spiculated margin (p < 0.05). In CEUS, high TIL tumors showed a more regular shape, clearer margin, more homogeneous enhancement and higher peak intensity (PI) value (p < 0.05). Logistic analysis indicated that shape, posterior features, PI, and enhanced homogeneity were independent predictors for high TIL tumors. The model combined the four independent predictors have a moderate performance in predicting high TIL tumors with AUC 0.79, sensitivity 0.72, and specificity 0.78. CONCLUSIONS Conventional ultrasound and CEUS features were associated with TIL levels in invasive BC. Consequently, the results suggested that preoperative conventional ultrasound and CEUS may be a useful noninvasive imaging biomarker for individualized treatment decisions.
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Affiliation(s)
- Yingying Jia
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Department of Ultrasound, People's Hospital of Ningxia Hui Nationality Autonomous Region, Yinchuan, Ningxia, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
| | - Yangyang Zhu
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
| | - Ting Li
- Department of Ultrasound, People's Hospital of Ningxia Hui Nationality Autonomous Region, Yinchuan, Ningxia, China
| | - XueWen Song
- Pathology Department, Lanzhou University Second Hospital, Lanzhou, China
| | - Ying Duan
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
| | - Dan Yang
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
| | - Fang Nie
- Ultrasound Medical Center, Lanzhou University Second Hospital, Lanzhou, China
- Gansu Province Clinical Research Center for Ultrasonography, Lanzhou, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Lanzhou, China
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11
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Jia Y, Wu R, Lu X, Duan Y, Zhu Y, Ma Y, Nie F. Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study. Cancers (Basel) 2023; 15:cancers15030838. [PMID: 36765796 PMCID: PMC9913836 DOI: 10.3390/cancers15030838] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 01/20/2023] [Accepted: 01/27/2023] [Indexed: 02/01/2023] Open
Abstract
This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making.
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Affiliation(s)
- Yingying Jia
- Ultrasound Medical Center, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Ultrasonography, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Ruichao Wu
- School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730030, China
| | - Xiangyu Lu
- School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730030, China
| | - Ying Duan
- Department of Ultrasound, Gansu Provincial Cancer Hospital, West Lake East Street No. 2, Qilihe District, Lanzhou 730030, China
| | - Yangyang Zhu
- Ultrasound Medical Center, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Ultrasonography, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Yide Ma
- School of Information Science and Engineering, Lanzhou University, No. 222 South Tianshui Road, Lanzhou 730030, China
- Correspondence: (Y.M.); (F.N.)
| | - Fang Nie
- Ultrasound Medical Center, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Medical Engineering Research Center for Intelligence Ultrasound, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Gansu Province Clinical Research Center for Ultrasonography, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
- Correspondence: (Y.M.); (F.N.)
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12
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Zhang Y, Yang L, Jiao X. Analysis of Breast Cancer Differences between China and Western Countries Based on Radiogenomics. Genes (Basel) 2022; 13:2416. [PMID: 36553681 PMCID: PMC9778234 DOI: 10.3390/genes13122416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/12/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Using radiogenomics methods, the differences between tumor imaging data and genetic data in Chinese and Western breast cancer (BC) patients were analyzed, and the correlation between phenotypic data and genetic data was explored. In this paper, we analyzed BC patients' image characteristics and transcriptome data separately, then correlated the magnetic resonance imaging (MRI) phenotype with the transcriptome data through a computational method to develop a radiogenomics feature. The data was fed into the designed random forest (RF) model, which used the area under the receiver operating curve (AUC) as the evaluation index. Next, we analyzed the hub genes in the differentially expressed genes (DEGs) and obtained seven hub genes, which may cause Chinese and Western BC patients to behave differently in the clinic. We demonstrated that combining relevant genetic data and imaging features could better classify Chinese and Western patients than using genes or imaging characteristics alone. The AUC values of 0.74, 0.81, and 0.95 were obtained separately using the image characteristics, DEGs, and radiogenomics features. We screened SYT4, GABRG2, CHGA, SLC6A17, NEUROG2, COL2A1, and MATN4 and found that these genes were positively or negatively correlated with certain imaging characteristics. In addition, we found that the SLC6A17, NEUROG2, CHGA, and MATN4 genes were associated with clinical features.
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Affiliation(s)
- Yuanyuan Zhang
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong 030600, China
| | - Lifeng Yang
- College of Information and Computer, Taiyuan University of Technology, Jinzhong 030600, China
| | - Xiong Jiao
- College of Biomedical Engineering, Taiyuan University of Technology, Jinzhong 030600, China
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13
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Capobianco E. Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research. ANNALS OF TRANSLATIONAL MEDICINE 2022; 10:1300. [PMID: 36660729 PMCID: PMC9843365 DOI: 10.21037/atm-22-5477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 12/15/2022] [Indexed: 01/01/2023]
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14
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Li J, Zhang C, Guo H, Li S, You Y, Zheng P, Zhang H, Wang H, Bai J. Non-invasive measurement of tumor immune microenvironment and prediction of survival and chemotherapeutic benefits from 18F fluorodeoxyglucose PET/CT images in gastric cancer. Front Immunol 2022; 13:1019386. [PMID: 36311742 PMCID: PMC9606753 DOI: 10.3389/fimmu.2022.1019386] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/23/2022] [Indexed: 02/11/2024] Open
Abstract
BACKGROUND The tumor immune microenvironment could provide prognostic and predictive information. It is necessary to develop a noninvasive radiomics-based biomarker of a previously validated tumor immune microenvironment signature of gastric cancer (GC) with immunohistochemistry staining. METHODS A total of 230 patients (training (n = 153) or validation (n = 77) cohort) with gastric cancer were subjected to (Positron Emission Tomography-Computed Tomography) radiomics feature extraction (80 features). A radiomics tumor immune microenvironment score (RTIMS) was developed to predict the tumor immune microenvironment signature with LASSO logistic regression. Furthermore, we evaluated its relation with prognosis and chemotherapy benefits. RESULTS A 8-feature radiomics signature was established and validated (area under the curve=0.692 and 0.713). The RTIMS signature was significantly associated with disease-free survival and overall survival both in the training and validation cohort (all P<0.001). RTIMS was an independent prognostic factor in the Multivariate analysis. Further analysis revealed that high RTIMS patients benefitted from adjuvant chemotherapy (for DFS, stage II: HR 0.208(95% CI 0.061-0.711), p=0.012; stage III: HR 0.321(0.180-0.570), p<0.001, respectively); while there were no benefits from chemotherapy in a low RTIMS patients. CONCLUSION This PET/CT radiomics model provided a promising way to assess the tumor immune microenvironment and to predict clinical outcomes and chemotherapy response. The RTIMS signature could be useful in estimating tumor immune microenvironment and predicting survival and chemotherapy benefit for patients with gastric cancer, when validated by further prospective randomized trials.
