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Javadinia SA, Valizadeh N, Saeedian A. Editorial for "Prognostic Value of MRI Assessment of Residual Peritumoral Edema in Breast Cancer Treated With Neoadjuvant Chemotherapy". J Magn Reson Imaging 2025; 61:956-957. [PMID: 38923081 DOI: 10.1002/jmri.29480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Revised: 05/24/2024] [Accepted: 05/24/2024] [Indexed: 06/28/2024] Open
Affiliation(s)
- Seyed Alireza Javadinia
- Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Niloufar Valizadeh
- Department of Radiology, Birjand University of Medical Sciences, Birjand, Iran
| | - Arefeh Saeedian
- Radiation Oncology Research Center, Cancer Research Institute, Tehran University of Medical Sciences, Tehran, Iran
- Department of Radiation Oncology, Cancer Institute, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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Shigematsu H, Fujimoto M, Kobayashi Y, Yasui D, Komoto D, Matsuura N, Kuraoka K, Yoshiyama T. Prognostic Value of MRI Assessment of Residual Peritumoral Edema in Breast Cancer Treated With Neoadjuvant Chemotherapy. J Magn Reson Imaging 2025; 61:944-955. [PMID: 38809133 DOI: 10.1002/jmri.29456] [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: 02/07/2024] [Revised: 05/08/2024] [Accepted: 05/09/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND Peritumoral edema (PE) identified on T2-weighted breast MRI is a factor for poor prognosis in breast cancer. PURPOSE To assess the prognostic value of residual PE (rPE) in patients with PE positive breast cancer prior to neoadjuvant chemotherapy (NACT) who subsequently underwent curative surgery. STUDY TYPE Retrospective. POPULATION In total, 128 patients with nonmetastatic invasive breast cancer who underwent breast MRI before and after NACT. FIELD STRENGTH/SEQUENCE Axial precontrast 2D fast spin echo T2W fat-suppressed sequence. Axial dynamic 3D gradient echo T1W fat-suppressed sequence. ASSESSMENT PE was diagnosed when a signal intensity as high as water was detected surrounding the tumor on a T2-weighted breast MRI. PE was qualitatively evaluated by three readers with more than 20 years of experience in interpreting breast field imaging findings. Residual cancer burden (RCB) were assessed post-NACT. Recurrence-free survival (RFS) and overall survival (OS) were evaluated as the endpoints of this study. STATISTICAL TESTS Chi-square test; Kaplan-Meier method, log-rank test, and Cox proportional hazard model. A P-value <0.05 was considered statistically significant. RESULTS Pre-PE was observed in 64 out of 128 patients. Of these, rPE was observed in 21. In the log-rank test, breast cancer with rPE had significantly worse RFS and OS than that without rPE. Cox proportional hazard analysis identified rPE as a significant prognostic factor for recurrence (hazard ratio, 11.6; 95% confidence interval [CI], 3.05-43.8) and death (hazard ratio, 17.8; 95% CI, 3.30-96.3). Breast cancer with rPE had significant worse RFS and OS than that without rPE in RCB class II, and significant worse OS in pathological complete response, class I and class II in the log-rank test. DATA CONCLUSION rPE on a T2-weighted breast MRI was a significant factor for breast cancer recurrence and death in patients with pre-PE-positive breast cancer treated with NACT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY Stage 2.
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Affiliation(s)
- Hideo Shigematsu
- Department of Breast Surgery, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Mutsumi Fujimoto
- Department of Breast Surgery, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Yoshie Kobayashi
- Department of Breast Surgery, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Daisuke Yasui
- Department of Breast Surgery, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Daisuke Komoto
- Department of Radiology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Noriaki Matsuura
- Department of Radiology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Kazuya Kuraoka
- Department of Pathology, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
| | - Tomoyuki Yoshiyama
- Department of Breast Surgery, National Hospital Organization Kure Medical Center and Chugoku Cancer Center, Hiroshima, Japan
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Tani NH, Koreeda Y, Nawata A, Fujisaki A, Hayashida Y, Shimajiri S, Nakayama T, Hisaoka M, Inoue Y, Hirata K, Tashima Y, Tanaka F, Aoki T. Peritumoral Fat Content Identified Using Iterative Decomposition of Water and Fat with Echo Asymmetry and Least-squares Estimation (IDEAL) Correlates with Breast Cancer Prognosis. Magn Reson Med Sci 2025; 24:112-121. [PMID: 38325834 PMCID: PMC11733510 DOI: 10.2463/mrms.mp.2023-0127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/01/2023] [Indexed: 02/09/2024] Open
Abstract
PURPOSE Adipocytes around aggressive breast cancer (BC) are less lipid different from naive adipocytes (cancer-associated adipocytes, CAAs), and peritumoral edema caused by the release of cytokines from CAAs can conduce to decrease the peritumoral fat proportion. The purpose of this study was to correlate peritumoral fat content identified by using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) with lymph node metastasis (LNM) and recurrence-free survival (RFS) in BC patients and to compare with T2-weighted (T2WI) and diffusion-weighted images (DWI) analyses. METHODS This retrospective study consisted of 85 patients who were diagnosed with invasive carcinoma of breast and underwent breast MRI, including IDEAL before surgery. The scan time of fat fraction (FF) map imaging using IDEAL was 33s. Four regions of interest (ROIs), which are 5 mm from the tumor edge, and one ROI in the mammary fat of the healthy side were set on the FF map. Then average peritumoral FF values (TFF), average FF values on the healthy side (HFF), and peritumoral fat ratio (PTFR, which is defined as TFF/HFF) were calculated. Tumor apparent diffusion coefficient (ADC) values were measured on ADC map obtained by DWI. Peritumoral edema was classified into three grades based on the degree of signal intensity around the tumor on T2WI (T2 edema). RESULTS The results of stepwise logistic regression analysis for four variables (TFF, PTFR, T2 edema, and ADC value) indicated that TFF and T2 edema were significant factors of LNM (P < 0.01). RFS was significantly associated with TFF (P = 0.016), and 47 of 49 (95.9%) patients with TFF more than 85.5% were alive without recurrence. CONCLUSION Peritumoral fat content identified by using IDEAL is associated with LNM and RFS and may therefore be a useful prognostic biomarker for BC.
