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Wang S, Lan Z, Wan X, Liu J, Wen W, Peng Y. Correlation between Baseline Conventional Ultrasounds, Shear-Wave Elastography Indicators, and Neoadjuvant Therapy Efficacy in Triple-Negative Breast Cancer. Diagnostics (Basel) 2023; 13:3178. [PMID: 37891999 PMCID: PMC10605864 DOI: 10.3390/diagnostics13203178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/29/2023] [Accepted: 10/06/2023] [Indexed: 10/29/2023] Open
Abstract
In patients with triple-negative breast cancer (TNBC)-the subtype with the poorest prognosis among breast cancers-it is crucial to assess the response to the currently widely employed neoadjuvant treatment (NAT) approaches. This study investigates the correlation between baseline conventional ultrasound (US) and shear-wave elastography (SWE) indicators and the pathological response of TNBC following NAT, with a specific focus on assessing predictive capability in the baseline state. This retrospective analysis was conducted by extracting baseline US features and SWE parameters, categorizing patients based on postoperative pathological grading. A univariate analysis was employed to determine the relationship between ultrasound indicators and pathological reactions. Additionally, we employed a receiver operating characteristic (ROC) curve analysis and multivariate logistic regression methods to evaluate the predictive potential of the baseline US indicators. This study comprised 106 TNBC patients, with 30 (28.30%) in a nonmajor histological response (NMHR) group and 76 (71.70%) in a major histological response (MHR) group. Following the univariate analysis, we found that T staging, dmax values, volumes, margin changes, skin alterations (i.e., thickening and invasion), retromammary space invasions, and supraclavicular lymph node abnormalities were significantly associated with pathological efficacy (p < 0.05). Combining clinical information with either US or SWE independently yielded baseline predictive abilities, with AUCs of 0.816 and 0.734, respectively. Notably, the combined model demonstrated an improved AUC of 0.827, with an accuracy of 76.41%, a sensitivity of 90.47%, a specificity of 55.81%, and statistical significance (p < 0.01). The baseline US and SWE indicators for TNBC exhibited a strong relationship with NAT response, offering predictive insights before treatment initiation, to a considerable extent.
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Affiliation(s)
| | | | | | | | | | - Yulan Peng
- Department of Medical Ultrasound, West China Hospital, Sichuan University, Wai Nan Guo Xue Xiang 37, Chengdu 610041, China; (S.W.); (Z.L.); (X.W.); (J.L.); (W.W.)
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Chen Y, Qi Y, Wang K. Neoadjuvant chemotherapy for breast cancer: an evaluation of its efficacy and research progress. Front Oncol 2023; 13:1169010. [PMID: 37854685 PMCID: PMC10579937 DOI: 10.3389/fonc.2023.1169010] [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: 02/18/2023] [Accepted: 09/14/2023] [Indexed: 10/20/2023] Open
Abstract
Neoadjuvant chemotherapy (NAC) for breast cancer is widely used in the clinical setting to improve the chance of surgery, breast conservation and quality of life for patients with advanced breast cancer. A more accurate efficacy evaluation system is important for the decision of surgery timing and chemotherapy regimen implementation. However, current methods, encompassing imaging techniques such as ultrasound and MRI, along with non-imaging approaches like pathological evaluations, often fall short in accurately depicting the therapeutic effects of NAC. Imaging techniques are subjective and only reflect macroscopic morphological changes, while pathological evaluation is the gold standard for efficacy assessment but has the disadvantage of delayed results. In an effort to identify assessment methods that align more closely with real-world clinical demands, this paper provides an in-depth exploration of the principles and clinical applications of various assessment approaches in the neoadjuvant chemotherapy process.