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Affiliation(s)
- Junmeng Li
- Department of Gastrointestinal Surgery, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Chao Zhang
- Department of Gastrointestinal Surgery, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Huihui Guo
- Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Shuang Li
- Department of Pathology, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Yang You
- Department of Nuclear Medicine, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, Henan, China
| | - Peiming Zheng
- Department of Clinical Laboratory, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
| | - Hongquan Zhang
- Department of Thoracic Surgery, The First Hospital Affiliated of Xinxiang Medical University, Xinxiang, China
| | - Huanan Wang
- Department of Gastrointestinal Surgery, The First Hospital Affiliated of Zhengzhou University, Zhengzhou, China
| | - Junwei Bai
- Department of Gastrointestinal Surgery, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Henan University People’s Hospital, Zhengzhou, China
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Chen MM, Terzic A, Becker AS, Johnson JM, Wu CC, Wintermark M, Wald C, Wu J. Artificial intelligence in oncologic imaging. Eur J Radiol Open 2022; 9:100441. [PMID: 36193451 PMCID: PMC9525817 DOI: 10.1016/j.ejro.2022.100441] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 09/25/2022] [Accepted: 09/26/2022] [Indexed: 01/07/2023] Open
Abstract
Radiology is integral to cancer care. Compared to molecular assays, imaging has its advantages. Imaging as a noninvasive tool can assess the entirety of tumor unbiased by sampling error and is routinely acquired at multiple time points in oncological practice. Imaging data can be digitally post-processed for quantitative assessment. The ever-increasing application of Artificial intelligence (AI) to clinical imaging is challenging radiology to become a discipline with competence in data science, which plays an important role in modern oncology. Beyond streamlining certain clinical tasks, the power of AI lies in its ability to reveal previously undetected or even imperceptible radiographic patterns that may be difficult to ascertain by the human sensory system. Here, we provide a narrative review of the emerging AI applications relevant to the oncological imaging spectrum and elaborate on emerging paradigms and opportunities. We envision that these technical advances will change radiology in the coming years, leading to the optimization of imaging acquisition and discovery of clinically relevant biomarkers for cancer diagnosis, staging, and treatment monitoring. Together, they pave the road for future clinical translation in precision oncology.
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Affiliation(s)
- Melissa M. Chen
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Admir Terzic
- Department of Radiology, Dom Zdravlja Odzak, Odzak, Bosnia and Herzegovina
| | - Anton S. Becker
- Department Radiology, Memorial Sloan Kettering, New York, NY, USA
| | - Jason M. Johnson
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Carol C. Wu
- Department of Thoracic Imaging, MD Anderson Cancer Center, Houston, TX, USA
| | - Max Wintermark
- Department of Neuroradiology, MD Anderson Cancer Center, Houston, TX, USA
| | - Christoph Wald
- Department of Radiology, Lahey Hospital and Medical Center, Burlington, MA, USA
| | - Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
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16
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Wu J, Mayer AT, Li R. Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy. Semin Cancer Biol 2022; 84:310-328. [PMID: 33290844 PMCID: PMC8319834 DOI: 10.1016/j.semcancer.2020.12.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 11/29/2020] [Accepted: 12/02/2020] [Indexed: 02/07/2023]
Abstract
Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.
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Affiliation(s)
- Jia Wu
- Department of Imaging Physics, MD Anderson Cancer Center, Texas, 77030, USA; Department of Thoracic/Head & Neck Medical Oncology, MD Anderson Cancer Center, Texas, 77030, USA.
| | - Aaron T Mayer
- Department of Bioengineering, Stanford University, Stanford, California, 94305, USA; Department of Radiology, Stanford University, Stanford, California, 94305, USA; Molecular Imaging Program at Stanford, Stanford University, Stanford, California, 94305, USA; BioX Program at Stanford, Stanford University, Stanford, California, 94305, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University, Stanford, California, 94305, USA
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17
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Machine Learning for Computed Tomography Radiomics: Prediction of Tumor-Infiltrating Lymphocytes in Patients With Pancreatic Ductal Adenocarcinoma. Pancreas 2022; 51:549-558. [PMID: 35877153 DOI: 10.1097/mpa.0000000000002069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
OBJECTIVES The aims of the study were to develop and validate a machine learning classifier for preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS In this retrospective study of 183 PDAC patients who underwent multidetector computed tomography and surgical resection, CD4 + , CD8 + , and CD20 + expression was evaluated using immunohistochemistry, and TIL scores were calculated using the Cox regression model. The patients were divided into TIL-low and TIL-high groups. An extreme gradient boosting (XGBoost) classifier was developed using a training set consisting of 136 consecutive patients, and the model was validated in 47 consecutive patients. The discriminative ability, calibration, and clinical utility of the XGBoost classifier were evaluated. RESULTS The prediction model showed good discrimination in the training (area under the curve, 0.93; 95% confidence interval, 0.89-0.97) and validation (area under the curve, 0.79; 95% confidence interval, 0.65-0.92) sets with good calibration. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 0.93, 0.85, 0.90, 0.89, and 0.91, respectively, while those for the validation set were 0.63, 0.91, 0.77, 0.88, and 0.70, respectively. CONCLUSIONS The XGBoost-based model could predict PDAC TILs and may facilitate clinical decision making for immune therapy.
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Rella R, Bufi E, Belli P, Scrofani AR, Petta F, Borghetti A, Marazzi F, Valentini V, Manfredi R. Association between contralateral background parenchymal enhancement on MRI and outcome in patients with unilateral invasive breast cancer breast receiving neoadjuvant chemotherapy. Diagn Interv Imaging 2022; 103:486-494. [DOI: 10.1016/j.diii.2022.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 04/08/2022] [Accepted: 04/10/2022] [Indexed: 11/03/2022]
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Bian Y, Liu C, Li Q, Meng Y, Liu F, Zhang H, Fang X, Li J, Yu J, Feng X, Ma C, Zhao Z, Wang L, Xu J, Shao C, Lu J. Preoperative Radiomics Approach to Evaluating Tumor-Infiltrating CD8 + T Cells in Patients With Pancreatic Ductal Adenocarcinoma Using Noncontrast Magnetic Resonance Imaging. J Magn Reson Imaging 2022; 55:803-814. [PMID: 34355834 DOI: 10.1002/jmri.27871] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/20/2021] [Accepted: 07/20/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND CD8+ T cell in pancreatic ductal adenocarcinoma (PDAC) is closely related to the prognosis and treatment response of patients. Accurate preoperative CD8+ T-cell expression can better identify the population benefitting from immunotherapy. PURPOSE To develop and validate a machine learning classifier based on noncontrast magnetic resonance imaging (MRI) for the preoperative prediction of CD8+ T-cell expression in patients with PDAC. STUDY TYPE Retrospective cohort study. POPULATION Overall, 114 patients with PDAC undergoing MR scan and surgical resection; 97 and 47 patients in the training and validation cohorts. FIELD STRENGTH/SEQUENCE/3 T: Breath-hold single-shot fast-spin echo T2-weighted sequence and noncontrast T1-weighted fat-suppressed sequences. ASSESSMENT CD8+ T-cell expression was quantified using immunohistochemistry. For each patient, 2232 radiomics features were extracted from noncontrast T1- and T2-weighted images and reduced using the Wilcoxon rank-sum test and least absolute shrinkage and selection operator method. Linear discriminative analysis was used to construct radiomics and mixed models. Model performance was determined by its discriminative ability, calibration, and clinical utility. STATISTICAL TESTS Kaplan-Meier estimates, Student's t-test, the Kruskal-Wallis H test, and the chi-square test, receiver operating characteristic curve, and decision curve analysis. RESULTS A log-rank test showed that the survival duration in the CD8-high group (25.51 months) was significantly longer than that in the CD8-low group (22.92 months). The mixed model included all MRI characteristics and 13 selected radiomics features, and the area under the curve (AUC) was 0.89 (95% confidence interval [CI], 0.77-0.92) and 0.69 (95% CI, 0.53-0.82) in the training and validation cohorts. The radiomics model included 13 radiomics features, which showed good discrimination in the training cohort (AUC, 0.85; 95% CI, 0.77-0.92) and the validation cohort (AUC, 0.76; 95% CI, 0.61-0.87). DATA CONCLUSIONS This study developed a noncontrast MRI-based radiomics model that can preoperatively determine CD8+ T-cell expression in patients with PDAC and potentially immunotherapy planning. EVIDENCE LEVEL 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Cong Liu
- Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Zengrui Zhao
- Institute for AI in Medicine, School of Automation, Nanjing University of Information Science and Technology, Nanjing, 210044, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jun Xu
- Jiangsu Key Laboratory of Big Data Analysis Technique, School of Automation, Nanjing University of Information Science and Technology, Nanjing, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, China
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20
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Bera K, Braman N, Gupta A, Velcheti V, Madabhushi A. Predicting cancer outcomes with radiomics and artificial intelligence in radiology. Nat Rev Clin Oncol 2022; 19:132-146. [PMID: 34663898 PMCID: PMC9034765 DOI: 10.1038/s41571-021-00560-7] [Citation(s) in RCA: 257] [Impact Index Per Article: 128.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/03/2021] [Indexed: 12/14/2022]
Abstract
The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address other, more complex, clinical needs. In this Perspective, we discuss the next generation of challenges in clinical decision-making that AI tools can solve using radiology images, such as prognostication of outcome across multiple cancers, prediction of response to various treatment modalities, discrimination of benign treatment confounders from true progression, identification of unusual response patterns and prediction of the mutational and molecular profile of tumours. We describe the evolution of and opportunities for AI in oncology imaging, focusing on hand-crafted radiomic approaches and deep learning-derived representations, with examples of their application for decision support. We also address the challenges faced on the path to clinical adoption, including data curation and annotation, interpretability, and regulatory and reimbursement issues. We hope to demystify AI in radiology for clinicians by helping them to understand its limitations and challenges, as well as the opportunities it provides as a decision-support tool in cancer management.
