1
|
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: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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).
Collapse
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.
| |
Collapse
|
2
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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).
Collapse
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
| |
Collapse
|
3
|
Xu WJ, Zheng BJ, Lu J, Liu SY, Li HL. Identification of triple-negative breast cancer and androgen receptor expression based on histogram and texture analysis of dynamic contrast-enhanced MRI. BMC Med Imaging 2023; 23:70. [PMID: 37264313 DOI: 10.1186/s12880-023-01022-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 05/23/2023] [Indexed: 06/03/2023] Open
Abstract
BACKGROUND Triple-negative breast cancer (TNBC) is highly malignant and has a poor prognosis due to the lack of effective therapeutic targets. Androgen receptor (AR) has been investigated as a possible therapeutic target. This study quantitatively assessed intratumor heterogeneity by histogram analysis of pharmacokinetic parameters and texture analysis on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to discriminate TNBC from non-triple-negative breast cancer (non-TNBC) and to identify AR expression in TNBC. METHODS This retrospective study included 99 patients with histopathologically proven breast cancer (TNBC: 36, non-TNBC: 63) who underwent breast DCE-MRI before surgery. The pharmacokinetic parameters of DCE-MRI (Ktrans, Kep and Ve) and their corresponding texture parameters were calculated. The independent t-test, or Mann-Whitney U-test was used to compare quantitative parameters between TNBC and non-TNBC groups, and AR-positive (AR+) and AR-negative (AR-) TNBC groups. The parameters with significant difference between two groups were further involved in logistic regression analysis to build a prediction model for TNBC. The ROC analysis was conducted on each independent parameter and the TNBC predicting model for evaluating the discrimination performance. The area under the ROC curve (AUC), sensitivity and specificity were derived. RESULTS The binary logistic regression analysis revealed that Kep_Range (p = 0.032) and Ve_SumVariance (p = 0.005) were significantly higher in TNBC than in non-TNBC. The AUC of the combined model for identifying TNBC was 0.735 (p < 0.001) with a cut-off value of 0.268, and its sensitivity and specificity were 88.89% and 52.38%, respectively. The value of Kep_Compactness2 (p = 0.049), Kep_SphericalDisproportion (p = 0.049), and Ve_GlcmEntropy (p = 0.008) were higher in AR + TNBC group than in AR-TNBC group. CONCLUSION Histogram and texture analysis of breast lesions on DCE-MRI showed potential to identify TNBC, and the specific features can be possible predictors of AR expression, enhancing the ability to individualize the treatment of patients with TNBC.
Collapse
Affiliation(s)
- Wen-Juan Xu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bing-Jie Zheng
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jun Lu
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Si-Yun Liu
- GE healthcare (China), Beijing, 100176, China
| | - Hai-Liang Li
- Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
| |
Collapse
|
4
|
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] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [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.
Collapse
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
| | | |
Collapse
|
5
|
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. J Ultrasound Med 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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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
| |
Collapse
|
6
|
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] [What about the content of this article? (0)] [Affiliation(s)] [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.
Collapse
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.)