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Affiliation(s)
- Natsumi Hirano Tani
- Department of Radiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yuki Koreeda
- Department of Surgery 1, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Aya Nawata
- Department of Pathology and Oncology, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Akitaka Fujisaki
- Department of Radiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yoshiko Hayashida
- Department of Radiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Shohei Shimajiri
- Department of Pathology and Cell Biology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Toshiyuki Nakayama
- Department of Pathology and Cell Biology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Masanori Hisaoka
- Department of Pathology and Oncology, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yuzuru Inoue
- Department of Surgery 1, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Keiji Hirata
- Department of Surgery 1, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Yuko Tashima
- Department of Surgery 2, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Fumihiro Tanaka
- Department of Surgery 2, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
| | - Takatoshi Aoki
- Department of Radiology, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
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Yamaguchi K, Nakazono T, Egashira R, Fukui S, Imaizumi T, Maruyama K, Nickel D, Hamamoto T, Yamaguchi R, Irie H. Relationship between kinetic parameters of ultrafast dynamic contrast-enhanced (DCE) MRI and tumor-infiltrating lymphocytes (TILs) in breast cancer. Jpn J Radiol 2025; 43:43-50. [PMID: 39186213 PMCID: PMC11717854 DOI: 10.1007/s11604-024-01645-w] [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: 04/09/2024] [Accepted: 08/15/2024] [Indexed: 08/27/2024]
Abstract
PURPOSE To evaluate the relationship between kinetic parameters of ultrafast dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and tumor-infiltrating lymphocytes (TILs) in breast cancer. PATIENTS AND METHODS This retrospective study was approved by an institutional review board and included 76 women (median age: 60) with 76 surgically proven breast cancers who underwent DCE MRI including ultrafast sequence. Based on the TILs level, we classified the patients into the low-TILs (< 10%) group and the high-TILs (≥ 10%) group. Maximum slope (MS) and time to enhancement (TTE) derived from ultrafast DCE sequence were correlated in each TILs group. The percentages of six kinetic patterns (fast, medium, and slow from the early phase, washout, plateau, and persistent from the delayed phase) derived from the conventional DCE sequence were also correlated in each TILs group. RESULTS Of the 76 breast cancers, 57 were in the low-TILs group and 19 comprised the high-TILs group. The median MS in the high-TILs group (32.4%/sec) was significantly higher than that in the low-TILs group (23.68%/s) (p = 0.037). In a receiver-operating characteristic (ROC) analysis, the area under the curve (AUC) for differentiating between the high- and low-TILs group was 0.661. The TTE in the high-TILs group was significantly shorter than that in the low-TILs group (p = 0.012). In the ROC analysis, the AUC was 0.685. There were no significant differences between the percentages of the six kinetic patterns from the conventional DCE sequence and the TILs level (p = 0.075-0.876). CONCLUSION Compared to the low-TILs group, the high-TILs group had higher MS and shorter TTE.
