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Yang W, Yang Y, Zhang C, Yin Q, Zhang N. A clinicopathological-imaging nomogram for the prediction of pathological complete response in breast cancer cases administered neoadjuvant therapy. Magn Reson Imaging 2024; 111:120-130. [PMID: 38703971 DOI: 10.1016/j.mri.2024.05.002] [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: 03/04/2023] [Revised: 04/30/2024] [Accepted: 05/01/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE To construct a user-friendly nomogram with MRI and clinicopathological parameters for the prediction of pathological complete response (pCR) after neoadjuvant therapy (NAT) in patients with breast cancer (BC). METHODS We retrospectively enrolled consecutive female patients pathologically confirmed with breast cancer who received NAT followed by surgery between January 2018 and December 2022 as the development cohort. Additionally, we prospectively collected eligible candidates between January 2023 and December 2023 as an external validation group at our institution. Pretreatment MRI features and clinicopathological variables were collected, and the pre- and post-treatment background parenchymal enhancement (BPE) and the changes in BPE on two MRIs were compared between patients who achieved pCR and those who did not. Multivariable logistic regression analysis was used to identify independent variables associated with pCR in the development cohort. These independent variables were combined into a predictive nomogram for which performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, decision curve analysis, and external validation. RESULTS In the development cohort, there were a total of 276 female patients with a mean age of 48.3 ± 8.7 years, while in the validation cohort, there were 87 female patients with a mean age of 49.0 ± 9.5 years. Independent prognostic factors of pCR included small tumor size, HER2(+), high Ki-67 index,high signal enhancement ratio (SER), low minimum value of apparent diffusion coefficient (ADCmin), and significantly decreased BPE after NAT(change of BPE). The nomogram, which incorporates the above parameters, demonstrated excellent predictive performance in both the development and external validation cohorts, with AUC values of 0.900 and 0.850, respectively. Additionally, the nomogram showed excellent calibration capacities, as indicated by Hosmer-Lemeshow test p values of 0.508 and 0.423 in the two cohorts. Furthermore, the nomogram provided greater net benefits compared to the default simple schemes in both cohorts. CONCLUSION A nomogram constructed using tumor size, HER2 status, Ki-67 index, SER, ADCmin, and changes in pre- and post-NAT BPE demonstrated strong predictive performance, calibration ability, and greater net benefits for predicting pCR in patients with BC after NAT. This suggests that the user-friendly nomogram could be a valuable imaging biomarker for identifying suitable candidates for NAT.
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Affiliation(s)
- Wei Yang
- Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan 750004, China.
| | - Yan Yang
- Information Technology Center, 32752 Troop, Xiangyang 441000, China
| | - Chaolin Zhang
- Department of Surgical Oncology, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan 750004, China
| | - Qingyun Yin
- Department of medical Oncology, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan 750004, China
| | - Ningmei Zhang
- Department of Pathology, General Hospital of Ningxia Medical University, 804 Shengli Road, Yinchuan 750004, China
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Choi Y, Kim SY, Cho N, Moon WK. Mammographic density changes after neoadjuvant chemotherapy in triple-negative breast cancer: Association with treatment and survival outcome. Clin Imaging 2024; 109:110136. [PMID: 38552382 DOI: 10.1016/j.clinimag.2024.110136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 03/04/2024] [Accepted: 03/19/2024] [Indexed: 04/17/2024]
Abstract
PURPOSE To investigate the association of mammographic breast density with treatment and survival outcomes in patients with triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy (NAC). METHODS This retrospective study evaluated 306 women with TNBC who underwent NAC followed by surgery between 2010 and 2019. The baseline density and the density changes after NAC were evaluated. Qualitative breast density (a-d) was evaluated using the Breast Imaging Reporting and Data System. Quantitative breast density (%) was evaluated using fully automated software (the Laboratory for Individualized Breast Radiodensity Assessment) in the contralateral breast. Multivariable logistic regression analysis was used to evaluate the association between breast density and pathologic complete response (pCR), stratified by menopausal status. Cox proportional hazard regression analysis was used to evaluate the association among breast density, the development of contralateral breast cancer, and the development of locoregional recurrence and/or distant metastasis. RESULTS Contralateral density reduction ≥10 % was independently associated with pCR in premenopausal women (odds ratio [OR], 2.5; p = 0.022) but not in postmenopausal women (OR, 0.9; p = 0.823). During a mean follow-up of 65 months, 10 (3 %) women developed contralateral breast cancer, and 68 (22 %) women developed locoregional recurrences and/or distant metastases. Contralateral density reduction ≥10 % showed no association with the occurrence of contralateral breast cancer (hazard ratio [HR], 3.1; p = 0.308) or with locoregional recurrence and/or distant metastasis (HR, 1.1; p = 0.794). CONCLUSION In premenopausal women, a contralateral breast density reduction of ≥10 % after NAC was independently associated with pCR, although it did not translate into improved outcomes.
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Affiliation(s)
- Yelim Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Soo-Yeon Kim
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea.
| | - Nariya Cho
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea; Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea
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3
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Chan AM, Aguirre B, Liu L, Mah V, Balko JM, Tsui J, Wadehra NP, Moatamed NA, Khoshchehreh M, Dillard CM, Kiyohara M, Elshimali Y, Chang HR, Marquez-Garban D, Hamilton N, Pietras RJ, Gordon LK, Wadehra M. EMP2 Serves as a Functional Biomarker for Chemotherapy-Resistant Triple-Negative Breast Cancer. Cancers (Basel) 2024; 16:1481. [PMID: 38672563 PMCID: PMC11048488 DOI: 10.3390/cancers16081481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/06/2024] [Accepted: 04/09/2024] [Indexed: 04/28/2024] Open
Abstract
Breast cancer (BC) remains among the most commonly diagnosed cancers in women worldwide. Triple-negative BC (TNBC) is a subset of BC characterized by aggressive behavior, a high risk of distant recurrence, and poor overall survival rates. Chemotherapy is the backbone for treatment in patients with TNBC, but outcomes remain poor compared to other BC subtypes, in part due to the lack of recognized functional targets. In this study, the expression of the tetraspan protein epithelial membrane protein 2 (EMP2) was explored as a predictor of TNBC response to standard chemotherapy. We demonstrate that EMP2 functions as a prognostic biomarker for patients treated with taxane-based chemotherapy, with high expression at both transcriptomic and protein levels following treatment correlating with poor overall survival. Moreover, we show that targeting EMP2 in combination with docetaxel reduces tumor load in syngeneic and xenograft models of TNBC. These results provide support for the prognostic and therapeutic potential of this tetraspan protein, suggesting that anti-EMP2 therapy may be beneficial for the treatment of select chemotherapy-resistant TNBC tumors.
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Affiliation(s)
- Ann M. Chan
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
- UCLA Stein Eye Institute and the Department of Ophthalmology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Brian Aguirre
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Lucia Liu
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Vei Mah
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Justin M. Balko
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Jessica Tsui
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Navin P. Wadehra
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Neda A. Moatamed
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Mahdi Khoshchehreh
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Christen M. Dillard
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Meagan Kiyohara
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
| | - Yahya Elshimali
- Division of Cancer Research and Training, Department of Internal Medicine, Charles Drew University of Medicine and Science, 1720 East 120th Street, Los Angeles, CA 90059, USA
| | - Helena R. Chang
- Division of Surgical Oncology, Department of Surgery, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Diana Marquez-Garban
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Nalo Hamilton
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
- School of Nursing, UCLA, Los Angeles, CA 90095, USA
| | - Richard J. Pietras
- Division of Cancer Research and Training, Department of Internal Medicine, Charles Drew University of Medicine and Science, 1720 East 120th Street, Los Angeles, CA 90059, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
- Division of Hematology and Oncology, Department of Medicine, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Lynn K. Gordon
- UCLA Stein Eye Institute and the Department of Ophthalmology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
| | - Madhuri Wadehra
- Department of Pathology Lab Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA (V.M.)