| |
Collapse
|
7
|
Kong QC, Tang WJ, Chen SY, Hu WK, Hu Y, Liang YS, Zhang QQ, Cheng ZX, Huang D, Yang J, Guo Y. Nomogram for the prediction of triple-negative breast cancer histological heterogeneity based on multiparameter MRI features: A preliminary study including metaplastic carcinoma and non- metaplastic carcinoma. Front Oncol 2022; 12:916988. [PMID: 36212484 PMCID: PMC9533710 DOI: 10.3389/fonc.2022.916988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
Objectives Triple-negative breast cancer (TNBC) is a heterogeneous disease, and different histological subtypes of TNBC have different clinicopathological features and prognoses. Therefore, this study aimed to establish a nomogram model to predict the histological heterogeneity of TNBC: including Metaplastic Carcinoma (MC) and Non-Metaplastic Carcinoma (NMC). Methods We evaluated 117 patients who had pathologically confirmed TNBC between November 2016 and December 2020 and collected preoperative multiparameter MRI and clinicopathological data. The patients were randomly assigned to a training set and a validation set at a ratio of 3:1. Based on logistic regression analysis, we established a nomogram model to predict the histopathological subtype of TNBC. Nomogram performance was assessed with the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. According to the follow-up information, disease-free survival (DFS) survival curve was estimated using the Kaplan-Meier product-limit method. Results Of the 117 TNBC patients, 29 patients had TNBC-MC (age range, 29–65 years; median age, 48.0 years), and 88 had TNBC-NMC (age range, 28–88 years; median age, 44.5 years). Multivariate logistic regression analysis demonstrated that lesion type (p = 0.001) and internal enhancement pattern (p = 0.001) were significantly predictive of TNBC subtypes in the training set. The nomogram incorporating these variables showed excellent discrimination power with an AUC of 0.849 (95% CI: 0.750−0.949) in the training set and 0.819 (95% CI: 0.693−0.946) in the validation set. Up to the cutoff date for this analysis, a total of 66 patients were enrolled in the prognostic analysis. Six of 14 TNBC-MC patients experienced recurrence, while 7 of 52 TNBC-NMC patients experienced recurrence. The DFS of the two subtypes was significantly different (p=0.035). Conclusions In conclusion, we developed a nomogram consisting of lesion type and internal enhancement pattern, which showed good discrimination ability in predicting TNBC-MC and TNBC-NMC.
Collapse
Affiliation(s)
- Qing-cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Wen-jie Tang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Si-yi Chen
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Wen-ke Hu
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Yue Hu
- Breast Tumor Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yun-shi Liang
- Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Qiong-qiong Zhang
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Zi-xuan Cheng
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
| | - Di Huang
- Department of Breast Surgery, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- *Correspondence: Di Huang, ; Jing Yang, ; Yuan Guo,
| | - Jing Yang
- Department of Pathology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- *Correspondence: Di Huang, ; Jing Yang, ; Yuan Guo,
| | - Yuan Guo
- Department of Radiology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China
- *Correspondence: Di Huang, ; Jing Yang, ; Yuan Guo,
| |
Collapse
|
8
|
Fang S, Zhu J, Wang Y, Zhou J, Wang G, Xu W, Zhang W. The value of whole-lesion histogram analysis based on field‑of‑view optimized and constrained undistorted single shot (FOCUS) DWI for predicting axillary lymph node status in early-stage breast cancer. BMC Med Imaging 2022; 22:163. [PMID: 36088299 PMCID: PMC9464403 DOI: 10.1186/s12880-022-00891-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 08/31/2022] [Indexed: 12/28/2022] Open
Abstract
Abstract
Background
This study aims to estimate the amount of axillary lymph node (ALN) involvement in early-stage breast cancer utilizing a field of view (FOV) optimized and constrained undistorted single-shot (FOCUS) diffusion-weighted imaging (DWI) approach, as well as a whole-lesion histogram analysis.
Methods
This retrospective analysis involved 81 individuals with invasive breast cancer. The patients were divided into three groups: N0 (negative ALN metastasis), N1–2 (low metastatic burden with 1–2 ALNs), and N≥3 (heavy metastatic burden with ≥ 3 ALNs) based on their sentinel lymph node biopsy (SLNB) or axillary lymph node dissection (ALND). Histogram parameters of apparent diffusion coefficient (ADC) depending basically on FOCUS DWI were performed using 3D-Slicer software for whole lesions. The typical histogram characteristics for N0, N1–2, and N≥ 3 were compared to identify the significantly different parameters. To determine the diagnostic efficacy of significantly different factors, the area under their receiver operating characteristic (ROC) curves was examined.
Results
There were significant differences in the energy, maximum, 90 percentile, range, and lesion size among N0, N1–2, and N≥ 3 groups (P < 0.05). The energy differed significantly between N0 and N1–2 groups (P < 0.05), and some certain ADC histogram parameters and lesion sizes differed significantly between N0 and N≥3, or N1–2 and N≥3 groups. For ROC analysis, the energy yielded the best diagnostic performance in distinguishing N0 and N1–2 groups from N≥3 group with an AUC value of0.853. All parameters revealed excellent inter-observer agreement with inter-reader consistencies data ranging from0.919 to 0.982.