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Affiliation(s)
- Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Tempus Labs, Chicago, IL, USA
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH, USA.
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21
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Bian Y, Liu YF, Jiang H, Meng Y, Liu F, Cao K, Zhang H, Fang X, Li J, Yu J, Feng X, Li Q, Wang L, Lu J, Shao C. Machine learning for MRI radiomics: a study predicting tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2021; 46:4800-4816. [PMID: 34189612 DOI: 10.1007/s00261-021-03159-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 12/25/2022]
Abstract
OBJECTIVE To develop and validate a machine learning classifier based on magnetic resonance imaging (MRI), for the preoperative prediction of tumor-infiltrating lymphocytes (TILs) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS In this retrospective study, 156 patients with PDAC underwent MR scan and surgical resection. The expression of CD4, CD8 and CD20 was detected and quantified using immunohistochemistry, and TILs score was achieved by Cox regression model. All patients were divided into TILs score-low and TILs score-high groups. The least absolute shrinkage and selection operator method and the extreme gradient boosting (XGBoost) were used to select the features and to construct a prediction model. The performance of the models was assessed using the training cohort (116 patients) and the validation cohort (40 patients), and decision curve analysis (DCA) was applied for clinical use. RESULTS The XGBoost prediction model showed good discrimination in the training (AUC 0.86; 95% CI 0.79-0.93) and validation sets (AUC 0.79; 95% CI 0.64-0.93). The sensitivity, specificity, and accuracy for the training set were 86.67%, 75.00%, and 0.81, respectively, whereas those for the validation set were 84.21%, 66.67%, and 0.75, respectively. Decision curve analysis indicated the clinical usefulness of the XGBoost classifier. CONCLUSION The model constructed by XGBoost could predict PDAC TILs and may aid clinical decision making for immune therapy.
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Affiliation(s)
- Yun Bian
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Yan Fang Liu
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Hui Jiang
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai, China
| | - Yinghao Meng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Kai Cao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Navy Medical University, Shanghai, 200434, China.
- Department of Radiology, Changhai Hospital, 168 Changhai Road, Shanghai, 200433, China.
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22
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Wu J, Li C, Gensheimer M, Padda S, Kato F, Shirato H, Wei Y, Schönlieb CB, Price SJ, Jaffray D, Heymach J, Neal JW, Loo BW, Wakelee H, Diehn M, Li R. Radiological tumor classification across imaging modality and histology. NAT MACH INTELL 2021; 3:787-798. [PMID: 34841195 PMCID: PMC8612063 DOI: 10.1038/s42256-021-00377-0] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 06/24/2021] [Indexed: 02/07/2023]
Abstract
Radiomics refers to the high-throughput extraction of quantitative features from radiological scans and is widely used to search for imaging biomarkers for prediction of clinical outcomes. Current radiomic signatures suffer from limited reproducibility and generalizability, because most features are dependent on imaging modality and tumor histology, making them sensitive to variations in scan protocol. Here, we propose novel radiological features that are specially designed to ensure compatibility across diverse tissues and imaging contrast. These features provide systematic characterization of tumor morphology and spatial heterogeneity. In an international multi-institution study of 1,682 patients, we discover and validate four unifying imaging subtypes across three malignancies and two major imaging modalities. These tumor subtypes demonstrate distinct molecular characteristics and prognoses after conventional therapies. In advanced lung cancer treated with immunotherapy, one subtype is associated with improved survival and increased tumor-infiltrating lymphocytes compared with the others. Deep learning enables automatic tumor segmentation and reproducible subtype identification, which can facilitate practical implementation. The unifying radiological tumor classification may inform prognosis and treatment response for precision medicine.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
- Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Chao Li
- The Centre for Mathematical Imaging in Healthcare, Department of Pure Mathematics and Mathematical Statistics, University of Cambridge, UK
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Michael Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Sukhmani Padda
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Fumi Kato
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Hiroki Shirato
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Yiran Wei
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | | | - Stephen John Price
- Cambridge Brain Tumor Imaging Laboratory, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David Jaffray
- Department of Imaging Physics, MD Anderson Cancer Center, Houston, TX, USA
- Office of the Chief Technology and Digital Officer, MD Anderson Cancer Center, Houston, TX, USA
| | - John Heymach
- Department of Thoracic and Head and Neck Medical Oncology, MD Anderson Cancer Center, Houston, TX, USA
| | - Joel W Neal
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Billy W Loo
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Heather Wakelee
- Department of Medicine, Division of Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Maximilian Diehn
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
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Human Epidermal Growth Factor Receptor Type 2-Positive Breast Cancer: Association of MRI and Clinicopathologic Features With Tumor-Infiltrating Lymphocytes. AJR Am J Roentgenol 2021; 218:258-269. [PMID: 34431365 DOI: 10.2214/ajr.21.26400] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Tumor-infiltrating lymphocytes (TILs) are associated with therapeutic outcomes and prognosis in patients with human epidermal growth factor receptor type 2-positive (HER2+) breast cancer. Identification of TIL levels is clinically relevant. Objective: To explore associations of clinicopathologic and MRI features with TIL levels in patients with HER2+ breast cancer. Methods: A total of 212 consecutive women (mean age, 54 years) diagnosed with HER2+ breast cancer between January 2017 and December 2019 were included in this retrospective study. Patients were divided into low (<10%) and high (≥10%) TIL groups. Three breast radiologists independently reviewed images; interreader agreement was assessed, and the first readers' findings were used for further analysis. Associations of clinicopathologic and MRI features with TIL levels were evaluated using multivariable logistic regression analysis. Subanalysis of TIL levels by hormone receptor (HR) status was also performed. Results: A total of 115 (54.2%) patients had low, and 97 (45.8%) had high, TIL levels. High TIL level was associated (all p<.05) with histologic grade 3 (odds ratio [OR]=3.98; frequency of 78.4% vs 52.2% in high vs low TIL groups, respectively), high tumor cellularity (OR=4.59; median cellularity of 60% vs 50%), lower frequency of associated ductal carcinoma in situ (OR=0.16; frequency of 86.6% vs 94.8%), and higher frequency of peritumoral edema on T2-weighted images (OR=2.83; 71.1% vs 50.4%). In subgroup analysis by HR status, histologic grade 3 (OR=5.03, p=.002) was a significant independent predictor of high TIL in the HR+/HER2+ group, while high tumor cellularity (OR=9.06, p=.002), peritumoral edema (OR=5.23, p=.03), and low ADC (OR=11.69, p=.047) were independent predictors of high TIL in the HR-/HER2+ group. Interreader agreement for peritumoral edema was moderate among the three radiologists (к, range 0.432-0.539). Conclusion: Peritumoral edema on MRI and histopathologic feature of tumor aggressiveness help predict high TIL levels in patients with HER2+ breast cancer. Clinical Impact: Pretreatment MRI features may serve as a useful tool for assessing TIL levels in patients with HER2+ breast cancer, helping to classify patients with variable clinical outcomes related to immune activity and to guide selection among neoadjuvant chemotherapy (NAC) or HER2-targeted therapy or immunotherapy.