- Division of Cancer Research and Training, Department of Internal Medicine, Charles Drew University of Medicine and Science, 1720 East 120th Street, Los Angeles, CA 90059, USA
- Jonsson Comprehensive Cancer Center, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA
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Seller A, Tegeler CM, Mauermann J, Schreiber T, Hagelstein I, Liebel K, Koch A, Heitmann JS, Greiner SM, Hayn C, Dannehl D, Engler T, Hartkopf AD, Hahn M, Brucker SY, Salih HR, Märklin M. Soluble NKG2DLs Are Elevated in Breast Cancer Patients and Associate with Disease Outcome. Int J Mol Sci 2024; 25:4126. [PMID: 38612935 PMCID: PMC11012452 DOI: 10.3390/ijms25074126] [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: 03/08/2024] [Revised: 04/02/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024] Open
Abstract
Ligands of the natural killer group 2D (NKG2DL) family are expressed on malignant cells and are usually absent from healthy tissues. Recognition of NKG2DLs such as MICA/B and ULBP1-3 by the activating immunoreceptor NKG2D, expressed by NK and cytotoxic T cells, stimulates anti-tumor immunity in breast cancer. Upregulation of membrane-bound NKG2DLs in breast cancer has been demonstrated by immunohistochemistry. Tumor cells release NKG2DLs via proteolytic cleavage as soluble (s)NKG2DLs, which allows for effective immune escape and is associated with poor prognosis. In this study, we collected serum from 140 breast cancer (BC) and 20 ductal carcinoma in situ (DCIS) patients at the time of initial diagnosis and 20 healthy volunteers (HVs). Serum levels of sNKG2DLs were quantified through the use of ELISA and correlated with clinical data. The analyzed sNKG2DLs were low to absent in HVs and significantly higher in BC patients. For some of the ligands analyzed, higher sNKG2DLs serum levels were associated with the classification of malignant tumor (TNM) stage and grading. Low sMICA serum levels were associated with significantly longer progression-free (PFS) and overall survival (OS). In conclusion, we provide the first insights into sNKG2DLs in BC patients and suggest their potential role in tumor immune escape in breast cancer. Furthermore, our observations suggest that serum sMICA levels may serve as a prognostic parameter in the patients analyzed in this study.
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Affiliation(s)
- Anna Seller
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - Christian M. Tegeler
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany
| | - Jonas Mauermann
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - Tatjana Schreiber
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - Ilona Hagelstein
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - Kai Liebel
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - André Koch
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Jonas S. Heitmann
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
- Department of Peptide-Based Immunotherapy, Institute of Immunology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany
| | - Sarah M. Greiner
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Clara Hayn
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - Dominik Dannehl
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Tobias Engler
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Andreas D. Hartkopf
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Markus Hahn
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Sara Y. Brucker
- Department of Women’s Health, University Hospital Tübingen, Calwerstraße 7, 72076 Tübingen, Germany
| | - Helmut R. Salih
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
| | - Melanie Märklin
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), Department of Internal Medicine, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076 Tübingen, Germany; (A.S.)
- Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies”, University of Tübingen, Röntgenweg 11, 72076 Tübingen, Germany
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Shen M, Cao S, Long X, Xiao L, Yang L, Zhang P, Li L, Chen F, Lei T, Gao H, Ye F, Bu H. DNAJC12 causes breast cancer chemotherapy resistance by repressing doxorubicin-induced ferroptosis and apoptosis via activation of AKT. Redox Biol 2024; 70:103035. [PMID: 38306757 PMCID: PMC10847378 DOI: 10.1016/j.redox.2024.103035] [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: 12/10/2023] [Revised: 01/03/2024] [Accepted: 01/07/2024] [Indexed: 02/04/2024] Open
Abstract
BACKGROUND Chemotherapy is a primary treatment for breast cancer (BC), yet many patients develop resistance over time. This study aims to identify critical factors contributing to chemoresistance and their underlying molecular mechanisms, with a focus on reversing this resistance. METHODS We utilized samples from the Gene Expression Omnibus (GEO) and West China Hospital to identify and validate genes associated with chemoresistance. Functional studies were conducted using MDA-MB-231 and MCF-7 cell lines, involving gain-of-function and loss-of-function approaches. RNA sequencing (RNA-seq) identified potential mechanisms. We examined interactions between DNAJC12, HSP70, and AKT using co-immunoprecipitation (Co-IP) assays and established cell line-derived xenograft (CDX) models for in vivo validations. RESULTS Boruta analysis of four GEO datasets identified DNAJC12 as highly significant. Patients with high DNAJC12 expression showed an 8 % pathological complete response (pCR) rate, compared to 38 % in the low expression group. DNAJC12 inhibited doxorubicin (DOX)-induced cell death through both ferroptosis and apoptosis. Combining apoptosis and ferroptosis inhibitors completely reversed DOX resistance caused by DNAJC12 overexpression. RNA-seq suggested that DNAJC12 overexpression activated the PI3K-AKT pathway. Inhibition of AKT reversed the DOX resistance induced by DNAJC12, including reduced apoptosis and ferroptosis, restoration of cleaved caspase 3, and decreased GPX4 and SLC7A11 levels. Additionally, DNAJC12 was found to increase AKT phosphorylation in an HSP70-dependent manner, and inhibiting HSP70 also reversed the DOX resistance. In vivo studies confirmed that AKT inhibition reversed DNAJC12-induced DOX resistance in the CDX model. CONCLUSION DNAJC12 expression is closely linked to chemoresistance in BC. The DNAJC12-HSP70-AKT signaling axis is crucial in mediating resistance to chemotherapy by suppressing DOX-induced ferroptosis and apoptosis. Our findings suggest that targeting AKT and HSP70 activities may offer new therapeutic strategies to overcome chemoresistance in BC.
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Affiliation(s)
- Mengjia Shen
- Department of Pathology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Chengdu, 610041, Sichuan, China; Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Shiyu Cao
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Xinyi Long
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Lin Xiao
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Libo Yang
- Department of Pathology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Chengdu, 610041, Sichuan, China; Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Peichuan Zhang
- Institute of Thoracic Oncology and Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, China
| | - Li Li
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Fei Chen
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China
| | - Ting Lei
- Department of Pathology, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China
| | - Hongwei Gao
- Laboratory Medicine Center, Lanzhou University Second Hospital, The Second Clinical Medical College of Lanzhou University, Lanzhou 730000, China
| | - Feng Ye
- Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Key Lab of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
| | - Hong Bu
- Department of Pathology, West China Hospital, Sichuan University, No. 37, Guo Xue Xiang, Chengdu, 610041, Sichuan, China; Institute of Clinical Pathology, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China; Key Lab of Transplant Engineering and Immunology, Ministry of Health, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.
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6
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Baldassi B, Poladyan H, Shahi A, Maa-Hacquoil H, Rapley M, Komarov B, Stiles J, Freitas V, Waterston M, Aseyev O, Reznik A, Bubon O. Image quality evaluation for a clinical organ-targeted PET camera. Front Oncol 2024; 14:1268991. [PMID: 38590664 PMCID: PMC10999605 DOI: 10.3389/fonc.2024.1268991] [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: 07/28/2023] [Accepted: 03/12/2024] [Indexed: 04/10/2024] Open
Abstract
Introduction A newly developed clinical organ-targeted Positron Emission Tomography (PET) system (also known as Radialis PET) is tested with a set of standardized and custom tests previously used to evaluate the performance of Positron Emission Mammography (PEM) systems. Methods Imaging characteristics impacting standardized uptake value (SUV) and detectability of small lesions, namely spatial resolution, linearity, uniformity, and recovery coefficients, are evaluated. Results In-plane spatial resolution was measured as 2.3 mm ± 0.1 mm, spatial accuracy was 0.1 mm, and uniformity measured with flood field and NEMA NU-4 phantom was 11.7% and 8.3% respectively. Selected clinical images are provided as reference to the imaging capabilities under different clinical conditions such as reduced activity of 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG) and time-delayed acquisitions. SUV measurements were performed for selected clinical acquisitions to demonstrate a capability for quantitative image assessment of different types of cancer including for invasive lobular carcinoma with comparatively low metabolic activity. Quantitative imaging performance assessment with phantoms demonstrates improved contrast recovery and spill-over ratio for this PET technology when compared to other commercial organ-dedicated PET systems with similar spatial resolution. Recovery coefficients were measured to be 0.21 for the 1 mm hot rod and up to 0.89 for the 5 mm hot rod of NEMA NU-4 Image Quality phantom. Discussion Demonstrated ability to accurately reconstruct activity in tumors as small as 5 mm suggests that the Radialis PET technology may be well suited for emerging clinical applications such as image guided assessment of response to neoadjuvant systemic treatment (NST) in lesions smaller than 2 cm. Also, our results suggest that, while spatial resolution greatly influences the partial volume effect which degrades contrast recovery, optimized count rate performance and image reconstruction workflow may improve recovery coefficients for systems with comparable spatial resolution. We emphasize that recovery coefficient should be considered as a primary performance metric when a PET system is used for accurate lesion size or radiotracer uptake assessments.