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Affiliation(s)
- Yushi Chen
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Yu Qi
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
| | - Kuansong Wang
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, China
- Department of Pathology, Basic Medical School, Central South University, Changsha, Hunan, China
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Yao L, Liu X, Wang M, Yu K, Xu S, Qiu P, Lv Z, Zhang X, Xu Y. Predicting Pathological Complete Response in Breast Cancer After Two Cycles of Neoadjuvant Chemotherapy by Tumor Reduction Rate: A Retrospective Case-Control Study. J Breast Cancer 2023; 26:136-151. [PMID: 37051647 PMCID: PMC10139844 DOI: 10.4048/jbc.2023.26.e12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 12/16/2022] [Accepted: 02/08/2023] [Indexed: 03/30/2023] Open
Abstract
PURPOSE We aimed to identify effectiveness-associated indicators and evaluate the optimal tumor reduction rate (TRR) after two cycles of neoadjuvant chemotherapy (NAC) in patients with invasive breast cancer. METHODS This retrospective case-control study included patients who underwent at least four cycles of NAC at the Department of Breast Surgery between February 2013 and February 2020. A regression nomogram model for predicting pathological responses was constructed based on potential indicators. RESULTS A total of 784 patients were included, of whom 170 (21.68%) reported pathological complete response (pCR) after NAC and 614 (78.32%) had residual invasive tumors. The clinical T stage, clinical N stage, molecular subtype, and TRR were identified as independent predictors of pCR. Patients with a TRR > 35% were more likely to achieve pCR (odds ratio, 5.396; 95% confidence interval [CI], 3.299-8.825). The receiver operating characteristic (ROC) curve was plotted using the probability value, and the area under the ROC curve was 0.892 (95% CI, 0.863-0.922). CONCLUSION TRR > 35% is predictive of pCR after two cycles of NAC, and an early evaluation model using a nomogram based on five indicators, age, clinical T stage, clinical N stage, molecular subtype, and TRR, is applicable in patients with invasive breast cancer.
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Pavlov MV, Bavrina AP, Plekhanov VI, Golubyatnikov GY, Orlova AG, Subochev PV, Davydova DA, Turchin IV, Maslennikova AV. Changes in the tumor oxygenation but not in the tumor volume and tumor vascularization reflect early response of breast cancer to neoadjuvant chemotherapy. Breast Cancer Res 2023; 25:12. [PMID: 36717842 PMCID: PMC9887770 DOI: 10.1186/s13058-023-01607-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 01/17/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Breast cancer neoadjuvant chemotherapy (NACT) allows for assessing tumor sensitivity to systemic treatment, planning adjuvant treatment and follow-up. However, a sufficiently large number of patients fail to achieve the desired level of pathological tumor response while optimal early response assessment methods have not been established now. In our study, we simultaneously assessed the early chemotherapy-induced changes in the tumor volume by ultrasound (US), the tumor oxygenation by diffuse optical spectroscopy imaging (DOSI), and the state of the tumor vascular bed by Doppler US to elaborate the predictive criteria of breast tumor response to treatment. METHODS A total of 133 patients with a confirmed diagnosis of invasive breast cancer stage II to III admitted to NACT following definitive breast surgery were enrolled, of those 103 were included in the final analysis. Tumor oxygenation by DOSI, tumor volume by US, and tumor vascularization by Doppler US were determined before the first and second cycle of NACT. After NACT completion, patients underwent surgery followed by pathological examination and assessment of the pathological tumor response. On the basis of these, data regression predictive models were created. RESULTS We observed changes in all three parameters 3 weeks after the start of the treatment. However, a high predictive potential for early assessment of tumor sensitivity to NACT demonstrated only the level of oxygenation, ΔStO2, (ρ = 0.802, p ≤ 0.01). The regression model predicts the tumor response with a high probability of a correct conclusion (89.3%). The "Tumor volume" model and the "Vascularization index" model did not accurately predict the absence of a pathological tumor response to treatment (60.9% and 58.7%, respectively), while predicting a positive response to treatment was relatively better (78.9% and 75.4%, respectively). CONCLUSIONS Diffuse optical spectroscopy imaging appeared to be a robust tool for early predicting breast cancer response to chemotherapy. It may help identify patients who need additional molecular genetic study of the tumor in order to find the source of resistance to treatment, as well as to correct the treatment regimen.