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Affiliation(s)
- Ken Yamaguchi
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, 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
| | - Shuichi Fukui
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Tsutomu Imaizumi
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
| | - Katsuya Maruyama
- MR Research & Collaboration Department, Siemens Healthcare K.K., Gate City Osaki West Tower, 1-11-1 Osaki, Shinagawa-Ku, Tokyo, 141-8644, Japan
| | - Dominik Nickel
- MR Application Development, Siemens Healthcare GmbH, Allee Am Roethelheimpark 2, 91052, Erlangen, Germany
| | | | - Rin Yamaguchi
- Department of Pathology and Laboratory Medicine, Kurume University Medical Center, 155-1 Kokubu, Kurume, 859-0863, Japan
| | - Hiroyuki Irie
- Department of Radiology, Faculty of Medicine, Saga University, 5-1-1 Nabeshima, Saga, 849-8501, Japan
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van der Voort A, van der Hoogt KJJ, Wessels R, Schipper RJ, Wesseling J, Sonke GS, Mann RM. Diffusion-weighted imaging in addition to contrast-enhanced MRI in identifying complete response in HER2-positive breast cancer. Eur Radiol 2024; 34:7994-8004. [PMID: 38967659 PMCID: PMC11557627 DOI: 10.1007/s00330-024-10857-7] [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: 10/15/2023] [Revised: 04/15/2024] [Accepted: 04/26/2024] [Indexed: 07/06/2024]
Abstract
OBJECTIVES The aim of this study is to investigate the added value of diffusion-weighted imaging (DWI) to dynamic-contrast enhanced (DCE)-MRI to identify a pathological complete response (pCR) in patients with HER2-positive breast cancer and radiological complete response (rCR). MATERIALS AND METHODS This is a single-center observational study of 102 patients with stage I-III HER2-positive breast cancer and real-world documented rCR on DCE-MRI. Patients were treated between 2015 and 2019. Both 1.5 T/3.0 T single-shot diffusion-weighted echo-planar sequence were used. Post neoadjuvant systemic treatment (NST) diffusion-weighted images were reviewed by two readers for visual evaluation and ADCmean. Discordant cases were resolved in a consensus meeting. pCR of the breast (ypT0/is) was used to calculate the negative predictive value (NPV). Breast pCR-percentages were tested with Fisher's exact test. ADCmean and ∆ADCmean(%) for patients with and without pCR were compared using a Mann-Whitney U-test. RESULTS The NPV for DWI added to DCE is 86% compared to 87% for DCE alone in hormone receptor (HR)-/HER2-positive and 67% compared to 64% in HR-positive/HER2-positive breast cancer. Twenty-seven of 39 non-rCR DWI cases were false positives. In HR-positive/HER2-positive breast cancer the NPV for DCE MRI differs between MRI field strength (1.5 T: 50% vs. 3 T: 81% [p = 0.02]). ADCmean at baseline, post-NST, and ∆ADCmean were similar between patients with and without pCR. CONCLUSION DWI has no clinically relevant effect on the NPV of DCE alone to identify a pCR in early HER2-positive breast cancer. The added value of DWI in HR-positive/HER2-positive breast cancer should be further investigated taken MRI field strength into account. CLINICAL RELEVANCE STATEMENT The residual signal on DWI after neoadjuvant systemic therapy in cases with early HER2-positive breast cancer and no residual pathologic enhancement on DCE-MRI breast should not (yet) be considered in assessing a complete radiologic response. KEY POINTS Radiologic complete response is associated with a pathologic complete response (pCR) in HER2+ breast cancer but further improvement is warranted. No relevant increase in negative predictive value was observed when DWI was added to DCE. Residual signal on DW-images without pathologic enhancement on DCE-MRI, does not indicate a lower chance of pCR.
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Affiliation(s)
- Anna van der Voort
- Department of Medical Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands.
| | - Kay J J van der Hoogt
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Ronni Wessels
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Robert-Jan Schipper
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Surgery, Catharina Hospital, Eindhoven, The Netherlands
| | - Jelle Wesseling
- Department of Pathology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Gabe S Sonke
- Department of Medical Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
- University of Amsterdam, Amsterdam, The Netherlands
| | - Ritse M Mann
- Department of Radiology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
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Ge W, Fan X, Zeng Y, Yang X, Zhou L, Zuo Z. Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer. Acad Radiol 2024; 31:4317-4328. [PMID: 38876841 DOI: 10.1016/j.acra.2024.05.043] [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: 04/19/2024] [Revised: 05/12/2024] [Accepted: 05/24/2024] [Indexed: 06/16/2024]
Abstract
RATIONALE AND OBJECTIVES Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC. MATERIALS AND METHODS In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05. RESULTS GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set. CONCLUSION The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.
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Affiliation(s)
- Wu Ge
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Xiaohong Fan
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Lu Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan province 411000, PR China (W.G., Y.Z., X.Y., L.Z.).
| | - Zhichao Zuo
- School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, Hunan province, PR China (X.F., Z.Z.).
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Yan L, Chen Y, He J. Leveraging MRI radiomics signature for predicting the diagnosis of CXCL9 in breast cancer. Heliyon 2024; 10:e38640. [PMID: 39430466 PMCID: PMC11490775 DOI: 10.1016/j.heliyon.2024.e38640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 09/26/2024] [Accepted: 09/26/2024] [Indexed: 10/22/2024] Open
Abstract
Objective A non-invasive predictive model was developed using radiomic features to forecast CXCL9 expression level in breast cancer patients. Methods CXCL9 expression data and MRI images of breast cancer patients were obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) databases, respectively. Local tissue samples from 20 breast cancer patients were collected to measure CXCL9 expression levels. Radiomic features were extracted from MRI images using 3DSlicer, and the minimum Redundancy Maximum Relevance and Recursive Feature Elimination (mRMR_RFE) method was employed to select the most pertinent radiomic features associated with CXCL9 expression levels. Support vector machine (SVM) and Logistic Regression (LR) models were utilized to construct the predictive model, and the area under the receiver operating characteristic curve (AUC) was calculated for performance evaluation. Results CXCL9 was found to be upregulated in breast cancer patients and linked to breast cancer prognosis. Nine radiomic features were ultimately selected using the mRMR_RFE method, and SVM and LR models were trained and validated. The SVM model achieved AUC values of 0.748 and 0.711 in the training and validation sets, respectively. The LR model obtained AUC values of 0.771 and 0.724 in the training and validation sets, respectively. Conclusion The utilization of MRI radiomic features for predicting CXCL9 expression level provides a novel non-invasive approach for breast cancer Prognostic research.