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24
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A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8 + cells in locally advanced rectal cancer. Radiother Oncol 2021; 162:124-131. [PMID: 34265357 DOI: 10.1016/j.radonc.2021.07.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 07/02/2021] [Accepted: 07/03/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND AND PURPOSE Regarding the altered tumor immune status following cytotoxic treatment, this study aims to develop a radiomic signature to predict CD8+ tumor-infiltrating lymphocyte (TIL) density changes in chemoradiotherapy (CRT) of rectal cancer. MATERIALS AND METHODS We used the magnetic resonance imaging (MRI) and immunohistochemistry data before and after neoadjuvant CRT. The discovery datasets consisted of pre-CRT dataset A1 (n = 113), post-CRT datasets A2 (n = 32; predominance of tumor) and A3 (n = 20; pure fibrosis). The developed model was validated in dataset B (n = 28). Thirty-eight radiomic features from T2-weighted MRI scans were incorporated into the least absolute shrinkage and selection operator method. RESULTS In pre-CRT dataset A1, the area under the receiver operating characteristic curve (AUC) values of radiomic score for predicting CD8+ TILs were 0.760 and 0.729 for training and validation subsets, respectively. A significant correlation was observed between the signature and CD8+ TIL density in the post-CRT dataset A2 (Pearson's R = -0.372, P = 0.036), whereas no association was found in dataset A3 (Pearson's R = -0.069, P = 0.77). The association was also observed in the validation dataset B (Pearson's R = -0.374, P = 0.049). In dataset A2, the radiomic score difference predicted changes in CD8+ TIL density (AUC = 0.824). CONCLUSION We established the MRI-derived radiomic signature for predicting CRT-induced alterations in CD8+ TILs. This study suggests the clinical utility of radiomics-immunophenotype modeling to evaluate tumor immune status following neoadjuvant chemoradiation in rectal cancer.
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25
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Jiang Y, Liang X, Han Z, Wang W, Xi S, Li T, Chen C, Yuan Q, Li N, Yu J, Xie Y, Xu Y, Zhou Z, Poultsides GA, Li G, Li R. Radiographical assessment of tumour stroma and treatment outcomes using deep learning: a retrospective, multicohort study. LANCET DIGITAL HEALTH 2021; 3:e371-e382. [PMID: 34045003 DOI: 10.1016/s2589-7500(21)00065-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 03/16/2021] [Accepted: 04/07/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND The tumour stroma microenvironment plays an important part in disease progression and its composition can influence treatment response and outcomes. Histological evaluation of tumour stroma is limited by access to tissue, spatial heterogeneity, and temporal evolution. We aimed to develop a radiological signature for non-invasive assessment of tumour stroma and treatment outcomes. METHODS In this multicentre, retrospective study, we analysed CT images and outcome data of 2209 patients with resected gastric cancer from five independent cohorts recruited from two centres (Nanfang Hospital of Southern Medical University [Guangzhou, China] and Sun Yat-sen University Cancer Center [Guangzhou, China]). Patients with histologically confirmed gastric cancer, at least 15 lymph nodes harvested, preoperative abdominal CT available, and complete clinicopathological and follow-up data were eligible for inclusion. Tumour tissue was collected for patients in the training cohort (321 patients), internal validation cohort one (246 patients), and external validation cohort one (128 patients). Four stroma classes were defined according to the protein expression of α-smooth muscle actin and periostin assessed by immunohistochemistry. The primary objective was to predict the histologically based stroma classes by using preoperative CT images. We trained a deep convolutional neural network model using the training cohort and tested the model in the internal and external validation cohort one. We evaluated the model's association with prognosis in the training cohort, two internal, and two external validation cohorts and compared outcomes of patients who received or did not receive adjuvant chemotherapy. FINDINGS The deep-learning model achieved a high diagnostic accuracy for assessing tumour stroma in both internal validation cohort one (area under the receiver operating characteristic curve [AUC] 0·96-0·98]) and external validation cohort one (AUC 0·89-0·94). The stromal imaging signature was significantly associated with disease-free survival and overall survival in all cohorts (p<0·0001). The predicted stroma classes remained an independent prognostic factor adjusting for clinicopathological variables including tumour size, stage, differentiation, and Lauren histology. In patients with stage II or III disease in predicted stroma classes one and two subgroups, patients who received adjuvant chemotherapy had improved survival compared with those who did not (in those with stage II disease hazard ratio [HR] 0·48 [95% CI 0·29-0·77], p=0·0021; and in those with stage III disease HR 0·70 [0·57-0·85], p=0·00042). However, in the other two subgroups adjuvant chemotherapy was not associated with survival and might even be detrimental in the predicted stroma class 4 subgroup (HR 1·48 [1·08-2·03], p=0·013). INTERPRETATION The deep-learning model could allow for accurate and non-invasive evaluation of tumour stroma from CT images in gastric cancer. The radiographical model predicted chemotherapy outcomes and could be used in combination with clinicopathological criteria to refine prognosis and inform treatment decisions of patients with gastric cancer. FUNDING None.
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Affiliation(s)
- Yuming Jiang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA
| | - Xiaokun Liang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Zhen Han
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Guangzhou, China
| | - Wei Wang
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Sujuan Xi
- The Reproductive Medical Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, China
| | - Tuanjie Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Guangzhou, China
| | - Chuanli Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Qingyu Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Na Li
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Jiang Yu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China; Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Guangzhou, China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China; Shenzhen Colleges of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, China
| | - Yikai Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Zhiwei Zhou
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - George A Poultsides
- Department of Surgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Guoxin Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, China; Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA.
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Xu C, Yu J, Wu F, Li X, Hu D, Chen G, Wu G. High-background parenchymal enhancement in the contralateral breast is an imaging biomarker for favorable prognosis in patients with triple-negative breast cancer treated with chemotherapy. Am J Transl Res 2021; 13:4422-4436. [PMID: 34150024 PMCID: PMC8205756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 03/12/2021] [Indexed: 06/12/2023]
Abstract
This study aimed to analyze the association between background parenchymal enhancement (BPE) in the contralateral breast tissue on magnetic resonance imaging (MRI) and clinicopathologic parameters in patients with unilateral breast carcinoma and to investigate its potential prognostic significance. A total of 467 patients who were pathologically confirmed to have unilateral breast cancer and underwent breast MRI were recruited to participate in this cohort study. BPE was assessed in the healthy contralateral breast. Minimal and mild levels were classified as low BPE, whereas moderate and marked levels were classified as high BPE. The effects of BPE on clinicopathologic parameters, overall survival (OS), and invasive disease-free survival (IDFS) were determined. Among the 467 patients, 327 cases were classified into the low-BPE group, whereas 140 cases were classified into the high-BPE group. The high-BPE pattern markedly correlated with age at diagnosis, menopausal status, histologic grading, and estrogen receptor status. BPE pattern did not correlate with OS and IDFS in the entire breast cancer cohort, regardless of whether adjuvant chemotherapy was received. Notably, BPE in the healthy contralateral breast on MRI is markedly related to OS and IDFS in triple-negative breast cancer (TNBC) cases who received chemotherapy. High BPE is related to chemotherapeutic benefits and can be an independent favorable prognostic factor for TNBC patients. Thus, our observations suggest that high BPE pattern can potentially be used as an imaging biomarker for relatively favorable prognosis in TNBC cases receiving chemotherapy. However, the findings need to be verified in a large-scale study.