Conclusion
By employing FOCUS DWI method, the analysis of whole-lesion ADC histogram quantitatively provides a non-invasive way to evaluate the degree of ALN metastatic spread in early-stage breast cancer.
Collapse
|
9
|
Jiménez de los Santos ME, Reyes-Pérez JA, Domínguez Osorio V, Villaseñor-Navarro Y, Moreno-Astudillo L, Vela-Sarmiento I, Sollozo-Dupont I. Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer. World J Gastroenterol 2022; 28:2609-2624. [PMID: 35949349 PMCID: PMC9254137 DOI: 10.3748/wjg.v28.i23.2609] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 11/25/2021] [Accepted: 04/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC).
AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC.
METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC).
RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice.
CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.
Collapse
Affiliation(s)
| | | | | | | | | | - Itzel Vela-Sarmiento
- Department of Gastrointestinal Surgery, National Cancer Institute, Mexico 14080, Mexico
| | | |
Collapse
|
10
|
Han X, Cao W, Wu L, Liang C. Radiomics Assessment of the Tumor Immune Microenvironment to Predict Outcomes in Breast Cancer. Front Immunol 2022; 12:773581. [PMID: 35046937 PMCID: PMC8761791 DOI: 10.3389/fimmu.2021.773581] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/25/2021] [Indexed: 12/14/2022] Open
Abstract
Background The immune microenvironment of tumors provides information on prognosis and prediction. A prior validation of the immunoscore for breast cancer (ISBC) was made on the basis of a systematic assessment of immune landscapes extrapolated from a large number of neoplastic transcripts. Our goal was to develop a non-invasive radiomics-based ISBC predictive factor. Methods Immunocell fractions of 22 different categories were evaluated using CIBERSORT on the basis of a large, open breast cancer cohort derived from comprehensive information on gene expression. The ISBC was constructed using the LASSO Cox regression model derived from the Immunocell type scores, with 479 quantified features in the intratumoral and peritumoral regions as observed from DCE-MRI. A radiomics signature [radiomics ImmunoScore (RIS)] was developed for the prediction of ISBC using a random forest machine-learning algorithm, and we further evaluated its relationship with prognosis. Results An ISBC consisting of seven different immune cells was established through the use of a LASSO model. Multivariate analyses showed that the ISBC was an independent risk factor in prognosis (HR=2.42, with a 95% CI of 1.49–3.93; P<0.01). A radiomic signature of 21 features of the ISBC was then exploited and validated (the areas under the curve [AUC] were 0.899 and 0.815). We uncovered statistical associations between the RIS signature with recurrence-free and overall survival rates (both P<0.05). Conclusions The RIS is a valuable instrument with which to assess the immunoscore, and offers important implications for the prognosis of breast cancer.