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Affiliation(s)
- Liping Yan
- Department of Breast Surgery, Maternal and Child Health Hospital of Jiangxi Province, Nanchang, China
- Department of Surgery, the First Affiliated Hospital of Guangxi Medical University, China
| | - Yuexia Chen
- Department of Pathology, The Third Hospital of Nanchang, Nanchang, China
| | - Jianxin He
- Department of Ultrasound Medicine, The First Affiliated Hospital of Nanchang University, China
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Hu L, Gu Y, Xu W, Wang C. Association of clinicopathologic and sonographic features with stromal tumor-infiltrating lymphocytes in triple-negative breast cancer. BMC Cancer 2024; 24:997. [PMID: 39135184 PMCID: PMC11320771 DOI: 10.1186/s12885-024-12778-6] [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: 07/07/2024] [Accepted: 08/07/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Increased level of stromal tumor-infiltrating lymphocytes (sTILs) are associated with therapeutic outcomes and prognosis in triple-negative breast cancer (TNBC). This study aimed to investigate the associations of clinicopathologic and sonographic features with sTILs level in TNBC. METHODS This study included invasive TNBC patients with postoperative evaluation of sTILs after surgical resection. Tumor shape, margin, orientation, echo pattern, posterior features, calcification, and vascularity were retrospectively evaluated. The patients were categorized into high-sTILs (≥ 20%) and low-sTILs (< 20%) level groups. Chi-square or Fisher's exact tests were used to assess the association of clinicopathologic and sonographic features with sTILs level. RESULTS The 171 patients (mean ± SD age, 54.7 ± 10.3 years [range, 22‒87 years]) included 58.5% (100/171) with low-sTILs level and 41.5% (71/171) with high-sTILs level. The TNBC tumors with high-sTILs level were more likely to be no special type invasive carcinoma (p = 0.008), higher histologic grade (p = 0.029), higher Ki-67 proliferation rate (all p < 0.05), and lower frequency of associated DCIS component (p = 0.026). In addition, the TNBC tumors with high-sTILs level were more likely to be an oval or round shape (p = 0.001), parallel orientation (p = 0.011), circumscribed or micro-lobulated margins (p < 0.001), complex cystic and solid echo patterns (p = 0.001), posterior enhancement (p = 0.002), and less likely to have a heterogeneous pattern (p = 0.001) and no posterior features (p = 0.002). CONCLUSIONS This preliminary study showed that preoperative sonographic characteristics could be helpful in distinguishing high-sTILs from low-sTILs in TNBC patients.
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Affiliation(s)
- Ling Hu
- Department of Ultrasound in Medicine, Hangzhou Women's Hospital, Hangzhou, Zhejiang, China
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yunxia Gu
- Department of Ultrasound in Medicine, Hangzhou Women's Hospital, Hangzhou, Zhejiang, China
| | - Wen Xu
- Department of Ultrasound in Medicine, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Chao Wang
- Department of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
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Rizzo V, Cicciarelli F, Galati F, Moffa G, Maroncelli R, Pasculli M, Pediconi F. Could breast multiparametric MRI discriminate between pure ductal carcinoma in situ and microinvasive carcinoma? Acta Radiol 2024; 65:565-574. [PMID: 38196268 DOI: 10.1177/02841851231225807] [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] [Indexed: 01/11/2024]
Abstract
BACKGROUND Ductal carcinoma in situ (DCIS) is often reclassified as invasive cancer in the final pathology report of the surgical specimen. It is of significant clinical relevance to acknowledge the possibility of underestimating invasive disease when utilizing preoperative biopsies for a DCIS diagnosis. In cases where such histologic upgrades occur, it is imperative to consider them in the preoperative planning process, including the potential inclusion of sentinel lymph node biopsy due to the risk of axillary lymph node metastasis. PURPOSE To assess the capability of breast multiparametric magnetic resonance imaging (MP-MRI) in differentiating between pure DCIS and microinvasive carcinoma (MIC). MATERIAL AND METHODS Between January 2018 and November 2022, this retrospective study enrolled patients with biopsy-proven DCIS who had undergone preoperative breast MP-MRI. We assessed various MP-MRI features, including size, morphology, margins, internal enhancement pattern, extent of disease, presence of peritumoral edema, time-intensity curve value, diffusion restriction, and ADC value. Subsequently, a logistic regression analysis was conducted to explore the association of these features with the pathological outcome. RESULTS Of 129 patients with biopsy-proven DCIS, 36 had foci of micro-infiltration on surgical specimens and eight were diagnosed with invasive ductal carcinoma (IDC). The presence of micro-infiltration foci was significantly associated with several MP-MRI features, including tumor size (P <0.001), clustered ring enhancement (P <0.001), segmental distribution (P <0.001), diffusion restriction (P = 0.005), and ADC values <1.3 × 10-3 mm2/s (P = 0.004). CONCLUSION Breast MP-MRI has the potential to predict the presence of micro-infiltration foci in biopsy-proven DCIS and may serve as a valuable tool for guiding therapeutic planning.