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Affiliation(s)
- Brandon Baldassi
- Department of Physics, Lakehead University, Thunder Bay, ON, Canada
| | | | - Anirudh Shahi
- Department of Physics, Lakehead University, Thunder Bay, ON, Canada
| | | | - Madeline Rapley
- Department of Physics, Lakehead University, Thunder Bay, ON, Canada
| | | | - Justin Stiles
- Department of Physics, Lakehead University, Thunder Bay, ON, Canada
| | - Vivianne Freitas
- Department of Medical Imaging, University Health Network, Sinai Health System, Women’s College Hospital, Toronto, ON, Canada
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | | | - Olexiy Aseyev
- Department of Medical Oncology, Thunder Bay Regional Health Sciences Center, Thunder Bay, ON, Canada
| | - Alla Reznik
- Department of Physics, Lakehead University, Thunder Bay, ON, Canada
- Radialis Inc., Thunder Bay, ON, Canada
| | - Oleksandr Bubon
- Department of Physics, Lakehead University, Thunder Bay, ON, Canada
- Radialis Inc., Thunder Bay, ON, Canada
- Thunder Bay Regional Health Research Institute, Thunder Bay, ON, Canada
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7
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Zhang N, Song Q, Liang H, Wang Z, Wu Q, Zhang H, Zhang L, Liu A, Wang H, Wang J, Lin L. Early prediction of pathological response to neoadjuvant chemotherapy of breast tumors: a comparative study using amide proton transfer-weighted, diffusion weighted and dynamic contrast enhanced MRI. Front Med (Lausanne) 2024; 11:1295478. [PMID: 38298813 PMCID: PMC10827983 DOI: 10.3389/fmed.2024.1295478] [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: 09/16/2023] [Accepted: 01/05/2024] [Indexed: 02/02/2024] Open
Abstract
Objective To examine amide proton transfer-weighted (APTw) combined with diffusion weighed (DWI) and dynamic contrast enhanced (DCE) MRI for early prediction of pathological response to neoadjuvant chemotherapy in invasive breast cancer. Materials In this prospective study, 50 female breast cancer patients (49.58 ± 10.62 years old) administered neoadjuvant chemotherapy (NAC) were enrolled with MRI carried out both before NAC (T0) and at the end of the second cycle of NAC (T1). The patients were divided into 2 groups based on tumor response according to the Miller-Payne Grading (MPG) system. Group 1 included patients with a greater degree of decrease in major histologic responder (MHR, Miller-Payne G4-5), while group 2 included non-MHR cases (Miller-Payne G1-3). Traditional imaging protocols (T1 weighted, T2 weighted, diffusion weighted, and DCE-MRI) and APTw imaging were scanned for each subject before and after treatment. APTw value (APTw0 and APTw1), Dmax (maximum diameter, Dmax0 and Dmax1), V (3D tumor volume, V0 and V1), and ADC (apparent diffusion coefficient, ADC0 and ADC1) before and after treatment, as well as changes between the two times points (ΔAPT, ΔDmax, ΔV, ΔADC) for breast tumors were compared between the two groups. Results APT0 and APT1 values significantly differed between the two groups (p = 0.034 and 0.01). ΔAPTw values were significantly lower in non-MHR tumors compared with MHR tumors (p = 0.015). ΔDmax values were significantly higher in MHR tumors compared with non-MHR tumors (p = 0.005). ADC0 and ADC1 values were significantly higher in MHR tumors than in non-MHR tumors (p = 0.038 and 0.035). AUC (Dmax+DWI + APTw) = AUC (Dmax+APTw) > AUC (APTw) > AUC (Dmax+DWI) > AUC (Dmax). Conclusion APTw imaging along with change of tumor size showed a significant potential in early prediction of MHR for NAC treatment in breast cancer, which might allow timely regimen refinement before definitive surgical treatment.
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Affiliation(s)
- Nan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qingwei Song
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Hongbing Liang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Zhuo Wang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Qi Wu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Haonan Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Lina Zhang
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Ailian Liu
- Department of Radiology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Huali Wang
- Department of Pathology, First Affiliated Hospital, Dalian Medical University, Dalian, China
| | - Jiazheng Wang
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
| | - Liangjie Lin
- MSC Clinical and Technical Solutions, Philips Healthcare, Beijing, China
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8
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Saednia K, Tran WT, Sadeghi-Naini A. A hierarchical self-attention-guided deep learning framework to predict breast cancer response to chemotherapy using pre-treatment tumor biopsies. Med Phys 2023; 50:7852-7864. [PMID: 37403567 DOI: 10.1002/mp.16574] [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: 09/22/2022] [Revised: 06/06/2023] [Accepted: 06/10/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) has demonstrated a strong correlation to improved survival in breast cancer (BC) patients. However, pCR rates to NAC are less than 30%, depending on the BC subtype. Early prediction of NAC response would facilitate therapeutic modifications for individual patients, potentially improving overall treatment outcomes and patient survival. PURPOSE This study, for the first time, proposes a hierarchical self-attention-guided deep learning framework to predict NAC response in breast cancer patients using digital histopathological images of pre-treatment biopsy specimens. METHODS Digitized hematoxylin and eosin-stained slides of BC core needle biopsies were obtained from 207 patients treated with NAC, followed by surgery. The response to NAC for each patient was determined using the standard clinical and pathological criteria after surgery. The digital pathology images were processed through the proposed hierarchical framework consisting of patch-level and tumor-level processing modules followed by a patient-level response prediction component. A combination of convolutional layers and transformer self-attention blocks were utilized in the patch-level processing architecture to generate optimized feature maps. The feature maps were analyzed through two vision transformer architectures adapted for the tumor-level processing and the patient-level response prediction components. The feature map sequences for these transformer architectures were defined based on the patch positions within the tumor beds and the bed positions within the biopsy slide, respectively. A five-fold cross-validation at the patient level was applied on the training set (144 patients with 9430 annotated tumor beds and 1,559,784 patches) to train the models and optimize the hyperparameters. An unseen independent test set (63 patients with 3574 annotated tumor beds and 173,637 patches) was used to evaluate the framework. RESULTS The obtained results on the test set showed an AUC of 0.89 and an F1-score of 90% for predicting pCR to NAC a priori by the proposed hierarchical framework. Similar frameworks with the patch-level, patch-level + tumor-level, and patch-level + patient-level processing components resulted in AUCs of 0.79, 0.81, and 0.84 and F1-scores of 86%, 87%, and 89%, respectively. CONCLUSIONS The results demonstrate a high potential of the proposed hierarchical deep-learning methodology for analyzing digital pathology images of pre-treatment tumor biopsies to predict the pathological response of breast cancer to NAC.
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Affiliation(s)
- Khadijeh Saednia
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
| | - William T Tran
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Department of Radiation Oncology, University of Toronto, Toronto, Ontario, Canada
- Temerity Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ali Sadeghi-Naini
- Department of Electrical Engineering and Computer Science, Lassonde School of Engineering, York University, Toronto, Ontario, Canada
- Department of Radiation Oncology, Sunnybrook Health Sciences Center, Toronto, Ontario, Canada
- Temerity Centre for AI Research and Education in Medicine, University of Toronto, Toronto, Ontario, Canada
- Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
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9
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Li JW, Sheng DL, Chen JG, You C, Liu S, Xu HX, Chang C. Artificial intelligence in breast imaging: potentials and challenges. Phys Med Biol 2023; 68:23TR01. [PMID: 37722385 DOI: 10.1088/1361-6560/acfade] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 09/18/2023] [Indexed: 09/20/2023]
Abstract
Breast cancer, which is the most common type of malignant tumor among humans, is a leading cause of death in females. Standard treatment strategies, including neoadjuvant chemotherapy, surgery, postoperative chemotherapy, targeted therapy, endocrine therapy, and radiotherapy, are tailored for individual patients. Such personalized therapies have tremendously reduced the threat of breast cancer in females. Furthermore, early imaging screening plays an important role in reducing the treatment cycle and improving breast cancer prognosis. The recent innovative revolution in artificial intelligence (AI) has aided radiologists in the early and accurate diagnosis of breast cancer. In this review, we introduce the necessity of incorporating AI into breast imaging and the applications of AI in mammography, ultrasonography, magnetic resonance imaging, and positron emission tomography/computed tomography based on published articles since 1994. Moreover, the challenges of AI in breast imaging are discussed.