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Affiliation(s)
- Mikhail V. Pavlov
- Nizhny Novgorod Regional Clinical Oncology Dispensary, Delovaya St., 11/1, Nizhny Novgorod, Russia 603126
| | - Anna P. Bavrina
- grid.416347.30000 0004 0386 1631Privolzhsky Research Medical University, Minina Square, 10/1, Nizhny Novgorod, Russia 603950
| | - Vladimir I. Plekhanov
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - German Yu. Golubyatnikov
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Anna G. Orlova
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Pavel V. Subochev
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Diana A. Davydova
- Nizhny Novgorod Regional Clinical Oncology Dispensary, Delovaya St., 11/1, Nizhny Novgorod, Russia 603126
| | - Ilya V. Turchin
- grid.410472.40000 0004 0638 0147Institute of Applied Physics RAS, Ul’yanov Street, 46, Nizhny Novgorod, Russia 603950
| | - Anna V. Maslennikova
- grid.416347.30000 0004 0386 1631Privolzhsky Research Medical University, Minina Square, 10/1, Nizhny Novgorod, Russia 603950 ,grid.28171.3d0000 0001 0344 908XNational Research Lobachevsky State University of Nizhny Novgorod, Gagarin Ave., 23, Nizhny Novgorod, Russia 603022
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Liu Y, Wang Y, Wang Y, Xie Y, Cui Y, Feng S, Yao M, Qiu B, Shen W, Chen D, Du G, Chen X, Liu Z, Li Z, Yang X, Liang C, Wu L. Early prediction of treatment response to neoadjuvant chemotherapy based on longitudinal ultrasound images of HER2-positive breast cancer patients by Siamese multi-task network: A multicentre, retrospective cohort study. EClinicalMedicine 2022; 52:101562. [PMID: 35928032 PMCID: PMC9343415 DOI: 10.1016/j.eclinm.2022.101562] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2022] [Revised: 06/22/2022] [Accepted: 06/27/2022] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND Early prediction of treatment response to neoadjuvant chemotherapy (NACT) in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer can facilitate timely adjustment of treatment regimens. We aimed to develop and validate a Siamese multi-task network (SMTN) for predicting pathological complete response (pCR) based on longitudinal ultrasound images at the early stage of NACT. METHODS In this multicentre, retrospective cohort study, a total of 393 patients with biopsy-proven HER2-positive breast cancer were retrospectively enrolled from three hospitals in china between December 16, 2013 and March 05, 2021, and allocated into a training cohort and two external validation cohorts. Patients receiving full cycles of NACT and with surgical pathological results available were eligible for inclusion. The key exclusion criteria were missing ultrasound images and/or clinicopathological characteristics. The proposed SMTN consists of two subnetworks that could be joined at multiple layers, which allowed for the integration of multi-scale features and extraction of dynamic information from longitudinal ultrasound images before and after the first /second cycles of NACT. We constructed the clinical model as a baseline using multivariable logistic regression analysis. Then the performance of SMTN was evaluated and compared with the clinical model. FINDINGS The training cohort, comprising 215 patients, were selected from Yunnan Cancer Hospital. The two independent external validation cohorts, comprising 95 and 83 patients, were selected from Guangdong Provincial People's Hospital, and Shanxi Cancer Hospital, respectively. The SMTN yielded an area under the receiver operating characteristic curve (AUC) values of 0.986 (95% CI: 0.977-0.995), 0.902 (95%CI: 0.856-0.948), and 0.957 (95%CI: 0.924-0.990) in the training cohort and two external validation cohorts, respectively, which were significantly higher than that those of the clinical model (AUC: 0.524-0.588, P all < 0.05). The AUCs values of the SMTN within the anti-HER2 therapy subgroups were 0.833-0.972 in the two external validation cohorts. Moreover, 272 of 279 (97.5%) non-pCR patients (159 of 160 (99.4%), 53 of 54 (98.1%), and 60 of 65 (92.3%) in the training and two external validation cohorts, respectively) were successfully identified by the SMTN, suggesting that they could benefit from regime adjustment at the early-stage of NACT. INTERPRETATION The SMTN was able to predict pCR in the early-stage of NACT for HER2-positive breast cancer patients, which could guide clinicians in adjusting treatment regimes. FUNDING Key-Area Research and Development Program of Guangdong Province (No.2021B0101420006); National Natural Science Foundation of China (No.82071892, 82171920); Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application (No.2022B1212010011); the National Science Foundation for Young Scientists of China (No.82102019, 82001986); Project Funded by China Postdoctoral Science Foundation (No.2020M682643); the Outstanding Youth Science Foundation of Yunnan Basic Research Project (202101AW070001); Scientific research fund project of Department of Education of Yunnan Province(2022J0249). Science and technology Projects in Guangzhou (202201020001;202201010513); High-level Hospital Construction Project (DFJH201805, DFJHBF202105).