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MESH Headings
- Humans
- Female
- Breast Neoplasms/diagnostic imaging
- Breast Neoplasms/pathology
- Middle Aged
- Retrospective Studies
- Carcinoma, Intraductal, Noninfiltrating/diagnostic imaging
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Aged
- Adult
- Diagnosis, Differential
- Multiparametric Magnetic Resonance Imaging/methods
- Neoplasm Invasiveness
- Breast/diagnostic imaging
- Breast/pathology
- Carcinoma, Ductal, Breast/diagnostic imaging
- Carcinoma, Ductal, Breast/pathology
- Aged, 80 and over
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Affiliation(s)
- Veronica Rizzo
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
| | - Federica Cicciarelli
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
| | - Francesca Galati
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
| | - Giuliana Moffa
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
| | - Roberto Maroncelli
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
| | - Marcella Pasculli
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
| | - Federica Pediconi
- Department of Radiological, Oncological and Pathological Sciences; Sapienza, University of Rome, Rome, Italy
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Zheng H, Jian L, Li L, Liu W, Chen W. Delta-Radiomics Based on Dynamic Contrast-Enhanced MRI for Predicting Lymphovascular Invasion in Invasive Breast Cancer. Acad Radiol 2024; 31:1762-1772. [PMID: 38092588 DOI: 10.1016/j.acra.2023.11.017] [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: 09/28/2023] [Revised: 11/07/2023] [Accepted: 11/08/2023] [Indexed: 05/12/2024]
Abstract
RATIONALE AND OBJECTIVES Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions. MATERIALS AND METHODS Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI. Radiomic features were extracted from pre-contrast (A0), first post-contrast (A1), and subtracted images of A0 and A1. Three radiomics models were developed using several data analyses; logistic analyses were performed to identify radiological features to predict the LVI status. A hybrid model integrating both radiological features and optimal radiomics was developed. Receiver operating characteristic analysis was employed to evaluate model performance, using the area under the curve (AUC) as a quantitative metric for discriminative ability. RESULTS In the test set, the Radiomics-Delta model, with 17 radiomic features, had an AUC of 0.781 and accuracy of 0.705. Radiomics-A0, with 10 features, had an AUC of 0.619 and accuracy of 0.523, while Radiomics-A1, with 8 features, had an AUC of 0.715 and accuracy of 0.591. The hybrid model exhibited better performance, with an AUC of 0.868 and accuracy of 0.875, than the radiological and Radiomics-Delta models, with an AUC of 0.759 and 0.781, respectively, and accuracy of 0.773 and 0.705, respectively. CONCLUSION Compared to Radiomics-A0 and Radiomics-A1, Radiomics-Delta demonstrated superior performance. Moreover, the hybrid model incorporating Radiomics-Delta and radiological features exhibited excellent performance in determining the LVI status in cases of invasive breast cancer.
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Affiliation(s)
- Hong Zheng
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, China (H.Z., L.J.)
| | - Lian Jian
- Department of Radiology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan, China (H.Z., L.J.)
| | - Li Li
- Department of Radiology, Hunan Children's Hospital, Changsha, 410007, Hunan, China (L.L.)
| | - Wen Liu
- Department of Radiology, The Third Xiang Ya Hospital, Central South University, Changsha, 410013, Hunan, China (W.L.)
| | - Wei Chen
- Department of Radiology, The second People's Hospital of Hunan Province, Brain Hospital of Hunan Province, Changsha, 410007, Hunan, China (W.C.).
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11
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Wang Y, Fan J, Liu Y, Du J, Liang B, Wang H, Song Z. Identification and validation of DHCR7 as a diagnostic biomarker involved in the proliferation and mitochondrial function of breast cancer. Aging (Albany NY) 2024; 16:5967-5986. [PMID: 38526324 PMCID: PMC11042931 DOI: 10.18632/aging.205683] [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: 09/18/2023] [Accepted: 02/20/2024] [Indexed: 03/26/2024]
Abstract
BACKGROUND Energy metabolism has a complex intersection with pathogenesis and development of breast cancer (BC). This allows for the possibility of identifying energy-metabolism-related genes (EMRGs) as novel prognostic biomarkers for BC. 7-dehydrocholesterol reductase (DHCR7) is a key enzyme of cholesterol biosynthesis involved in many cancers, and in this paper, we investigate the effects of DHCR7 on the proliferation and mitochondrial function of BC. METHODS EMRGs were identified from the Gene Expression Omnibus (GEO) and MSigDB databases using bioinformatics methods. Key EMRGs of BC were then identified and validated by functional enrichment analysis, interaction analysis, weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, Cox analysis, and immune infiltration. Western blot, qRT-PCR, immunohistochemistry (IHC), MTT assay, colony formation assay and flow cytometry assay were then used to analyze DHCR7 expression and its biological effects on BC cells. RESULTS We identified 31 EMRGs in BC. These 31 EMRGs and related transcription factors (TFs), miRNAs, and drugs were enriched in glycerophospholipid metabolism, glycoprotein metabolic process, breast cancer, and cell cycle. Crucially, DHCR7 was a key EMRG in BC identified and validated by WGCNA, LASSO regression and receiver operating characteristic (ROC) curve analysis. High DHCR7 expression was significantly associated with tumor immune infiltration level, pathological M, and poor prognosis in BC. In addition, DHCR7 knockdown inhibited cell proliferation, induced apoptosis and affected mitochondrial function in BC cells. CONCLUSIONS DHCR7 was found to be a key EMRG up-regulated in BC cells. This study is the first to our knowledge to report that DHCR7 acts as an oncogene in BC, which might become a novel therapeutic target for BC patients.