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Affiliation(s)
- Jia-Wei Li
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Dan-Li Sheng
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jian-Gang Chen
- Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication & Electronic Engineering, East China Normal University, People's Republic of China
| | - Chao You
- Department of Radiology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Shuai Liu
- Department of Nuclear Medicine, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, People's Republic of China
| | - Hui-Xiong Xu
- Department of Ultrasound, Zhongshan Hospital, Institute of Ultrasound in Medicine and Engineering, Fudan University, Shanghai, 200032, People's Republic of China
| | - Cai Chang
- Department of Medical Ultrasound, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
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10
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Elsayed B, Alksas A, Shehata M, Mahmoud A, Zaky M, Alghandour R, Abdelwahab K, Abdelkhalek M, Ghazal M, Contractor S, El-Din Moustafa H, El-Baz A. Exploring Neoadjuvant Chemotherapy, Predictive Models, Radiomic, and Pathological Markers in Breast Cancer: A Comprehensive Review. Cancers (Basel) 2023; 15:5288. [PMID: 37958461 PMCID: PMC10648987 DOI: 10.3390/cancers15215288] [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: 09/02/2023] [Revised: 10/30/2023] [Accepted: 11/01/2023] [Indexed: 11/15/2023] Open
Abstract
Breast cancer retains its position as the most prevalent form of malignancy among females on a global scale. The careful selection of appropriate treatment for each patient holds paramount importance in effectively managing breast cancer. Neoadjuvant chemotherapy (NACT) plays a pivotal role in the comprehensive treatment of this disease. Administering chemotherapy before surgery, NACT becomes a powerful tool in reducing tumor size, potentially enabling fewer invasive surgical procedures and even rendering initially inoperable tumors amenable to surgery. However, a significant challenge lies in the varying responses exhibited by different patients towards NACT. To address this challenge, researchers have focused on developing prediction models that can identify those who would benefit from NACT and those who would not. Such models have the potential to reduce treatment costs and contribute to a more efficient and accurate management of breast cancer. Therefore, this review has two objectives: first, to identify the most effective radiomic markers correlated with NACT response, and second, to explore whether integrating radiomic markers extracted from radiological images with pathological markers can enhance the predictive accuracy of NACT response. This review will delve into addressing these research questions and also shed light on the emerging research direction of leveraging artificial intelligence techniques for predicting NACT response, thereby shaping the future landscape of breast cancer treatment.
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Affiliation(s)
- Basma Elsayed
- Biomedical Engineering Program, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt;
| | - Ahmed Alksas
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mohamed Shehata
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Ali Mahmoud
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
| | - Mona Zaky
- Diagnostic Radiology Department, Faculty of Medicine, Mansoura University, Mansoura 35516, Egypt;
| | - Reham Alghandour
- Medical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt;
| | - Khaled Abdelwahab
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohamed Abdelkhalek
- Surgical Oncology Department, Mansoura Oncology Center, Mansoura University, Mansoura 35516, Egypt; (K.A.); (M.A.)
| | - Mohammed Ghazal
- Electrical, Computer, and Biomedical Engineering Department, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates;
| | - Sohail Contractor
- Department of Radiology, University of Louisville, Louisville, KY 40202, USA;
| | | | - Ayman El-Baz
- Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA; (A.A.); (M.S.); (A.M.)
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11
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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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12
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Abstract
New challenges are currently faced by clinical and surgical oncologists in the management of patients with breast cancer, mainly related to the need for molecular and prognostic data. Recent technological advances in diagnostic imaging and informatics have led to the introduction of functional imaging modalities, such as hybrid PET/MR imaging, and artificial intelligence (AI) software, aimed at the extraction of quantitative radiomics data, which may reflect tumor biology and behavior. In this article, the most recent applications of radiomics and AI to PET/MR imaging are described to address the new needs of clinical and surgical oncology.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Via S. Pansini 5, Naples 80138, Italy.
| | - Linda Moy
- Department of Radiology, New York University School of Medicine, 160 East 34th Street, New York, NY 10016, USA
| | - Katja Pinker
- Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, 300 East 66th Street, New York, NY 10065, USA
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13
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Qi J, Wang C, Ma Y, Wang J, Yang G, Wu Y, Wang H, Mi C. The potential role of combined shear wave elastography and superb microvascular imaging for early prediction the pathological response to neoadjuvant chemotherapy in breast cancer. Front Oncol 2023; 13:1176141. [PMID: 37746288 PMCID: PMC10515084 DOI: 10.3389/fonc.2023.1176141] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 08/22/2023] [Indexed: 09/26/2023] Open
Abstract
Objectives The potential role of shear wave elastography (SWE) and superb microvascular imaging (SMI) for early assessment of treatment response to neoadjuvant chemotherapy (NAC) in breast cancer remains unexplored. This study aimed to identify potential factors associated with the pathological response to NAC using these advanced ultrasound techniques. Methods Between August 2021 and October 2022, 68 patients with breast cancer undergoing NAC were recruited. Patients underwent conventional ultrasonography, SMI, and SWE examinations at baseline and post-2nd cycle of NAC. Maximum tumor diameter (Dmax), maximum elastic value (Emax), peak systolic velocity (PSV), and resistance index (RI) at baseline and the rate of change of these parameters post-2nd cycle were recorded. After chemotherapy, all patients underwent surgery. Using the Miller-Payne's grade, patients were categorized into response (grades 3, 4, or 5) and non-response (grades 1 or 2) group. Parameters were compared using t-tests at baseline and post-2nd cycle. Binary logistic regression analysis was used to identify variables and their odds ratios (ORs) related to responses and a prediction model was established. ROC curves were drawn to analyze the efficacy of each parameter and their combined model for early NAC response prediction. Results Among the 68 patients, 15(22.06%) were categorized into the non-response group, whereas 53(77.94%) were categorized into the response group. At baseline, no significant differences were observed between the two groups (p>0.05). Post-2nd cycle of NAC, rates of change of Emax, PSV and RI (ΔEmax, ΔPSV and ΔRI) were higher in responders than non-responders (p<0.05). Binary logistic regression analysis revealed that ΔEmax (OR 0.797 95% CI, 0.683-0.929), ΔPSV (OR 0.926, 95%CI, 0.860-0.998), and ΔRI (OR 0.841, 95%CI, 0.736-0.960) were independently associated with the pathological response of breast cancer after NAC. The combined prediction model exhibited higher accuracy in the early evaluation of the response to NAC (AUC 0.945, 95%CI, 0.873-1.000). Conclusion SWE and SMI techniques enable early identification of tumor characteristics associated with the pathological response to NAC and may be potentially indicative of an effective response. These factors may eventually be used for the early assessment of NAC treatment for clinical management.
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Affiliation(s)
- Jiaojiao Qi
- Department of Obstetrics and Gynecology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Chenyu Wang
- Ningxia Medical University, Yinchuan, Ningxia, China
| | - Yongxin Ma
- Ningxia Medical University, Yinchuan, Ningxia, China
| | - Jiaxing Wang
- Department of Obstetrics and Gynecology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Guangfei Yang
- Department of Ultrasound, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Yating Wu
- Department of Ultrasound, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Haiyan Wang
- Department of Obstetrics and Gynecology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
| | - Chengrong Mi
- Department of Ultrasound, General Hospital of Ningxia Medical University, Yinchuan, Ningxia, China
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14
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Radak M, Lafta HY, Fallahi H. Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies. J Cancer Res Clin Oncol 2023; 149:10473-10491. [PMID: 37278831 DOI: 10.1007/s00432-023-04956-z] [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: 04/26/2023] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
BACKGROUND Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of breast cancer. PURPOSE In this review, we examine studies that have used these techniques for breast cancer classification and diagnosis, focusing on five groups of medical images: mammography, ultrasound, MRI, histology, and thermography. We discuss the use of five popular machine learning techniques, including Nearest Neighbor, SVM, Naive Bayesian Network, DT, and ANN, as well as deep learning architectures and convolutional neural networks. CONCLUSION Our review finds that machine learning and deep learning techniques have achieved high accuracy rates in breast cancer classification and diagnosis across various medical imaging modalities. Furthermore, these techniques have the potential to improve clinical decision-making and ultimately lead to better patient outcomes.
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Affiliation(s)
- Mehran Radak
- Department of Biology, School of Sciences, Razi University, Baq-e-Abrisham, Kermanshah, 6714967346, Islamic Republic of Iran
| | - Haider Yabr Lafta
- Department of Biology, School of Sciences, Razi University, Baq-e-Abrisham, Kermanshah, 6714967346, Islamic Republic of Iran
| | - Hossein Fallahi
- Department of Biology, School of Sciences, Razi University, Baq-e-Abrisham, Kermanshah, 6714967346, Islamic Republic of Iran.