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Affiliation(s)
- Chuanhui Xu
- Department of Radiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan UniversityShanghai, China
| | - Jinhui Yu
- Department of Radiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan UniversityShanghai, China
| | - Feifei Wu
- Department of Radiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan UniversityShanghai, China
| | - Xuemei Li
- Department of Radiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan UniversityShanghai, China
| | - Dongmin Hu
- Department of Radiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan UniversityShanghai, China
| | - Guiming Chen
- Department of General Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of MedicineShanghai, China
| | - Gang Wu
- Department of Radiology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan UniversityShanghai, China
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Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD. Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 2021; 28:97-115. [PMID: 33937530 PMCID: PMC8076712 DOI: 10.1016/j.ctro.2021.03.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/20/2021] [Accepted: 03/24/2021] [Indexed: 02/08/2023] Open
Abstract
Immunotherapies are leading to improved outcomes for many cancers, including those with devastating prognoses. As therapies like immune checkpoint inhibitors (ICI) become a mainstay in treatment regimens, many concurrent challenges have arisen - for instance, delineating clinical responders from non-responders. Predicting response has proven to be difficult given a lack of consistent and accurate biomarkers, heterogeneity of the tumor microenvironment (TME), and a poor understanding of resistance mechanisms. For the most part, imaging data have remained an untapped, yet abundant, resource to address these challenges. In recent years, quantitative image analyses have highlighted the utility of medical imaging in predicting tumor phenotypes, prognosis, and therapeutic response. These studies have been fueled by an explosion of resources in high-throughput mining of image features (i.e. radiomics) and artificial intelligence. In this review, we highlight current progress in radiomics to understand tumor immune biology and predict clinical responses to immunotherapies. We also discuss limitations in these studies and future directions for the field, particularly if high-dimensional imaging data are to play a larger role in precision medicine.
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Affiliation(s)
- Jarey H. Wang
- Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, United States
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Lisanne V. van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, United States
| | - Reid F. Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, OR, United States
| | - Clifton David Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
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Xiao J, Rahbar H, Hippe DS, Rendi MH, Parker EU, Shekar N, Hirano M, Cheung KJ, Partridge SC. Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis. NPJ Breast Cancer 2021; 7:42. [PMID: 33863924 PMCID: PMC8052427 DOI: 10.1038/s41523-021-00247-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 03/15/2021] [Indexed: 02/02/2023] Open
Abstract
Angiogenesis is a critical component of breast cancer development, and identification of imaging-based angiogenesis assays has prognostic and treatment implications. We evaluated the association of semi-quantitative kinetic and radiomic breast cancer features on dynamic contrast-enhanced (DCE)-MRI with microvessel density (MVD), a marker for angiogenesis. Invasive breast cancer kinetic features (initial peak percent enhancement [PE], signal enhancement ratio [SER], functional tumor volume [FTV], and washout fraction [WF]), radiomics features (108 total features reflecting tumor morphology, signal intensity, and texture), and MVD (by histologic CD31 immunostaining) were measured in 27 patients (1/2016-7/2017). Lesions with high MVD levels demonstrated higher peak SER than lesions with low MVD (mean: 1.94 vs. 1.61, area under the receiver operating characteristic curve [AUC] = 0.79, p = 0.009) and higher WF (mean: 50.6% vs. 22.5%, AUC = 0.87, p = 0.001). Several radiomics texture features were also promising for predicting increased MVD (maximum AUC = 0.84, p = 0.002). Our study suggests DCE-MRI can non-invasively assess breast cancer angiogenesis, which could stratify biology and optimize treatments.
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Affiliation(s)
- Jennifer Xiao
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Habib Rahbar
- Department of Radiology, University of Washington, Seattle, WA, USA
- Breast Imaging, Seattle Cancer Care Alliance, Seattle, WA, USA
| | - Daniel S Hippe
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Mara H Rendi
- Department of Pathology, University of Washington, Seattle, WA, USA
| | | | - Neal Shekar
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Michael Hirano
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Kevin J Cheung
- Department of Medicine, Division of Medical Oncology, University of Washington, Seattle, WA, USA
- Translational Research Program, Public Health Sciences and Human Biology Divisions, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Savannah C Partridge
- Department of Radiology, University of Washington, Seattle, WA, USA.
- Breast Imaging, Seattle Cancer Care Alliance, Seattle, WA, USA.
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Yu H, Meng X, Chen H, Liu J, Gao W, Du L, Chen Y, Wang Y, Liu X, Liu B, Fan J, Ma G. Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features. Front Oncol 2021; 11:628577. [PMID: 33777776 PMCID: PMC7991288 DOI: 10.3389/fonc.2021.628577] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 01/29/2021] [Indexed: 12/26/2022] Open
Abstract
Objectives This study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer. Methods Data from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann–Whitney U test to evaluate the level of TILs in the low and high groups. Results Among the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738–0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615–0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively). Conclusion Radiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.
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Affiliation(s)
- Hongwei Yu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
| | - Xianqi Meng
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Huang Chen
- Department of Pathology, China-Japan Friendship Hospital, Beijing, China
| | - Jian Liu
- Department of Ultrasound medicine, China-Japan Friendship Hospital, Beijing, China
| | - Wenwen Gao
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Lei Du
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yue Chen
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Yige Wang
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiuxiu Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China
| | - Bing Liu
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China.,Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingfan Fan
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
| | - Guolin Ma
- Department of Radiology, China-Japan Friendship Hospital, Beijing, China
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30
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Yamaguchi K, Hara Y, Kitano I, Hamamoto T, Kiyomatsu K, Yamasaki F, Yamaguchi R, Nakazono T, Egashira R, Imaizumi T, Irie H. Relationship between MRI findings and invasive breast cancer with podoplanin-positive cancer-associated fibroblasts. Breast Cancer 2021; 28:572-580. [PMID: 33389554 DOI: 10.1007/s12282-020-01198-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 11/19/2020] [Indexed: 10/22/2022]
Abstract
OBJECTIVE Purpose of our study is to assess the relationship between MRI findings and invasive breast cancer (IBC) with cancer-associated fibroblasts (CAFs) that are positive for podoplanin. METHODS We retrospectively analyzed the consecutive 109 IBCs. The IBCs were dichotomized as with (+) or without (-) podoplanin-positive CAFs. In MRI analyses, the dichotomized IBCs were compared the lesion to muscle ratio (L/M ratio) in STIR images, the ADC value, the distribution of kinetic parameters, and morphological findings. RESULTS Of the 109 IBCs, 28 (26%) IBCs had podoplanin(+) CAFs. Compared to the podoplanin(-) group, the podoplanin(+) group tended to have a more malignant pathological status. In the STIR images, the podoplanin(+) group had significantly higher L/M ratio (7.59 vs. 6.55, p = 0.040). In a dynamic study, the podoplanin(+) group had a significantly higher percentage of the washout pattern (42.21% vs. 29.43%, p = 0.045). There were 23 mass lesions and 5 non-mass enhancement (NME) lesions in the podoplanin(+) group, and 69 mass lesions and 12 NME lesions in the podoplanin(-) group. The mass lesions of the podoplanin(-) group had a significantly higher likelihood of showing an irregular shape (n = 47 vs. 8, p = 0.035). The podoplanin(+) group's lesions had a significantly higher likelihood of showing a circumscribed margin (n = 14 vs. 6, p < 0.001) and a rim enhancement (n = 10 vs. 13, p = 0.047). In multivariate analyses, only high nuclear grade was significant predictive value of podoplanin(+) CAFs. CONCLUSION Although not significant in multivariate analyses, MRI findings may be used to determine the podoplanin-positive CAF status of invasive breast cancer.