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Affiliation(s)
- Yanfeng Wang
- Department of Surgical Oncology, Shaanxi Provincial People’s Hospital, Shaanxi, China
- Department of Clinical Laboratory, Affiliated Hospital of Yan’an University, Shaanxi, China
| | - Jiaxin Fan
- Department of Geriatric Neurology, Shaanxi Provincial People’s Hospital, Shaanxi, China
| | - Yongcheng Liu
- Department of Pathology, Affiliated Hospital of Yan’an University, Shaanxi, China
| | - Jie Du
- Department of Health Examination Center, Shaanxi Provincial People’s Hospital, Shaanxi, China
| | - Boyu Liang
- Department of Surgical Oncology, Shaanxi Provincial People’s Hospital, Shaanxi, China
| | - Huxia Wang
- Department of Breast Disease Center, Shaanxi Provincial Tumor Hospital, Shaanxi, China
| | - Zhangjun Song
- Department of Surgical Oncology, Shaanxi Provincial People’s Hospital, Shaanxi, China
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12
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Jirarayapong J, Portnow LH, Chikarmane SA, Lan Z, Gombos EC. High Peritumoral and Intratumoral T2 Signal Intensity in HER2-Positive Breast Cancers on Preneoadjuvant Breast MRI: Assessment of Associations With Histopathologic Characteristics. AJR Am J Roentgenol 2024; 222:e2330280. [PMID: 38117101 DOI: 10.2214/ajr.23.30280] [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] [Indexed: 12/21/2023]
Abstract
BACKGROUND. Intratumoral necrosis and peritumoral edema are features of aggressive breast cancer that may present as high T2 signal intensity (T2 SI). Implications of high T2 SI in HER2-positive cancers are unclear. OBJECTIVE. The purpose of this study was to assess associations with histopathologic characteristics of high peritumoral T2 SI and intratumoral T2 SI of HER2-positive breast cancer on MRI performed before initiation of neoadjuvant therapy. METHODS. This retrospective study included 210 patients (age, 24-82 years) with 211 HER2 breast cancers who, from January 1, 2015, to July 30, 2022, underwent breast MRI before receiving neoadjuvant therapy. Two radiologists independently assessed cancers for high peritumoral T2 SI and high intratumoral T2 SI on fat-suppressed T2-weighted imaging and classified patterns of high peritumoral T2 SI (adjacent to tumor vs prepectoral extension). A third radiologist resolved discrepancies. Multivariable logistic regression analyses were performed to identify associations of high peritumoral and intratumoral T2 SI with histopathologic characteristics (associated ductal carcinoma in situ, hormone receptor status, histologic grade, lymphovascular invasion, and axillary lymph node metastasis). RESULTS. Of 211 HER2-positive cancers, 81 (38.4%) had high peritumoral T2 SI, and 95 (45.0%) had high intratumoral T2 SI. A histologic grade of 3 was independently associated with high peritumoral T2 SI (OR = 1.90; p = .04). Otherwise, none of the five assessed histopathologic characteristics were independently associated with high intratumoral T2 SI or high peritumoral T2 SI (p > .05). Cancers with high T2 SI adjacent to the tumor (n = 29) and cancers with high T2 SI with prepectoral extension (n = 52) showed no significant difference in frequency for any of the histopathologic characteristics (p > .05). Sensitivities and specificities for predicting the histopathologic characteristics ranged from 35.6% to 43.7% and from 59.7% to 70.7%, respectively, for high peritumoral T2 SI, and from 37.3% to 49.6% and from 49.3% to 62.7%, respectively, for high intratumoral T2 SI. Interreader agreement was almost perfect for high peritumoral T2 SI (Gwet agreement coefficient [AC] = 0.93), high intratumoral T2 SI (Gwet AC = 0.89), and a pattern of high peritumoral T2 SI (Gwet AC = 0.95). CONCLUSION. The only independent association between histopathologic characteristics and high T2 SI of HER2-positive breast cancer was observed between a histologic grade of 3 and high peritumoral T2 SI. CLINICAL IMPACT. In contrast with previously reported findings in broader breast cancer subtypes, peritumoral and intratumoral T2 SI had overall limited utility as prognostic markers of HER2-positive breast cancer.