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15
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Chen Z, Huang M, Lyu J, Qi X, He F, Li X. Machine learning for predicting breast-conserving surgery candidates after neoadjuvant chemotherapy based on DCE-MRI. Front Oncol 2023; 13:1174843. [PMID: 37621690 PMCID: PMC10446166 DOI: 10.3389/fonc.2023.1174843] [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: 02/27/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023] Open
Abstract
Purpose This study aimed to investigate a machine learning method for predicting breast-conserving surgery (BCS) candidates, from patients who received neoadjuvant chemotherapy (NAC) by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) obtained before and after NAC. Materials and methods This retrospective study included 75 patients who underwent NAC and breast surgery. First, 3,390 features were comprehensively extracted from pre- and post-NAC DCE-MRIs. Then patients were then divided into two groups: type 1, patients with pathologic complete response (pCR) and single lesion shrinkage; type 2, major residual lesion with satellite foci, multifocal residual, stable disease (SD), and progressive disease (PD). The logistic regression (LR) was used to build prediction models to identify the two groups. Prediction performance was assessed using the area under the curve (AUC), accuracy, sensitivity, and specificity. Results Radiomics features were significantly related to breast cancer shrinkage after NAC. The combination model achieved an AUC of 0.82, and the pre-NAC model was 0.64, the post-NAC model was 0.70, and the pre-post-NAC model was 0.80. In the combination model, 15 features, including nine wavelet-based features, four Laplacian-of-Gauss (LoG) features, and two original features, were filtered. Among these selected were four features from pre-NAC DCE-MRI, six were from post-NAC DCE-MRI, and five were from pre-post-NAC features. Conclusion The model combined with pre- and post-NAC DCE-MRI can effectively predict candidates to undergo BCS and provide AI-based decision support for clinicians with ensured safety. High-order (LoG- and wavelet-based) features play an important role in our machine learning model. The features from pre-post-NAC DCE-MRI had better predictive performance.
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Affiliation(s)
| | | | | | | | | | - Xiang Li
- Department of Radiology, the Second Hospital of Dalian Medical University, Dalian, China
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16
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Machado P, Liu JB, Needleman L, Lazar M, Willis AI, Brill K, Nazarian S, Berger A, Forsberg F. Sentinel Lymph Node Identification in Post Neoadjuvant Chemotherapy Breast Cancer Patients Undergoing Surgical Excision Using Lymphosonography. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1509-1517. [PMID: 36591785 DOI: 10.1002/jum.16164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 12/09/2022] [Accepted: 12/10/2022] [Indexed: 06/17/2023]
Abstract
OBJECTIVES This study evaluated the efficacy of lymphosonography in the identification of sentinel lymph nodes (SLNs) in post neoadjuvant chemotherapy patients with breast cancer scheduled to undergo surgical excision. METHODS Seventy-nine subjects scheduled for breast cancer surgery with SLN excision completed this IRB-approved study, out of which 18 (23%) underwent neoadjuvant chemotherapy before surgery. Subjects underwent percutaneous Sonazoid (GE Healthcare) injections around the tumor area for a total of 1.0 mL. Lymphosonography was performed using CPS on an S3000 HELX scanner (Siemens Healthineers) with a linear probe. Subjects received blue dye and radioactive tracer as part of their standard of care. Excised SLNs were classified as positive or negative for the presence of blue dye, radioactive tracer and Sonazoid. The results were compared between methods and pathology findings. RESULTS Seventy-two SLNs were surgically excised from 18 subjects, 29 were positive for blue dye, 63 were positive for radioactive tracer and 57 were positive for Sonazoid. Comparison with blue dye showed that both radioactive tracer and lymphosonography achieved an accuracy of 53% (P > .50). Comparison with radioactive tracer showed that blue dye had an accuracy of 53%, while lymphosonography achieved an accuracy of 67% (P < .01). Of the 72 SLNs, 15 were determined malignant by pathology; the detection rate was 47% for blue dye (7/15), 67% for radioactive tracer (10/15) and 100% for lymphosonography (15/15) (P < .001). CONCLUSIONS Lymphosonography achieved similar accuracy as radioactive tracer and higher accuracy than blue dye for identifying SLNs. The 15 SLNs positive for malignancy were all identified by lymphosonography.
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Affiliation(s)
- Priscilla Machado
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Laurence Needleman
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Melissa Lazar
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Alliric I Willis
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Kristin Brill
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Susanna Nazarian
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Adam Berger
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flemming Forsberg
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
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Chopra S, Khosla M, Vidya R. Innovations and Challenges in Breast Cancer Care: A Review. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59050957. [PMID: 37241189 DOI: 10.3390/medicina59050957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 05/05/2023] [Accepted: 05/09/2023] [Indexed: 05/28/2023]
Abstract
Breast cancer care has seen tremendous advancements in recent years through various innovations to improve early detection, diagnosis, treatment, and survival. These innovations include advancements in imaging techniques, minimally invasive surgical techniques, targeted therapies and personalized medicine, radiation therapy, and multidisciplinary care. It is essential to recognize that challenges and limitations exist while significant advancements in breast cancer care exist. Continued research, advocacy, and efforts to address these challenges are necessary to make these innovations accessible to all patients while carefully considering and managing the ethical, social, and practical implications.
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Affiliation(s)
- Sharat Chopra
- Aneurin Bevan University Health Board, The Royal Gwent Hospital, Newport NP20 2UB, UK
| | - Muskaan Khosla
- The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK
| | - Raghavan Vidya
- The Royal Wolverhampton NHS Trust, Wolverhampton WV10 0QP, UK
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Tsydenova IA, Dolgasheva DS, Gaptulbarova KA, Ibragimova MK, Tsyganov MM, Kravtsova EA, Nushtaeva AA, Litviakov NV. WNT-Conditioned Mechanism of Exit from Postchemotherapy Shock of Differentiated Tumour Cells. Cancers (Basel) 2023; 15:2765. [PMID: 37345102 DOI: 10.3390/cancers15102765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/11/2023] [Accepted: 05/11/2023] [Indexed: 06/23/2023] Open
Abstract
BACKGROUND the present study aims to prove or disprove the hypothesis that the state of copy number aberration (CNA) activation of WNT signalling pathway genes accounts for the ability of differentiated tumour cells to emerge from postchemotherapy shock. METHODS In the first step, the CNA genetic landscape of breast cancer cell lines BT-474, BT-549, MDA-MB-231, MDA-MD-468, MCF7, SK-BR-3 and T47D, which were obtained from ATCC, was examined to rank cell cultures according to the degree of ectopic activation of the WNT signalling pathway. Then two lines of T47D with ectopic activation and BT-474 without activation were selected. The differentiated EpCAM+CD44-CD24-/+ cells of these lines were subjected to IL6 de-differentiation with formation of mammospheres on the background of cisplatin and WNT signalling inhibitor ICG-001. RESULTS it was found that T47D cells with ectopic WNT signalling activation after cisplatin exposure were dedifferentiated to form mammospheres while BT-474 cells without ectopic WNT-signalling activation did not form mammospheres. The dedifferentiation of T47D cells after cisplatin exposure was completely suppressed by the WNT signalling inhibitor ICG-001. Separately, ICG-001 reduced, but did not abolish, the ability to dedifferentiate in both cell lines. CONCLUSIONS these data support the hypothesis that the emergence of differentiated tumour cells from postchemotherapy shock after chemotherapy is due to ectopic activation of WNT signalling pathway genes.