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Affiliation(s)
- Ken Yamaguchi
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan.
| | - Yukiko Hara
- Department of Radiology, Saga Central Hospital, 3-8-1 Hyogominami, Saga, 849-8522, Japan
| | - Isao Kitano
- Department of Radiology, Saga Central Hospital, 3-8-1 Hyogominami, Saga, 849-8522, Japan
| | | | - Kazumitsu Kiyomatsu
- Department of Surgery, Saga Central Hospital, 3-8-1 Hyogominami, Saga, 849-8522, Japan
| | - Fumio Yamasaki
- Department of Pathology, Saga Central Hospital, 3-8-1 Hyogominami, Saga, 849-8522, Japan
| | - Rin Yamaguchi
- Department of Pathology and Laboratory Medicine, Kurume University Medical Center, 155-1 Kokubu, Kurume, 859-0863, Japan
| | - Takahiko Nakazono
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Ryoko Egashira
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Takeshi Imaizumi
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Hiroyuki Irie
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
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Radiogenomic signatures reveal multiscale intratumour heterogeneity associated with biological functions and survival in breast cancer. Nat Commun 2020; 11:4861. [PMID: 32978398 PMCID: PMC7519071 DOI: 10.1038/s41467-020-18703-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Accepted: 09/08/2020] [Indexed: 12/24/2022] Open
Abstract
Advanced tumours are often heterogeneous, consisting of subclones with various genetic alterations and functional roles. The precise molecular features that characterize the contributions of multiscale intratumour heterogeneity to malignant progression, metastasis, and poor survival are largely unknown. Here, we address these challenges in breast cancer by defining the landscape of heterogeneous tumour subclones and their biological functions using radiogenomic signatures. Molecular heterogeneity is identified by a fully unsupervised deconvolution of gene expression data. Relative prevalence of two subclones associated with cell cycle and primary immunodeficiency pathways identifies patients with significantly different survival outcomes. Radiogenomic signatures of imaging scale heterogeneity are extracted and used to classify patients into groups with distinct subclone compositions. Prognostic value is confirmed by survival analysis accounting for clinical variables. These findings provide insight into how a radiogenomic analysis can identify the biological activities of specific subclones that predict prognosis in a noninvasive and clinically relevant manner. Tumours are made up of heterogeneous subclones. Here, the authors show using breast cancer imaging and gene expression datasets that these subclones can be inferred by the deconvolution of gene expression data, mapped to MRI derived radiogenomic signatures and used to estimate prognosis.
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Zhang N, Liang R, Gensheimer MF, Guo M, Zhu H, Yu J, Diehn M, Loo BW, Li R, Wu J. Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. Am J Cancer Res 2020; 10:11707-11718. [PMID: 33052242 PMCID: PMC7546006 DOI: 10.7150/thno.50565] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/08/2020] [Indexed: 12/25/2022] Open
Abstract
Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.
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Jiang Y, Wang H, Wu J, Chen C, Yuan Q, Huang W, Li T, Xi S, Hu Y, Zhou Z, Xu Y, Li G, Li R. Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer. Ann Oncol 2020; 31:760-768. [PMID: 32240794 DOI: 10.1016/j.annonc.2020.03.295] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 03/15/2020] [Accepted: 03/18/2020] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND The tumor immune microenvironment can provide prognostic and predictive information. A previously validated ImmunoScore of Gastric Cancer (ISGC) evaluates both lymphoid and myeloid cells in the tumor core and invasive margin with immunohistochemical staining of surgical specimens. We aimed to develop a noninvasive radiomics-based predictor of ISGC. PATIENTS AND METHODS In this retrospective study including four independent cohorts of 1778 patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced computed tomography images. A radiomic signature [radiomics ImmunoScore (RIS)] was constructed to predict ISGC using regularized logistic regression. We further evaluated its association with prognosis and chemotherapy response. RESULTS A 13-feature radiomic signature for ISGC was developed and validated in three independent cohorts (area under the curve = 0.786, 0.745, and 0.766). The RIS signature was significantly associated with both disease-free and overall survival in the training and all validation cohorts [hazard ratio (HR) range: 0.296-0.487, all P < 0.001]. In multivariable analysis, the RIS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HR range: 0.339-0.605, all P < 0.003). For stage II and stage III disease, patients with a high RIS derived survival benefit from adjuvant chemotherapy {HR = 0.436 [95% confidence interval (CI) 0.253-0.753], P = 0.002; HR = 0.591 (95% CI 0.428-0.818), P < 0.001, respectively}, whereas those with a low RIS did not. CONCLUSION The RIS is a reliable tool for evaluation of immunoscore and retains the prognostic significance in gastric cancer. Future prospective studies are required to confirm its potential to predict treatment response and select patients who will benefit from chemotherapy.
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Affiliation(s)
- Y Jiang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou; Guangdong Provincial Key Laboratory on Precision and Minimally Invasive Medicine for Gastrointestinal Cancers, Guangzhou, China; Department of Radiation Oncology, Stanford University School of Medicine, Stanford, USA
| | - H Wang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, USA
| | - J Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, USA
| | - C Chen
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Q Yuan
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - W Huang
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou; Guangdong Provincial Key Laboratory on Precision and Minimally Invasive Medicine for Gastrointestinal Cancers, Guangzhou, China
| | - T Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou; Guangdong Provincial Key Laboratory on Precision and Minimally Invasive Medicine for Gastrointestinal Cancers, Guangzhou, China
| | - S Xi
- The Seventh Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Y Hu
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou; Guangdong Provincial Key Laboratory on Precision and Minimally Invasive Medicine for Gastrointestinal Cancers, Guangzhou, China
| | - Z Zhou
- Department of Gastric Surgery, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Y Xu
- Department of Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - G Li
- Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou; Guangdong Provincial Key Laboratory on Precision and Minimally Invasive Medicine for Gastrointestinal Cancers, Guangzhou, China.
| | - R Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, USA.
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Sun H, Li C, Liu B, Liu Z, Wang M, Zheng H, Dagan Feng D, Wang S. AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms. Phys Med Biol 2020; 65:055005. [PMID: 31722327 DOI: 10.1088/1361-6560/ab5745] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high- and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.