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Affiliation(s)
- Jirarat Jirarayapong
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- Department of Radiology, King Chulalongkorn Memorial Hospital, The Thai Red Cross Society, Chulalongkorn University, 1873 Rama 4 Rd, Pathumwan, Bangkok 10330, Thailand
| | - Leah H Portnow
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- Department of Radiology, Dana-Farber Cancer Institute, Boston, MA
| | - Sona A Chikarmane
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- Department of Radiology, Dana-Farber Cancer Institute, Boston, MA
| | - Zhou Lan
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| | - Eva C Gombos
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
- Department of Radiology, Dana-Farber Cancer Institute, Boston, MA
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Luo J, Shi X, Liu Y, Wang J, Wang H, Yang X, Sun Q, Hui Z, Wei F, Ren X, Zhao H. Immune checkpoint ligands expressed on mature high endothelial venules predict poor prognosis of NSCLC: have a relationship with CD8 + T lymphocytes infiltration. Front Immunol 2024; 15:1302761. [PMID: 38390332 PMCID: PMC10882939 DOI: 10.3389/fimmu.2024.1302761] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/22/2024] [Indexed: 02/24/2024] Open
Abstract
Background An insufficient number of intratumoral CD8+ T lymphocytes is a major barrier to antitumor immunity and immunotherapy. High endothelial venules (HEVs) are the major sites through which lymphocytes enter tumors; however, the molecular mechanism through which HEVs mediate CD8+ T lymphocyte infiltration remains poorly understood. Methods Forty-two patients with stage IIIA lung adenocarcinoma, who underwent surgery, were recruited. Multiplex immunohistochemical staining was conducted on tumor tissues to detect the immune checkpoint ligands (ICLs) expressed in the HEVs, blood vessels, and lymphatics. A new ICL score model was constructed to evaluate ligand expression. The relationship between ICL score, tumor-infiltrating CD8+ T cell frequency, and survival of patients was investigated. Results Mature HEVs, but not blood vessels or lymphatics, mediated CD8+ T cell infiltration. However, the ICLs expressed on mature HEVs could negatively regulate CD8+ T cell entry into tertiary lymphoid structures (TLSs). In addition, according to the results obtained using our ICLtotal score model, the expression of ICLs on HEVs was observed to be a predictor of both CD8+ T cell infiltration and survival, in which a high ICLtotal score > 1 represent a weak CD8+ T cell infiltration and a high ICLtotal score > 2 predicts poor survival. Conclusion Using the ICL score model, we discovered that ICLs expressed on HEVs are indicative of CD8+ T cell subset infiltration in TLSs, as well as of patient survival with lung cancer.
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Affiliation(s)
- Jing Luo
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Xiuhuan Shi
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Medical Oncology, Affiliated Hospital of Inner Mongolia Medical University, Huhhot, China
| | - Yumeng Liu
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Jian Wang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Hao Wang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, China
| | - Xuena Yang
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Qian Sun
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Zhenzhen Hui
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Feng Wei
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Haihe Laboratory of Cell Ecosystem, Tianjin, China
| | - Xiubao Ren
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Haihe Laboratory of Cell Ecosystem, Tianjin, China
- Department of Biotherapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Hua Zhao
- Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China
- Tianjin’s Clinical Research Center for Cancer, Tianjin, China
- Key Laboratory of Cancer Immunology and Biotherapy, Tianjin, China
- Department of Immunology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Haihe Laboratory of Cell Ecosystem, Tianjin, China
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14
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Jiang Y, Zeng Y, Zuo Z, Yang X, Liu H, Zhou Y, Fan X. Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer. Heliyon 2024; 10:e23916. [PMID: 38192872 PMCID: PMC10772250 DOI: 10.1016/j.heliyon.2023.e23916] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/12/2023] [Accepted: 12/15/2023] [Indexed: 01/10/2024] Open
Abstract
Objective This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC). Methods This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created. Results A total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555-0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy. Conclusion The combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.