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Affiliation(s)
- Irina A Tsydenova
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Biological Institute, National Research Tomsk State University, 634050 Tomsk, Russia
| | - Daria S Dolgasheva
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Biological Institute, National Research Tomsk State University, 634050 Tomsk, Russia
| | - Ksenia A Gaptulbarova
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Biological Institute, National Research Tomsk State University, 634050 Tomsk, Russia
- Genetic Technology Laboratory, Siberian State Medical University, 634050 Tomsk, Russia
| | - Marina K Ibragimova
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Biological Institute, National Research Tomsk State University, 634050 Tomsk, Russia
- Genetic Technology Laboratory, Siberian State Medical University, 634050 Tomsk, Russia
| | - Matvei M Tsyganov
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Genetic Technology Laboratory, Siberian State Medical University, 634050 Tomsk, Russia
| | - Ekaterina A Kravtsova
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Biological Institute, National Research Tomsk State University, 634050 Tomsk, Russia
| | - Anna A Nushtaeva
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of Russian Academy of Sciences, 630090 Novosibirsk, Russia
| | - Nikolai V Litviakov
- Cancer Research Institute, Tomsk National Research Medical Center, Russian Academy of Sciences, 634028 Tomsk, Russia
- Biological Institute, National Research Tomsk State University, 634050 Tomsk, Russia
- Genetic Technology Laboratory, Siberian State Medical University, 634050 Tomsk, Russia
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Liu Y, Wang S, Qu J, Tang R, Wang C, Xiao F, Pang P, Sun Z, Xu M, Li J. High-temporal resolution DCE-MRI improves assessment of intra- and peri-breast lesions categorized as BI-RADS 4. BMC Med Imaging 2023; 23:58. [PMID: 37076817 PMCID: PMC10116788 DOI: 10.1186/s12880-023-01015-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 04/06/2023] [Indexed: 04/21/2023] Open
Abstract
BACKGROUND BI-RADS 4 breast lesions are suspicious for malignancy with a range from 2 to 95%, indicating that numerous benign lesions are unnecessarily biopsied. Thus, we aimed to investigate whether high-temporal-resolution dynamic contrast-enhanced MRI (H_DCE-MRI) would be superior to conventional low-temporal-resolution DCE-MRI (L_DCE-MRI) in the diagnosis of BI-RADS 4 breast lesions. METHODS This single-center study was approved by the IRB. From April 2015 to June 2017, patients with breast lesions were prospectively included and randomly assigned to undergo either H_DCE-MRI, including 27 phases, or L_DCE-MRI, including 7 phases. Patients with BI-RADS 4 lesions were diagnosed by the senior radiologist in this study. Using a two-compartment extended Tofts model and a three-dimensional volume of interest, several pharmacokinetic parameters reflecting hemodynamics, including Ktrans, Kep, Ve, and Vp, were obtained from the intralesional, perilesional and background parenchymal enhancement areas, which were labeled the Lesion, Peri and BPE areas, respectively. Models were developed based on hemodynamic parameters, and the performance of these models in discriminating between benign and malignant lesions was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS A total of 140 patients were included in the study and underwent H_DCE-MRI (n = 62) or L_DCE-MRI (n = 78) scans; 56 of these 140 patients had BI-RADS 4 lesions. Some pharmacokinetic parameters from H_DCE-MRI (Lesion_Ktrans, Kep, and Vp; Peri_Ktrans, Kep, and Vp) and from L_DCE-MRI (Lesion_Kep, Peri_Vp, BPE_Ktrans and BPE_Vp) were significantly different between benign and malignant breast lesions (P < 0.01). ROC analysis showed that Lesion_Ktrans (AUC = 0.866), Lesion_Kep (AUC = 0.929), Lesion_Vp (AUC = 0.872), Peri_Ktrans (AUC = 0.733), Peri_Kep (AUC = 0.810), and Peri_Vp (AUC = 0.857) in the H_DCE-MRI group had good discrimination performance. Parameters from the BPE area showed no differentiating ability in the H_DCE-MRI group. Lesion_Kep (AUC = 0.767), Peri_Vp (AUC = 0.726), and BPE_Ktrans and BPE_Vp (AUC = 0.687 and 0.707) could differentiate between benign and malignant breast lesions in the L_DCE-MRI group. The models were compared with the senior radiologist's assessment for the identification of BI-RADS 4 breast lesions. The AUC, sensitivity and specificity of Lesion_Kep (0.963, 100.0%, and 88.9%, respectively) in the H_DCE-MRI group were significantly higher than those of the same parameter in the L_DCE-MRI group (0.663, 69.6% and 75.0%, respectively) for the assessment of BI-RADS 4 breast lesions. The DeLong test was conducted, and there was a significant difference only between Lesion_Kep in the H_DCE-MRI group and the senior radiologist (P = 0.04). CONCLUSIONS Pharmacokinetic parameters (Ktrans, Kep and Vp) from the intralesional and perilesional regions on high-temporal-resolution DCE-MRI, especially the intralesional Kep parameter, can improve the assessment of benign and malignant BI-RADS 4 breast lesions to avoid unnecessary biopsy.
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Affiliation(s)
- Yufeng Liu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Shiwei Wang
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Jingjing Qu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Rui Tang
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Chundan Wang
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Fengchun Xiao
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
- Department of Pathology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China
| | - Peipei Pang
- GE Healthcare, Precision Health Institution, Hangzhou, China
| | - Zhichao Sun
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China
| | - Maosheng Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
| | - Jiaying Li
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.
- The First Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, China.
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EL-Metwally D, Monier D, Hassan A, Helal AM. Preoperative prediction of Ki-67 status in invasive breast carcinoma using dynamic contrast-enhanced MRI, diffusion-weighted imaging and diffusion tensor imaging. THE EGYPTIAN JOURNAL OF RADIOLOGY AND NUCLEAR MEDICINE 2023. [DOI: 10.1186/s43055-023-01007-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023] Open
Abstract
Abstract
Background
The Ki-67 is a beneficial marker of tumor aggressiveness. It is proliferation index that has been used to distinguish luminal B from luminal A breast cancers. By fast progress in quantitative radiology modalities, tumor biology and genetics can be assessed in a more accurate, predictive, and cost-effective method. The aim of this study was to assess the role of dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging and diffusion tensor imaging in prediction of Ki-67 status in patients with invasive breast carcinoma estimate cut off values between breast cancer with high Ki-67 status and those with low Ki-67 status.
Results
Cut off ADC (apparent diffusion co-efficient) value of 0.657 mm2/s had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off maximum enhancement value of 1715 had 96.4% sensitivity, 75% specificity and 93.8% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off washout rate of 0.73 I/S had 60.7% sensitivity, 75% specificity and 62.5% accuracy in differentiating cases with high Ki67 from those with low Ki67. Cut off time to peak value of 304 had 71.4% sensitivity, 75% specificity and 71.9% accuracy in differentiating cases with high Ki67 from those with low Ki67.
Conclusions
ADC, time to peak and maximum enhancement values had high sensitivity, specificity and accuracy in differentiating breast cancer with high Ki-67 status from those with low Ki-67 status.
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Han X, Li H, Dong SS, Zhou SY, Wang CH, Guo L, Yang J, Zhang GL. Application of triple evaluation method in predicting the efficacy of neoadjuvant therapy for breast cancer. World J Surg Oncol 2023; 21:116. [PMID: 36978164 PMCID: PMC10052864 DOI: 10.1186/s12957-023-02998-8] [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: 11/21/2022] [Accepted: 03/22/2023] [Indexed: 03/30/2023] Open
Abstract
OBJECTIVE To analyze the factors related to the efficacy of neoadjuvant therapy for breast cancer and find appropriate evaluation methods for evaluating the efficacy of neoadjuvant therapy METHODS: A total of 143 patients with breast cancer treated by neoadjuvant chemotherapy at Baotou Cancer Hospital were retrospectively analyzed. The chemotherapy regimen was mainly paclitaxel combined with carboplatin for 1 week, docetaxel combined with carboplatin for 3 weeks, and was replaced with epirubicin combined with cyclophosphamide after evaluation of disease progression. All HER2-positive patients were treated with simultaneous targeted therapy, including trastuzumab single-target therapy and trastuzumab combined with pertuzumab double-target therapy. Combined with physical examination, color Doppler ultrasound, and magnetic resonance imaging (MRI), a systematic evaluation system was initially established-the "triple evaluation method." A baseline evaluation was conducted before treatment. The efficacy was evaluated by physical examination and color Doppler every cycle, and the efficacy was evaluated by physical examination, color Doppler, and MRI every two cycles. RESULTS The increase in ultrasonic blood flow after treatment could affect the efficacy of monitoring. The presence of two preoperative time-signal intensity curves is a therapeutically effective protective factor for inflow. The triple evaluation determined by physical examination, color Doppler ultrasound, and MRI in determining clinical efficacy is consistent with the effectiveness of the pathological gold standard. CONCLUSION The therapeutic effect of neoadjuvant therapy can be better evaluated by combining clinical physical examination, color ultrasound, and nuclear magnetic resonance evaluation. The three methods complement each other to avoid the insufficient evaluation of a single method, which is convenient for most prefecty-level hospitals. Additionally, this method is simple, feasible, and suitable for promotion.
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Affiliation(s)
- Xu Han
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China
| | - Hui Li
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China
| | - Sha-Sha Dong
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China
| | - Shui-Ying Zhou
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China
| | - Cai-Hong Wang
- Department of Operating Room, Baotou Cancer Hospital, Baotou, 014030, Inner Mongolia, China
| | - Lin Guo
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China
| | - Jie Yang
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China
| | - Gang-Ling Zhang
- Department of Breast Surgery, Baotou Cancer Hospital, No.18 Tuanjie Street, Qingshan District, Baotou, 014030, Inner Mongolia, China.