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Affiliation(s)
- Hui Sun
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, People's Republic of China. School of Control Science and Engineering, Shandong University, Jinan, Shandong 250100, People's Republic of China. These authors contribute equally to this paper
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Choi WJ, Kim Y, Cha JH, Shin HJ, Chae EY, Yoon GY, Kim HH. Correlation between magnetic resonance imaging and the level of tumor-infiltrating lymphocytes in patients with estrogen receptor-negative HER2-positive breast cancer. Acta Radiol 2020; 61:3-10. [PMID: 31109192 DOI: 10.1177/0284185119851235] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background High levels of tumor-infiltrating lymphocytes (TILs) are associated with improved prognosis and response to therapy in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Purpose This study investigated the associations between TIL levels and magnetic resonance imaging (MRI) findings in patients with estrogen receptor (ER)-negative HER-2 positive breast cancer. Material and Methods This study included 110 consecutive patients with surgically confirmed ER-negative HER2-positive breast cancers who underwent preoperative MRI from January to December 2015. Images of all lesions were reviewed in accordance with the BI-RADS lexicon by radiologists blinded to clinicopathologic findings. Tumor kinetic features were acquired by computer-aided diagnosis (CAD). Patients were divided into three TIL groups: low (<10%); intermediate (10–50%); and high (>50%). Associations between TIL levels and clinicopathologic and imaging features were evaluated; independent predictors of high and low TIL were identified by multiple logistic regression analysis. Results The 110 patients included 29 (26.4%) with low, 45 (40.9%) with intermediate, and 36 (32.7%) with high TIL levels. Multiple logistic regression analysis showed that older age (odds ratio [OR] = 1.08; P = 0.017), high peak enhancement (OR = 1.01; P = 0.019), positive CK5/6 (OR = 4.36; P = 0.024), and low Ki-67 (OR = 14.29; P = 0.037) were significantly associated with low TILs; low peak enhancement (OR = 1.01; P = 0.020) was significantly associated with high TILs. Conclusion MRI features may predict TIL levels in patients with ER-negative HER-2 positive breast cancer, enhancing the ability to diagnose and treat these patients.
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Affiliation(s)
- Woo Jung Choi
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Youyeon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Joo Hee Cha
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hee Jung Shin
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Eun Young Chae
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Ga Young Yoon
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Hak Hee Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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Usefulness of imaging findings in predicting tumor-infiltrating lymphocytes in patients with breast cancer. Eur Radiol 2019; 30:2049-2057. [PMID: 31822972 DOI: 10.1007/s00330-019-06516-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 09/26/2019] [Accepted: 10/16/2019] [Indexed: 12/18/2022]
Abstract
OBJECTIVES Tumor-infiltrating lymphocytes (TILs) have been determined as a new prognostic indicator of immunotherapy response in breast cancer (BC). The aim of this study is to investigate the effectiveness of imaging features in predicting the TIL levels in invasive BC patients. METHODS A total of 158 patients with invasive BC were included in our study. All lesions were evaluated based on the BIRADS lexicon. US was performed for all the patients and 89 of them underwent MRI. The histologic stromal TIL (sTIL) levels were assessed and associations between the sTIL levels and imaging features were evaluated. RESULTS Tumors with high sTIL levels had more circumscribed margins, round shape, heterogeneous echogenicity, and larger size on ultrasonography (p < 0.005). There was a statistically significant positive correlation between the sTIL levels and ADC value (p < 0.001). Tumors with high sTIL levels had significantly more homogeneous enhancement than the tumors with low sTIL levels (p = 0.001). Logistic regression analysis showed that the ADC was the most statistically significant parameter in predicting the sTIL levels (the odds ratio was 90.952; p = 0.002). The optimal cutoff value for ADC in predicting low and high sTIL levels was found to be 0.87 × 10-3 mm2 s-1 (AUC = 0.726, 73% specificity, and 60% sensitivity). CONCLUSIONS Imaging findings, especially the ADC, may play an important role as an adjunct tool in cases of uncertain situations and may improve the accuracy of biopsy results. The prediction of sTIL levels using imaging findings may give an opportunity to predict prognosis. KEY POINTS • Preoperative assessment of TILs is an important biomarker of prognosis and treatment efficacy. • ADC value can be a useful tool in distinguishing high and low sTIL levels as a non-invasive method. • The prediction of sTIL levels using imaging findings may give an opportunity to predict prognosis and an optimal treatment for the BC patients.
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Li X, Yin G, Zhang Y, Dai D, Liu J, Chen P, Zhu L, Ma W, Xu W. Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC. Front Oncol 2019; 9:1062. [PMID: 31681597 PMCID: PMC6803612 DOI: 10.3389/fonc.2019.01062] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 09/30/2019] [Indexed: 12/13/2022] Open
Abstract
Radiomics has become an area of interest for tumor characterization in 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.
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Affiliation(s)
- Xiaofeng Li
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Guotao Yin
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yufan Zhang
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Dong Dai
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Jianjing Liu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Peihe Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Lei Zhu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Wenjuan Ma
- National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China.,Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China.,National Clinical Research Center for Cancer, Tianjin, China.,Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.,Tianjin's Clinical Research Center for Cancer, Tianjin, China
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38
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Wu J, Gensheimer MF, Zhang N, Han F, Liang R, Qian Y, Zhang C, Fischbein N, Pollom EL, Beadle B, Le QT, Li R. Integrating Tumor and Nodal Imaging Characteristics at Baseline and Mid-Treatment Computed Tomography Scans to Predict Distant Metastasis in Oropharyngeal Cancer Treated With Concurrent Chemoradiotherapy. Int J Radiat Oncol Biol Phys 2019; 104:942-952. [PMID: 30940529 PMCID: PMC6579673 DOI: 10.1016/j.ijrobp.2019.03.036] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Revised: 03/11/2019] [Accepted: 03/20/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE Prognostic biomarkers of disease relapse are needed for risk-adaptive therapy of oropharyngeal cancer (OPC). This work aims to identify an imaging signature to predict distant metastasis in OPC. METHODS AND MATERIALS This single-institution retrospective study included 140 patients treated with definitive concurrent chemoradiotherapy, for whom both pre- and midtreatment contrast-enhanced computed tomography (CT) scans were available. Patients were divided into separate training and testing cohorts. Forty-five quantitative image features were extracted to characterize tumor and involved lymph nodes at both time points. By incorporating both imaging and clinicopathological features, a random survival forest (RSF) model was built to predict distant metastasis-free survival (DMFS). The model was optimized via repeated cross-validation in the training cohort and then independently validated in the testing cohort. RESULTS The most important features for predicting DMFS were the maximum distance among nodes, maximum distance between tumor and nodes at mid-treatment, and pretreatment tumor sphericity. In the testing cohort, the RSF model achieved good discriminability for DMFS (C-index = 0.73, P = .008), and further divided patients into 2 risk groups with different 2-year DMFS rates: 96.7% versus 67.6%. Similar trends were observed for patients with p16+ tumors and smoking ≤10 pack-years. The RSF model based on pretreatment CT features alone achieved lower performance (concordance index = 0.68, P = .03). CONCLUSIONS Integrating tumor and nodal imaging characteristics at baseline and mid-treatment CT allows prediction of distant metastasis in OPC. The proposed imaging signature requires prospective validation and, if successful, may help identify high-risk human papillomavirus-positive patients who should not be considered for deintensification therapy.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Micheal F Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Nasha Zhang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong, China
| | - Fei Han
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
| | - Rachel Liang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Yushen Qian
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Carrie Zhang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Nancy Fischbein
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Erqi L Pollom
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Beth Beadle
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California.
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39
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Reig B, Heacock L, Geras KJ, Moy L. Machine learning in breast MRI. J Magn Reson Imaging 2019; 52:998-1018. [PMID: 31276247 DOI: 10.1002/jmri.26852] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/13/2022] Open
Abstract
Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications of machine learning to breast MRI continue to expand rapidly as increasingly accurate 3D breast and lesion segmentation allows the combination of radiologist-level interpretation (eg, BI-RADS lexicon), data from advanced multiparametric imaging techniques, and patient-level data such as genetic risk markers. Advances in breast MRI feature extraction have led to rapid dataset analysis, which offers promise in large pooled multiinstitutional data analysis. The object of this review is to provide an overview of machine-learning and deep-learning techniques for breast MRI, including supervised and unsupervised methods, anatomic breast segmentation, and lesion segmentation. Finally, it explores the role of machine learning, current limitations, and future applications to texture analysis, radiomics, and radiogenomics. Level of Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:998-1018.