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Affiliation(s)
- Yihong Jiang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Ying Zeng
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Zhichao Zuo
- The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, 411105, China
| | - Xiuqi Yang
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Haibo Liu
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Yingjun Zhou
- Department of Radiology, Xiangtan Central Hospital, Xiangtan, Hunan, 411100, China
| | - Xiaohong Fan
- The School of Mathematics and Computational Science, Xiangtan University, Xiangtan, Hunan, 411105, China
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15
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Chen S, Sui Y, Ding S, Chen C, Liu C, Zhong Z, Liang Y, Kong Q, Tang W, Guo Y. A simple and convenient model combining multiparametric MRI and clinical features to predict tumour-infiltrating lymphocytes in breast cancer. Clin Radiol 2023; 78:e1065-e1074. [PMID: 37813758 DOI: 10.1016/j.crad.2023.08.029] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/30/2023] [Accepted: 08/31/2023] [Indexed: 10/11/2023]
Abstract
AIM To develop a simple and convenient method based on multiparametric magnetic resonance imaging (MRI) and clinical features to non-invasively predict tumour-infiltrating lymphocytes (TILs) in breast cancer (BC) and to explore the relationship between TIL levels and disease-free survival (DFS). MATERIALS AND METHODS A total of 172 BC patients were enrolled between November 2017 and June 2021 in this retrospective study. The patients were divided into high (≥10%) and low (<10%) TIL groups. Clinicopathological data were collected. MRI features were reviewed by two radiologists. Predictors associated with TILs were determined by using multivariable logistic regression analyses. Kaplan-Meier survival curves based on TIL levels were used to estimate DFS. RESULTS A total of 102 patients with low TILs and 70 patients with high TILs were included in the study. Tumour size (odds ratio [OR], 1.040; 95% confidence interval [CI]: 1.006, 1.075; p=0.020), apparent diffusion coefficient (ADC; OR, 1.003; 95% CI: 1.001, 1.005; p=0.015), clinical axillary lymph node status (CALNS; OR, 3.222; 95% CI: 1.372,7.568; p=0.007), and enhancement pattern (OR, 0.284; 95% CI: 0.143, 0.563; p<0.001) were independently associated with TIL levels. These features were used in the ALSE model (where A is ADC, L is CALNS, S is size, and E is enhancement pattern). High TILs were associated with better DFS (p=0.016). CONCLUSION The ALSE model derived from multiparametric MRI and clinical features could non-invasively predict TIL levels in BC, and high TILs were associated with longer DFS, especially in human epidermal growth factor receptor 2 (HER2)-positive BC and triple-negative BC (TNBC).
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Affiliation(s)
- S Chen
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Y Sui
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China; Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou, 510005, China
| | - S Ding
- Department of Radiology, Liuzhou People's Hospital, Guangxi Medical University, Liuzhou, 545006, China
| | - C Chen
- Department of Pathology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - C Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Z Zhong
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Y Liang
- Department of Pathology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China
| | - Q Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China.
| | - W Tang
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China.
| | - Y Guo
- Department of Radiology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, 510180, China.
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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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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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18
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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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19
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Frankowska K, Zarobkiewicz M, Dąbrowska I, Bojarska-Junak A. Tumor infiltrating lymphocytes and radiological picture of the tumor. Med Oncol 2023; 40:176. [PMID: 37178270 PMCID: PMC10182948 DOI: 10.1007/s12032-023-02036-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 04/24/2023] [Indexed: 05/15/2023]
Abstract
Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.
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Affiliation(s)
- Karolina Frankowska
- Department of Clinical Immunology, Medical University of Lublin, Lublin, Poland
| | - Michał Zarobkiewicz
- Department of Clinical Immunology, Medical University of Lublin, Lublin, Poland.
| | - Izabela Dąbrowska
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, Lublin, Poland
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20
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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: 0.5] [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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21
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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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22
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Dai X, Shen Y, Gao Y, Huang G, Lin B, Liu Y. Correlation study between apparent diffusion coefficients and the prognostic factors in breast cancer. Clin Radiol 2023; 78:347-355. [PMID: 36746720 DOI: 10.1016/j.crad.2022.11.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 11/10/2022] [Accepted: 11/17/2022] [Indexed: 01/05/2023]
Abstract
AIM To analyse the correlation between apparent diffusion coefficients (ADC) derived from intratumoural and peritumoural regions with prognostic factors and immune-inflammatory markers in breast cancer (BC). MATERIALS AND METHODS In this retrospective study, 89 patients (age range, 28-66 years; median, 45 years) with a diagnosis of invasive BC who underwent routine blood tests and multiparametric magnetic resonance imaging (MRI) were enrolled. The study cohort was stratified according to tumour maximum cross-section ≥20 mm, lymph node metastasis (LNM), time-signal intensity curve (TIC) type, and receptor status. Minimum, maximum, mean, and heterogeneity values of tumour ADC (ADCtmin, ADCtmax, ADCtmean, and ADCheter), maximum values of peritumoural ADC (ADCpmax), and the ratio of peritumoural-tumour ADC (ADCratio) were obtained on the ADC maps. Linear regression analyses were performed to investigate the correlation between immune-inflammatory markers, prognostic factors and ADC values. RESULTS HER-2 was positively associated with ADCtmax, ADCtmean, and ADCpmax values (β = 0.306, p=0.004; β = 0.283, p=0.007; β = 0.262, p=0.007, respectively), while platelet-to-lymphocyte ratio (PLR) was positively associated with ADCpmax and ADCratio values (β = 0.227, p=0.020; β = 0.231, p=0.020, respectively). Among ADC parameters, ADCpmax showed the highest predictive values for evaluating the presence of LNM (AUC, 0.751; sensitivity, 70.4%; specificity, 77.1%). CONCLUSION The ADCpmax value could provide additional assistance in predicting prognostic factors of BC.
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Affiliation(s)
- X Dai
- Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China; Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Y Shen
- Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China; Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China.
| | - Y Gao
- The First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, China
| | - G Huang
- Department of Pathology, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - B Lin
- Department of Breast Surgery, Longgang Central Hospital of Shenzhen, Shenzhen, China
| | - Y Liu
- Shenzhen Clinical Medical College, Guangzhou University of Chinese Medicine, Shenzhen, China; Department of Radiology, Longgang Central Hospital of Shenzhen, Shenzhen, China
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