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Romeo V, Cuocolo R, Sanduzzi L, Carpentiero V, Caruso M, Lama B, Garifalos D, Stanzione A, Maurea S, Brunetti A. MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer. Cancers (Basel) 2023; 15:cancers15061840. [PMID: 36980724 PMCID: PMC10047199 DOI: 10.3390/cancers15061840] [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: 01/13/2023] [Revised: 02/17/2023] [Accepted: 03/13/2023] [Indexed: 03/30/2023] Open
Abstract
AIM To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with ER+ HER2- invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm. MATERIALS AND METHODS Pre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS. RESULTS 248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable (n = 771), not variant (n = 82), and highly intercorrelated (n = 391), respectively. After the use of recursive feature elimination with logistic regression estimator and polynomial transformation, 92 features were finally selected. In the training set, the logistic regression classifier obtained an overall mean accuracy of 60%. In the test set, the accuracy of the ML classifier was 63%, with a sensitivity of 80%, specificity of 43%, and AUC of 66%. CONCLUSIONS Radiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, 84084 Baronissi, Italy
- Augmented Reality for Health Monitoring Laboratory (ARHeMLab), Department of Electrical Engineering and Information Technology, University of Naples "Federico II", 80131 Naples, Italy
| | - Luca Sanduzzi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Vincenzo Carpentiero
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Martina Caruso
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Beatrice Lama
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Dimitri Garifalos
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", 80131 Naples, Italy
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Romeo V, Helbich TH, Pinker K. Breast PET/MRI Hybrid Imaging and Targeted Tracers. J Magn Reson Imaging 2023; 57:370-386. [PMID: 36165348 PMCID: PMC10074861 DOI: 10.1002/jmri.28431] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 09/01/2022] [Accepted: 09/02/2022] [Indexed: 01/20/2023] Open
Abstract
The recent introduction of hybrid positron emission tomography/magnetic resonance imaging (PET/MRI) as a promising imaging modality for breast cancer assessment has prompted fervent research activity on its clinical applications. The current knowledge regarding the possible clinical applications of hybrid PET/MRI is constantly evolving, thanks to the development and clinical availability of hybrid scanners, the development of new PET tracers and the rise of artificial intelligence (AI) techniques. In this state-of-the-art review on the use of hybrid breast PET/MRI, the most promising advanced MRI techniques (diffusion-weighted imaging, dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, and chemical exchange saturation transfer) are discussed. Current and experimental PET tracers (18 F-FDG, 18 F-NaF, choline, 18 F-FES, 18 F-FES, 89 Zr-trastuzumab, choline derivatives, 18 F-FLT, and 68 Ga-FAPI-46) are described in order to provide an overview on their molecular mechanisms of action and corresponding clinical applications. New perspectives represented by the use of radiomics and AI techniques are discussed. Furthermore, the current strengths and limitations of hybrid PET/MRI in the real world are highlighted. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Thomas H Helbich
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria
| | - Katja Pinker
- Division of General and Pediatric Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Wien, Austria.,Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Machado P, Liu JB, Needleman L, Lazar M, Willis AI, Brill K, Nazarian S, Berger A, Forsberg F. Sentinel Lymph Node Identification in Patients With Breast Cancer Using Lymphosonography. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:616-625. [PMID: 36446688 PMCID: PMC9943072 DOI: 10.1016/j.ultrasmedbio.2022.10.020] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 09/27/2022] [Accepted: 10/30/2022] [Indexed: 06/06/2023]
Abstract
The objective of the work described here was to evaluate the efficacy of lymphosonography in identifying sentinel lymph nodes (SLNs) in patients with breast cancer undergoing surgical excision. Of the 86 individuals enrolled, 79 completed this institutional review board-approved study. Participants received subcutaneous 1.0-mL injections of ultrasound contrast agent (UCA) around the tumor. An ultrasound scanner with contrast-enhanced ultrasound (CEUS) capabilities was used to identify SLNs. Participants were administered with blue dye and radioactive tracer to guide SLN excision as standard-of-care. Excised SLNs were classified as positive or negative for the presence of blue dye, radioactive tracer and UCA, and sent for pathology. Two hundred fifty-two SLNs were excised; 158 were positive for blue dye, 222 were positive for radioactive tracer and 223 were positive for UCA. Comparison with blue dye revealed accuracies of 96.2% for radioactive tracer and 99.4% for lymphosonography (p > 0.15). Relative to radioactive tracer, blue dye had an accuracy of 68.5%, and lymphosonography achieved 86.5% (p < 0.0001). Of 252 SLNs excised, 34 were determined to be malignant by pathology; 18 were positive for blue dye (detection rate = 53%), 23 for radioactive tracer (detection rate = 68%) and 34 for UCA (detection rate = 100%) (p < 0.0001). Lymphosonography was similar in accuracy to radioactive tracer and higher in accuracy than blue dye in identifying SLNs. All 34 malignant SLNs were identified by lymphosonography.
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Affiliation(s)
- Priscilla Machado
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Ji-Bin Liu
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Laurence Needleman
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Melissa Lazar
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Alliric I Willis
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Kristin Brill
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Susanna Nazarian
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Adam Berger
- Department of Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Flemming Forsberg
- Department of Radiology, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
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Zheng D, He X, Jing J. Overview of Artificial Intelligence in Breast Cancer Medical Imaging. J Clin Med 2023; 12:jcm12020419. [PMID: 36675348 PMCID: PMC9864608 DOI: 10.3390/jcm12020419] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/26/2022] [Accepted: 12/30/2022] [Indexed: 01/07/2023] Open
Abstract
The heavy global burden and mortality of breast cancer emphasize the importance of early diagnosis and treatment. Imaging detection is one of the main tools used in clinical practice for screening, diagnosis, and treatment efficacy evaluation, and can visualize changes in tumor size and texture before and after treatment. The overwhelming number of images, which lead to a heavy workload for radiologists and a sluggish reporting period, suggests the need for computer-aid detection techniques and platform. In addition, complex and changeable image features, heterogeneous quality of images, and inconsistent interpretation by different radiologists and medical institutions constitute the primary difficulties in breast cancer screening and imaging diagnosis. The advancement of imaging-based artificial intelligence (AI)-assisted tumor diagnosis is an ideal strategy for improving imaging diagnosis efficient and accuracy. By learning from image data input and constructing algorithm models, AI is able to recognize, segment, and diagnose tumor lesion automatically, showing promising application prospects. Furthermore, the rapid advancement of "omics" promotes a deeper and more comprehensive recognition of the nature of cancer. The fascinating relationship between tumor image and molecular characteristics has attracted attention to the radiomic and radiogenomics, which allow us to perform analysis and detection on the molecular level with no need for invasive operations. In this review, we integrate the current developments in AI-assisted imaging diagnosis and discuss the advances of AI-based breast cancer precise diagnosis from a clinical point of view. Although AI-assisted imaging breast cancer screening and detection is an emerging field and draws much attention, the clinical application of AI in tumor lesion recognition, segmentation, and diagnosis is still limited to research or in limited patients' cohort. Randomized clinical trials based on large and high-quality cohort are lacking. This review aims to describe the progress of the imaging-based AI application in breast cancer screening and diagnosis for clinicians.
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Priscilla MMD, Ji-Bin LMD, Flemming FP. Sentinel Lymph Node Identification Using Contrast Lymphosonography: A Systematic Review. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY 2023. [DOI: 10.37015/audt.2023.230001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023] Open
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Artificial Intelligence (AI) in Breast Imaging: A Scientometric Umbrella Review. Diagnostics (Basel) 2022; 12:diagnostics12123111. [PMID: 36553119 PMCID: PMC9777253 DOI: 10.3390/diagnostics12123111] [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: 11/14/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
Artificial intelligence (AI), a rousing advancement disrupting a wide spectrum of applications with remarkable betterment, has continued to gain momentum over the past decades. Within breast imaging, AI, especially machine learning and deep learning, honed with unlimited cross-data/case referencing, has found great utility encompassing four facets: screening and detection, diagnosis, disease monitoring, and data management as a whole. Over the years, breast cancer has been the apex of the cancer cumulative risk ranking for women across the six continents, existing in variegated forms and offering a complicated context in medical decisions. Realizing the ever-increasing demand for quality healthcare, contemporary AI has been envisioned to make great strides in clinical data management and perception, with the capability to detect indeterminate significance, predict prognostication, and correlate available data into a meaningful clinical endpoint. Here, the authors captured the review works over the past decades, focusing on AI in breast imaging, and systematized the included works into one usable document, which is termed an umbrella review. The present study aims to provide a panoramic view of how AI is poised to enhance breast imaging procedures. Evidence-based scientometric analysis was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guideline, resulting in 71 included review works. This study aims to synthesize, collate, and correlate the included review works, thereby identifying the patterns, trends, quality, and types of the included works, captured by the structured search strategy. The present study is intended to serve as a "one-stop center" synthesis and provide a holistic bird's eye view to readers, ranging from newcomers to existing researchers and relevant stakeholders, on the topic of interest.
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Panico C, Ferrara F, Woitek R, D’Angelo A, Di Paola V, Bufi E, Conti M, Palma S, Cicero SL, Cimino G, Belli P, Manfredi R. Staging Breast Cancer with MRI, the T. A Key Role in the Neoadjuvant Setting. Cancers (Basel) 2022; 14:cancers14235786. [PMID: 36497265 PMCID: PMC9739275 DOI: 10.3390/cancers14235786] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 11/15/2022] [Accepted: 11/17/2022] [Indexed: 11/27/2022] Open
Abstract
Breast cancer (BC) is the most common cancer among women worldwide. Neoadjuvant chemotherapy (NACT) indications have expanded from inoperable locally advanced to early-stage breast cancer. Achieving a pathological complete response (pCR) has been proven to be an excellent prognostic marker leading to better disease-free survival (DFS) and overall survival (OS). Although diagnostic accuracy of MRI has been shown repeatedly to be superior to conventional methods in assessing the extent of breast disease there are still controversies regarding the indication of MRI in this setting. We intended to review the complex literature concerning the tumor size in staging, response and surgical planning in patients with early breast cancer receiving NACT, in order to clarify the role of MRI. Morphological and functional MRI techniques are making headway in the assessment of the tumor size in the staging, residual tumor assessment and prediction of response. Radiomics and radiogenomics MRI applications in the setting of the prediction of response to NACT in breast cancer are continuously increasing. Tailored therapy strategies allow considerations of treatment de-escalation in excellent responders and avoiding or at least postponing breast surgery in selected patients.