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Affiliation(s)
- Beatriu Reig
- The Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Laura Heacock
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Krzysztof J Geras
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
| | - Linda Moy
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.,Center for Advanced Imaging Innovation and Research (CAI2 R), New York University School of Medicine, New York, New York, USA
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40
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Alvi E, Gupta R, Borok RZ, Escobar-Hoyos L, Shroyer KR. Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI. J Magn Reson Imaging 2019; 51:341-354. [PMID: 31041822 DOI: 10.1002/jmri.26763] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Accepted: 04/13/2019] [Indexed: 01/03/2023] Open
Abstract
Clinical practice in radiology and pathology requires professional expertise and many years of training to visually evaluate and interpret abnormal phenotypic features in medical images and tissue sections to generate diagnoses that guide patient management and treatment. Recent advances in digital image analysis methods and machine learning have led to significant interest in extracting additional information from medical and digital whole-slide images in radiology and pathology, respectively. This has led to significant interest and research in radiomics and pathomics to correlate phenotypic features of disease with image analytics in order to identify image-based biomarkers. The expanding role of big data in radiology and pathology parallels the development and role of immunohistochemistry (IHC) in the daily practice of pathology. IHC methods were initially developed to provide additional information to help classify tumors and then transformed into an indispensable tool to guide treatment in many types of cancer. IHC markers are used in daily practice to identify specific types of cells and highlight their distributions in tissues in order to distinguish benign from neoplastic cells, determine tumor origin, subclassify neoplasms, and support and confirm diagnoses. In this regard, radiomics, pathomics, and IHC methods are very similar since they enable the extraction of image-based features to characterize various properties of diseases. Due to the dramatic advancements in recent radiomics research, we provide a brief overview of the role of established and emerging IHC biomarkers in various tumor types that have been correlated with radiologic biomarkers to improve diagnostic accuracy, predict prognosis, guide patient management, and select treatment strategies. Level of Evidence: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:341-354.
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Affiliation(s)
- Emaan Alvi
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Rajarsi Gupta
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, USA.,Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, USA
| | - Raphael Z Borok
- Department of Pathology, Advocate Good Samaritan Hospital, Downers Grove, Illinois, USA
| | - Luisa Escobar-Hoyos
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, USA.,David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Biology, Genetic Toxicology and Cytogenetics Research Group, School of Natural Sciences and Education, Universidad Del Cauca, Popayán, Colombia
| | - Kenneth R Shroyer
- Department of Pathology, Renaissance School of Medicine, Stony Brook University, Stony Brook, New York, USA
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41
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Braman N, Prasanna P, Whitney J, Singh S, Beig N, Etesami M, Bates DDB, Gallagher K, Bloch BN, Vulchi M, Turk P, Bera K, Abraham J, Sikov WM, Somlo G, Harris LN, Gilmore H, Plecha D, Varadan V, Madabhushi A. Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)-Positive Breast Cancer. JAMA Netw Open 2019; 2:e192561. [PMID: 31002322 PMCID: PMC6481453 DOI: 10.1001/jamanetworkopen.2019.2561] [Citation(s) in RCA: 185] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
IMPORTANCE There has been significant recent interest in understanding the utility of quantitative imaging to delineate breast cancer intrinsic biological factors and therapeutic response. No clinically accepted biomarkers are as yet available for estimation of response to human epidermal growth factor receptor 2 (currently known as ERBB2, but referred to as HER2 in this study)-targeted therapy in breast cancer. OBJECTIVE To determine whether imaging signatures on clinical breast magnetic resonance imaging (MRI) could noninvasively characterize HER2-positive tumor biological factors and estimate response to HER2-targeted neoadjuvant therapy. DESIGN, SETTING, AND PARTICIPANTS In a retrospective diagnostic study encompassing 209 patients with breast cancer, textural imaging features extracted within the tumor and annular peritumoral tissue regions on MRI were examined as a means to identify increasingly granular breast cancer subgroups relevant to therapeutic approach and response. First, among a cohort of 117 patients who received an MRI prior to neoadjuvant chemotherapy (NAC) at a single institution from April 27, 2012, through September 4, 2015, imaging features that distinguished HER2+ tumors from other receptor subtypes were identified. Next, among a cohort of 42 patients with HER2+ breast cancers with available MRI and RNaseq data accumulated from a multicenter, preoperative clinical trial (BrUOG 211B), a signature of the response-associated HER2-enriched (HER2-E) molecular subtype within HER2+ tumors (n = 42) was identified. The association of this signature with pathologic complete response was explored in 2 patient cohorts from different institutions, where all patients received HER2-targeted NAC (n = 28, n = 50). Finally, the association between significant peritumoral features and lymphocyte distribution was explored in patients within the BrUOG 211B trial who had corresponding biopsy hematoxylin-eosin-stained slide images. Data analysis was conducted from January 15, 2017, to February 14, 2019. MAIN OUTCOMES AND MEASURES Evaluation of imaging signatures by the area under the receiver operating characteristic curve (AUC) in identifying HER2+ molecular subtypes and distinguishing pathologic complete response (ypT0/is) to NAC with HER2-targeting. RESULTS In the 209 patients included (mean [SD] age, 51.1 [11.7] years), features from the peritumoral regions better discriminated HER2-E tumors (maximum AUC, 0.85; 95% CI, 0.79-0.90; 9-12 mm from the tumor) compared with intratumoral features (AUC, 0.76; 95% CI, 0.69-0.84). A classifier combining peritumoral and intratumoral features identified the HER2-E subtype (AUC, 0.89; 95% CI, 0.84-0.93) and was significantly associated with response to HER2-targeted therapy in both validation cohorts (AUC, 0.80; 95% CI, 0.61-0.98 and AUC, 0.69; 95% CI, 0.53-0.84). Features from the 0- to 3-mm peritumoral region were significantly associated with the density of tumor-infiltrating lymphocytes (R2 = 0.57; 95% CI, 0.39-0.75; P = .002). CONCLUSIONS AND RELEVANCE A combination of peritumoral and intratumoral characteristics appears to identify intrinsic molecular subtypes of HER2+ breast cancers from imaging, offering insights into immune response within the peritumoral environment and suggesting potential benefit for treatment guidance.
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Affiliation(s)
- Nathaniel Braman
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jon Whitney
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Salendra Singh
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Maryam Etesami
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, Connecticut
| | - David D. B. Bates
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Katherine Gallagher
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - B. Nicolas Bloch
- Department of Radiology, Boston Medical Center, Boston, Massachusetts
- Department of Radiology, Boston University School of Medicine, Boston, Massachusetts
| | - Manasa Vulchi
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - Paulette Turk
- Department of Diagnostic Radiology, The Cleveland Clinic, Cleveland, Ohio
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
| | - Jame Abraham
- Department of Hematology and Medical Oncology, The Cleveland Clinic, Cleveland, Ohio
| | - William M. Sikov
- Program in Women’s Oncology, Women and Infants Hospital, Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - George Somlo
- Department of Medical Oncology and Therapeutics Research, City of Hope National Medical Center, Duarte, California
- Department of Hematology and Hematopoietic Cell Transplantation, City of Hope National Medical Center, Duarte, California
| | - Lyndsay N. Harris
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
- National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Hannah Gilmore
- Department of Pathology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Donna Plecha
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, Ohio
| | - Vinay Varadan
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio
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