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Affiliation(s)
- Yu Liu
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Ying Wang
- Department of Medical Ultrasonics, the First Affiliated Hospital of Guangzhou medical University, 151 Yanjiang West Road, 510120, China
| | - Yuxiang Wang
- Department of Ultrasound, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Yu Xie
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
| | - Yanfen Cui
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
| | - Senwen Feng
- Department of General Surgery, Shenzhen YanTian district people's hospital (group), Shenzhen, 518081, China
| | - Mengxia Yao
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
| | - Bingjiang Qiu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Wenqian Shen
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
| | - Dong Chen
- Department of Medical Ultrasound, The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming 650118, China
| | - Guoqing Du
- Department of Ultrasound, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
| | - Xin Chen
- Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, 1 Panfu Road, Guangzhou, 510180, China
| | - Zaiyi Liu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Zhenhui Li
- Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China
- Corresponding author at: Department of Radiology, the Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Yunnan Cancer Center, Kunming, 650118, China.
| | - Xiaotang Yang
- Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China
- Corresponding author at: Department of Radiology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013, China.
| | - Changhong Liang
- The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Corresponding author at: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China.
| | - Lei Wu
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Corresponding author at: Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, 106 Zhangshan Er Road, Guangzhou 510080, China.
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Xu M, Li F, Yu S, Zeng S, Weng G, Teng P, Yang H, Li X, Liu G. Value of Histogram of Gray-Scale Ultrasound Image in Differential Diagnosis of Small Triple Negative Breast Invasive Ductal Carcinoma and Fibroadenoma. Cancer Manag Res 2022; 14:1515-1524. [PMID: 35478712 PMCID: PMC9038159 DOI: 10.2147/cmar.s359986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 04/12/2022] [Indexed: 11/23/2022] Open
Abstract
Objective To investigate the value of gray-scale ultrasound (US) image histogram in the differential diagnosis between small (≤2.00 cm), oval, or round triple negative breast invasive ductal carcinoma (TN-IDC) and fibroadenoma (FA). Methods Fifty-five cases of triple negative breast invasive ductal carcinoma (TN-IDC group) and 57 cases of breast fibroadenoma (FA group) confirmed by pathology in Hubei cancer hospital from September 2017 to September 2021 were analyzed retrospectively. The gray-scale US images were analyzed by histogram analysis method, from which some parameters (including mean, variance, skewness, kurtosis and 1st, 10th, 50th, 90th and 99th percentile) can be obtained. Intraclass correlation coefficient (ICC) was used to evaluate the inter observer reliability of histogram parameters. Histogram parameters between the TN-IDC and FA groups were compared using independent Student’s t-test or Mann-Whitney U-test, respectively. In addition, the receiver operating characteristic (ROC) curve analysis was used for the significant parameters to calculate the differential diagnosis efficiency. Results All the histogram parameters showed excellent inter-reader consistency, with the ICC values ranged from 0.883 to 0.999. The mean value, 1st, 10th, 50th, 90th and 99th percentiles of TN-IDC group were significantly lower than those of FA group (P < 0.05). The area under ROC curve (AUC) values of mean and n percentiles were from 0.807 to 0.848. However, there were no significant differences in variance, skewness and kurtosis between the two groups (P > 0.05). Conclusion Histogram analysis of gray-scale US images can well distinguish small, oval, or round TN-IDC from FA.