Collapse
Affiliation(s)
- Xiaorui Han
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Wuteng Cao
- Department of Radiology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China
| | - Lei Wu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Changhong Liang
- School of Medicine, South China University of Technology, Guangzhou, China.,Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.,Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| |
Collapse
|
11
|
Miyazaki K, Morine Y, Yamada S, Saito Y, Tokuda K, Okikawa S, Yamashita S, Oya T, Ikemoto T, Imura S, Hu H, Morioka H, Tsuneyama K, Shimada M. Stromal tumor-infiltrating lymphocytes level as a prognostic factor for resected intrahepatic cholangiocarcinoma and its prediction by apparent diffusion coefficient. Int J Clin Oncol 2021; 26:2265-2274. [PMID: 34596803 DOI: 10.1007/s10147-021-02026-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 08/09/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Tumor-infiltrating lymphocytes (TILs) are a prognostic factor or an indicator of chemotherapy response for various malignancies. The aim of this study was to investigate the prognostic impact of TILs in resected intrahepatic cholangiocarcinoma (IHCC). We also investigated the usefulness of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) to predict TILs. METHODS We enrolled 23 patients with IHCC who underwent initial hepatic resection in Tokushima University Hospital from 2006 to 2017. We evaluated stromal TILs in the tumor marginal area and central area in surgical specimens. Patients were divided into low vs high stromal TILs groups. We analyzed the patients' clinicopathological factors, including prognosis, according to the degree of stromal TILs. We also analyzed the correlation between stromal TILs and the minimum ADC value. RESULTS Stromal TILs in the marginal area reflected overall survival more accurately than that in the central area. Additionally, marginal low TILs was significantly associated with lymph node metastasis and portal vein invasion. Both overall- and disease-free survival rates in the marginal low TILs group were significantly worse than those in the marginal high TILs group (P < 0.05). In the multivariate analysis, marginal low TILs were an independent prognostic factor for both overall- and disease-free survival (P < 0.05), and marginal low TILs were significantly associated with lower minimum ADC values (P < 0.02). CONCLUSIONS Stromal TILs, especially in the marginal area, might demonstrate prognostic impact in patients with IHCC. Moreover, the ADC values from MRI may predict TILs in IHCC tumor tissue.
Collapse
Affiliation(s)
- Katsuki Miyazaki
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yuji Morine
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Shinichiro Yamada
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Yu Saito
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Kazunori Tokuda
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Shohei Okikawa
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Shoko Yamashita
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.,Department of Pathology and Laboratory Medicine, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Takeshi Oya
- Department of Molecular Pathology, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Tetsuya Ikemoto
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Satoru Imura
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Haun Hu
- Department of Public Health, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Hisayoshi Morioka
- Department of Public Health, Tokushima University Graduate School of Biomedical Sciences, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Koichi Tsuneyama
- Department of Pathology and Laboratory Medicine, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan
| | - Mitsuo Shimada
- Department of Surgery, Tokushima University, 3-18-15 Kuramoto-cho, Tokushima, 770-8503, Japan.
| |
Collapse
|
12
|
Lee HJ, Lee JE, Jeong WG, Ki SY, Park MH, Lee JS, Nah YK, Lim HS. 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. [PMID: 34431365 DOI: 10.2214/AJR.21.26400] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [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.
Collapse
|
13
|
Tang WJ, Kong QC, Cheng ZX, Liang YS, Jin Z, Chen LX, Hu WK, Liang YY, Wei XH, Guo Y, Jiang XQ. Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase. Eur Radiol 2021; 32:864-875. [PMID: 34430998 DOI: 10.1007/s00330-021-08173-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/20/2021] [Accepted: 06/25/2021] [Indexed: 01/26/2023]
Abstract
OBJECTIVE To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer. MATERIALS AND METHODS This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model. RESULTS Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant. CONCLUSION The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer. KEY POINTS • Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. • We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.
Collapse
Affiliation(s)
- Wen-Jie Tang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Qing-Cong Kong
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, Guangdong, China
| | - Zi-Xuan Cheng
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Yun-Shi Liang
- Department of Pathology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Zhe Jin
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Lei-Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Wen-Ke Hu
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Ying-Ying Liang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Xin-Hua Wei
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China
| | - Yuan Guo
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
| | - Xin-Qing Jiang
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, 510180, Guangdong, China.
| |
Collapse
|
14
|
Guo Y, Kong QC, Li LQ, Tang WJ, Zhang WL, Ning GY, Xue J, Zhou QW, Liang YY, Wu M, Jiang XQ. Whole Volume Apparent Diffusion Coefficient (ADC) Histogram as a Quantitative Imaging Biomarker to Differentiate Breast Lesions: Correlation with the Ki-67 Proliferation Index. Biomed Res Int 2021; 2021:4970265. [PMID: 34258262 DOI: 10.1155/2021/4970265] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 06/09/2021] [Indexed: 11/18/2022]
Abstract
Objectives To evaluate the value of the whole volume apparent diffusion coefficient (ADC) histogram in distinguishing between benign and malignant breast lesions and differentiating different molecular subtypes of breast cancers and to assess the correlation between ADC histogram parameters and Ki-67 expression in breast cancers. Methods The institutional review board approved this retrospective study. Between September 2016 and February 2019, 189 patients with 84 benign lesions and 105 breast cancers underwent magnetic resonance imaging (MRI). Volumetric ADC histograms were created by placing regions of interest (ROIs) on the whole lesion. The relationships between the ADC parameters and Ki-67 were analysed using Spearman's correlation analysis. Results Of the 189 breast lesions included, there were significant differences in patient age (P < 0.001) and lesion size (P = 0.006) between the benign and malignant lesions. The results also demonstrated significant differences in all ADC histogram parameters between benign and malignant lesions (all P < 0.001). The median and mean ADC histogram parameters performed better than the other ADC histogram parameters (AUCs were 0.943 and 0.930, respectively). The receiver operating characteristic (ROC) analysis revealed that the 10th percentile ADC value and entropy could determine the human epidermal growth factor receptor 2 (HER-2) status (both P = 0.001) and estrogen receptor (ER)/progesterone receptor (PR) status (P = 0.020 and P = 0.041, respectively). Among all breast cancer lesions, 35 tumours in the low-proliferation group (Ki − 67 < 14%) and 70 tumours in the high-proliferation group (Ki − 67 ≥ 14) were analysed with ROC curves and correlation analyses. The ROC analysis revealed that entropy and skewness could determine the Ki-67 status (P = 0.007 and P < 0.001, respectively), and there were weak correlations between ADC entropy (r = 0.383) and skewness (r = 0.209) and the Ki-67 index. Conclusion The volumetric ADC histogram could serve as an imaging marker to determine breast lesion characteristics and may be a supplemental method in predicting tumour proliferation in breast cancer.
Collapse
|
15
|
Usuda K, Iwai S, Yamagata A, Iijima Y, Motono N, Matoba M, Doai M, Hirata K, Uramoto H. Whole-Lesion Apparent Diffusion Coefficient Histogram Analysis: Significance for Discriminating Lung Cancer from Pulmonary Abscess and Mycobacterial Infection. Cancers (Basel) 2021; 13:2720. [PMID: 34072867 DOI: 10.3390/cancers13112720] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/01/2021] [Accepted: 05/28/2021] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules and masses. However, it is difficult to differentiate pulmonary abscesses and mycobacterium infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The purpose of this study was to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers and 19 PAMIs. Parameters more than 60% of AUC were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer. Abstract Diffusion-weighted magnetic resonance imaging (DWI) can differentiate malignant from benign pulmonary nodules. However, it is difficult to differentiate pulmonary abscesses and mycobacterial infections (PAMIs) from lung cancers because PAMIs show restricted diffusion in DWI. The study purpose is to establish the role of ADC histogram for differentiating lung cancer from PAMI. There were 41 lung cancers (25 adenocarcinomas, 16 squamous cell carcinomas), and 19 PAMIs (9 pulmonary abscesses, 10 mycobacterial infections). Parameters more than 60% of the area under the ROC curve (AUC) were ADC, maximal ADC, mean ADC, median ADC, most frequency ADC, kurtosis of ADC, and volume of lesion. There were significant differences between lung cancer and PAMI in ADC, mean ADC, median ADC, and most frequency ADC. The ADC (1.19 ± 0.29 × 10−3 mm2/s) of lung cancer obtained from a single slice was significantly lower than that (1.44 ± 0.54) of PAMI (p = 0.0262). In contrast, mean, median, or most frequency ADC of lung cancer which was obtained in the ADC histogram was significantly higher than the value of each parameter of PAMI. ADC histogram could discriminate PAMIs from lung cancers by showing that AUCs of several parameters were more than 60%, and that several parameters of ADC of PAMI were significantly lower than those of lung cancer. ADC histogram has the potential to be a valuable tool to differentiate PAMI from lung cancer.
Collapse
|