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Affiliation(s)
- Maolin Xu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Fang Li
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Shaonan Yu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Shue Zeng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Gaolong Weng
- Department of Ultrasound, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China
| | - Peihong Teng
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Huimin Yang
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
| | - Xuefeng Li
- Department of Anesthesiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
- Correspondence: Xuefeng Li, Department of Anesthesiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, People’s Republic of China, Email
| | - Guifeng Liu
- Department of Radiology, China-Japan Union Hospital of Jilin University, Changchun, People’s Republic of China
- Guifeng Liu, Department of Radiology, China-Japan Union Hospital of Jilin University, Xiantai Street, Changchun, 130033, People’s Republic of China, Email
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Wu L, Ye W, Liu Y, Chen D, Wang Y, Cui Y, Li Z, Li P, Li Z, Liu Z, Liu M, Liang C, Yang X, Xie Y, Wang Y. An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study. Breast Cancer Res 2022; 24:81. [PMID: 36414984 PMCID: PMC9680135 DOI: 10.1186/s13058-022-01580-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 11/13/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the breast-conserving rate. This study aimed to develop and validate an autosegmentation-based serial ultrasonography assessment system (SUAS) that incorporated serial ultrasonographic features throughout the NAC of breast cancer to predict pCR. METHODS A total of 801 patients with biopsy-proven breast cancer were retrospectively enrolled from three institutions and were split into a training cohort (242 patients), an internal validation cohort (197 patients), and two external test cohorts (212 and 150 patients). Three imaging signatures were constructed from the serial ultrasonographic features before (pretreatment signature), during the first-second cycle of (early-stage treatment signature), and after (posttreatment signature) NAC based on autosegmentation by U-net. The SUAS was constructed by subsequently integrating the pre, early-stage, and posttreatment signatures, and the incremental performance was analysed. RESULTS The SUAS yielded a favourable performance in predicting pCR, with areas under the receiver operating characteristic curve (AUCs) of 0.927 [95% confidence interval (CI) 0.891-0.963] and 0.914 (95% CI 0.853-0.976), compared with those of the clinicopathological prediction model [0.734 (95% CI 0.665-0.804) and 0.610 (95% CI 0.504-0.716)], and radiologist interpretation [0.632 (95% CI 0.570-0.693) and 0.724 (95% CI 0.644-0.804)] in the external test cohorts. Furthermore, similar results were also observed in the early-stage treatment of NAC [AUC 0.874 (0.793-0.955)-0.897 (0.851-0.943) in the external test cohorts]. CONCLUSIONS We demonstrate that autosegmentation-based SAUS integrating serial ultrasonographic features throughout NAC can predict pCR with favourable performance, which can facilitate individualized treatment strategies.
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Affiliation(s)
- Lei Wu
- grid.410643.4Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080 China ,grid.410643.4Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 China ,grid.413352.20000 0004 1760 3705Guangdong Cardiovascular Institute, 106 Zhongshan 2nd Road, Guangzhou, 510080 China
| | - Weitao Ye
- grid.410643.4Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080 China ,grid.410643.4Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 China
| | - Yu Liu
- grid.410643.4Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 China ,grid.410643.4Department of Ultrasound, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080 China
| | - Dong Chen
- grid.452826.fDepartment of Medical Ultrasound, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118 China
| | - Yuxiang Wang
- grid.263452.40000 0004 1798 4018Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013 China
| | - Yanfen Cui
- grid.263452.40000 0004 1798 4018Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013 China
| | - Zhenhui Li
- grid.452826.fDepartment of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118 China
| | - Pinxiong Li
- grid.410643.4Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080 China ,grid.410643.4Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 China
| | - Zhen Li
- grid.452826.fDepartment of 3rd Breast Surgery, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118 China
| | - Zaiyi Liu
- grid.410643.4Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080 China ,grid.410643.4Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 China
| | - Min Liu
- grid.488530.20000 0004 1803 6191Department of Ultrasound, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, 510060 China
| | - Changhong Liang
- grid.410643.4Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Road, Guangzhou, 510080 China ,grid.410643.4Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080 China
| | - Xiaotang Yang
- grid.263452.40000 0004 1798 4018Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, 030013 China
| | - Yu Xie
- grid.452826.fDepartment of Radiology, Yunnan Cancer Hospital, Yunnan Cancer Center, The Third Affiliated Hospital of Kunming Medical University, Kunming, 650118 China
| | - Ying Wang
- grid.470124.4Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang West Road, Guangzhou, 510120 China
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Li P, Niu Y, Li S, Zu X, Xiao M, Yin L, Feng J, He J, Shen Y. Identification of an AXL kinase inhibitor in triple-negative breast cancer by structure-based virtual screening and bioactivity test. Chem Biol Drug Des 2021; 99:222-232. [PMID: 34679238 DOI: 10.1111/cbdd.13977] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 09/27/2021] [Accepted: 10/16/2021] [Indexed: 01/04/2023]
Abstract
Breast cancer is a malignant tumor that occurs in the glandular epithelium of the breast, and more than 15% of the patients are triple-negative breast cancer (TNBC). Therefore, finding new targets and targeted therapeutic drugs for TNBC is urgent. Overexpression of the AXL is associated with motility and invasiveness of the TNBC cells, which is a potential target for breast cancer therapy. A compound Y041-5921 (IC50 = 6.069 μm for AXL kinase and IC50 = 4.1 μm for MDA-MB-231 cell line) was identified through structure-based virtual screening and bioassay test for the first time. The compound Y041-5921 could significantly inhibit the proliferation and invasion of the TNBC cells and the toxicity of Y041-5921 to normal immortalized breast epithelial cells was far lower than that of commonly used clinical chemotherapy drugs. Besides, it also had well inhibitory effect on the proliferation of many other malignant tumor cell lines (the IC50 value are 10.0 m, 7.1 m, 10.3 m, 11.4 m and 5.8 m for U251 cell, COLO cell, PC-9 cell, CAKI-1 cell and MG63 cell, respectively). The interaction mechanism between Y041-5921 and AXL was studied by molecular dynamics (MD) simulations and binding free energy calculation, and the key residues whose energy contribution mainly comes from non-polar solvation interaction (such as Ala565, Lys567, Met598, Leu620, Pro621, Met623, Lys624, Arg676, Asn677 and Met679) were identified. The small molecule inhibitors Y041-5921 targeting AXL reported in this work will lay a foundation and provide a theoretical basis for the development of the TNBC.
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Affiliation(s)
- Pei Li
- The First Affiliated Hospital, Department of Oncology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,Key Laboratory of Oncology and Molecular Pathology of Hunan Province, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
| | - Yuzhen Niu
- School of Life Sciences, Shandong University of Technology, Zibo, Shandong, China
| | - Shuyan Li
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, Gansu, China
| | - Xuyu Zu
- The First Affiliated Hospital, Department of Oncology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,Key Laboratory of Oncology and Molecular Pathology of Hunan Province, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
| | - Maoyu Xiao
- The First Affiliated Hospital, Department of Oncology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,Key Laboratory of Oncology and Molecular Pathology of Hunan Province, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
| | - Liyang Yin
- The First Affiliated Hospital, Department of Oncology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,Key Laboratory of Oncology and Molecular Pathology of Hunan Province, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
| | - Jianbo Feng
- The First Affiliated Hospital, Department of Oncology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,Key Laboratory of Oncology and Molecular Pathology of Hunan Province, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
| | - Jun He
- The Nanhua Affiliated Hospital, Department of Spine Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China
| | - Yingying Shen
- The First Affiliated Hospital, Department of Oncology, Hengyang Medical School, University of South China, Hengyang, Hunan, China.,Key Laboratory of Oncology and Molecular Pathology of Hunan Province, The First Affiliated Hospital of University of South China, Hengyang, Hunan, China
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