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Affiliation(s)
- Camilla Panico
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Correspondence:
| | - Francesca Ferrara
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Ramona Woitek
- Medical Image Analysis and AI (MIAAI), Danube Private University, 3500 Krems, Austria
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, Cambridge CB2 0RE, UK
| | - Anna D’Angelo
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Valerio Di Paola
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Enida Bufi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Marco Conti
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Simone Palma
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Stefano Lo Cicero
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Giovanni Cimino
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Paolo Belli
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
| | - Riccardo Manfredi
- Department of Bioimaging, Radiation Oncology and Hematology, UOC of Radiologia, Fondazione Policlinico Universitario A. Gemelli IRCSS, Largo A. Gemelli 8, 00168 Rome, Italy
- Institute of Radiology, Catholic University of the Sacred Heart, Largo A. Gemelli 8, 00168 Rome, Italy
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Muacevic A, Adler JR. Mammographic and Ultrasonographic Imaging Analysis for Neoadjuvant Chemotherapy Evaluation: Volume Reduction Indexes That Correlate With Pathological Complete Response. Cureus 2022; 14:e29960. [PMID: 36225243 PMCID: PMC9534532 DOI: 10.7759/cureus.29960] [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] [Accepted: 10/05/2022] [Indexed: 12/03/2022] Open
Abstract
INTRODUCTION We aimed to evaluate volume reduction in digital mammography (DM) and ultrasound (US) for neoadjuvant chemotherapy (NAC) evaluation, with breast cancer-specific survival and pathological complete response (pCR) associations. METHODS This is a retrospective observational cohort study analyzing recorded images in 122 selected subjects out of which 569 patients presented with advanced breast cancers. Spearman's correlation and generalized estimating equations (GEE) compared volume reduction on DM and US between pCR and non-pCR after NAC with post-surgical anatomopathology. Cox regression and Kaplan-Meier curves analyzed associations between cancer-specific survival, pCR, and volume reductions. RESULTS A total of 34.4% (N=42) obtained pCR and 65.6% (N=80) did not. Minimum percentage indexes needed to correlate with pCR over time were, at least, 28.9% for DM (p=0.006) and 10.36% for US (p=0.046), with high specificity (US=98%, DM=93%) but low sensitivity (US=7%, DM=18%). Positive predictive values were 82% (DM) and 86% (US) and negative predictive values were 37% (DM) and 36% (US). Cox regression and Kaplan-Meier curves demonstrated associations of breast cancer-specific survival with pCR (Cox regression coefficient {B}=0.209, CI 95%=0.048-0.914, p=0.038). CONCLUSIONS At least 28.9% of volume reduction on DM and 10.36% of volume reduction on US are correlated with pCR. Furthermore, pCR was associated with breast cancer-specific survival after NAC in volumetric morphological imaging analysis.
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Mansour S, Soliman S, Kansakar A, Marey A, Hunold C, Hanafy MM. Strengths and challenges of the artificial intelligence in the assessment of dense breasts. BJR Open 2022; 4:20220018. [PMID: 38525169 PMCID: PMC10958665 DOI: 10.1259/bjro.20220018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 06/27/2022] [Accepted: 07/20/2022] [Indexed: 11/05/2022] Open
Abstract
Objectives High breast density is a risk factor for breast cancer and overlapping of glandular tissue can mask lesions thus lowering mammographic sensitivity. Also, dense breasts are more vulnerable to increase recall rate and false-positive results. New generations of artificial intelligence (AI) have been introduced to the realm of mammography. We aimed to assess the strengths and challenges of adopting artificial intelligence in reading mammograms of dense breasts. Methods This study included 6600 mammograms of dense patterns "c" and "d" and presented 4061 breast abnormalities. All the patients were subjected to full-field digital mammography, breast ultrasound, and their mammographic images were scanned by AI software (Lunit INSIGHT MMG). Results Diagnostic indices of the sono-mammography: a sensitivity of 98.71%, a specificity of 88.04%, a positive-predictive value of 80.16%, a negative-predictive value of 99.29%, and a diagnostic accuracy of 91.5%. AI-aided mammograms presented sensitivity of 88.29%, a specificity of 96.34%, a positive-predictive value of 92.2%, a negative-predictive value of 94.4%, and a diagnostic accuracy of 94.5% in its ability to read dense mammograms. Conclusion Dense breasts scanned with AI showed a notable reduction of mammographic misdiagnosis. Knowledge of such software challenges would enhance its application as a decision support tool to mammography in the diagnosis of cancer. Advances in knowledge Dense breast is challenging for radiologists and renders low sensitivity mammogram. Mammogram scanned by AI could be used to overcome such limitation, enhance the discrimination between benign and malignant breast abnormalities and the early detection of breast cancer.
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Affiliation(s)
| | - Somia Soliman
- Pathology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Abisha Kansakar
- Radiology, Kasr ElAiny Hospital, Cairo University, Cairo, Egypt
| | - Ahmed Marey
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
| | - Christiane Hunold
- FUJIFILM Medical Imaging and IT Solutions MEA, Dubai, United Arab Emirates
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Zhao R, Xing J, Gao J. Development and Validation of a Prediction Model for Positive Margins in Breast-Conserving Surgery. Front Oncol 2022; 12:875665. [PMID: 35646633 PMCID: PMC9133412 DOI: 10.3389/fonc.2022.875665] [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: 02/14/2022] [Accepted: 04/12/2022] [Indexed: 11/21/2022] Open
Abstract
Background The chances of second surgery due to positive margins in patients receiving breast-conversing surgery (BCS) were about 20-40%. This study aims to develop and validate a nomogram to predict the status of breast-conserving margins. Methods The database identified patients with core needle biopsy-proven ductal carcinoma in situ (DCIS) or invasive breast carcinoma who underwent BCS in Shanxi Bethune Hospital between January 1, 2015 and December 31, 2021 (n = 573). The patients were divided into two models: (1) The first model consists of 398 patients who underwent BCS between 2015 and 2019; (2) The validation model consists of 175 patients who underwent BCS between 2020 and 2021. The development of the nomogram was based on the findings of multivariate logistic regression analysis. Discrimination was assessed by computing the C-index. The Hosmer-Lemeshow goodness-of-fit test was used to validate the calibration performance. Results The final multivariate regression model was developed as a nomogram, including blood flow signals (OR = 2.88, p = 0.001), grade (OR = 2.46, p = 0.002), microcalcifications (OR = 2.39, p = 0.003), tumor size in ultrasound (OR = 2.12, p = 0.011) and cerbB-2 status (OR = 1.99, p = 0.042). C-indices were calculated of 0.71 (95% CI: 0.64-0.78) and 0.68 (95% CI: 0.59-0.78) for the modeling and the validation group, respectively. The calibration of the model was considered adequate in the validation group (p > 0.05). Conclusion We developed a nomogram that enables the estimation of the preoperative risk of positive BCS margins. Our nomogram provides a valuable tool for identifying high-risk patients who might have to undergo a wider excision.
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Affiliation(s)
- Rong Zhao
- Department of Breast Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Jun Xing
- Department of Breast Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Jinnan Gao
- Department of Breast Surgery, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
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Bruckmann NM, Morawitz J, Fendler WP, Ruckhäberle E, Bittner AK, Giesel FL, Herrmann K, Antoch G, Umutlu L, Kirchner J. A Role of PET/MR in Breast Cancer? Semin Nucl Med 2022; 52:611-618. [DOI: 10.1053/j.semnuclmed.2022.01.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 01/26/2022] [Indexed: 01/18/2023]
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Sebestyén A, Dankó T, Sztankovics D, Moldvai D, Raffay R, Cervi C, Krencz I, Zsiros V, Jeney A, Petővári G. The role of metabolic ecosystem in cancer progression — metabolic plasticity and mTOR hyperactivity in tumor tissues. Cancer Metastasis Rev 2022; 40:989-1033. [PMID: 35029792 PMCID: PMC8825419 DOI: 10.1007/s10555-021-10006-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 11/26/2021] [Indexed: 12/14/2022]
Abstract
Despite advancements in cancer management, tumor relapse and metastasis are associated with poor outcomes in many cancers. Over the past decade, oncogene-driven carcinogenesis, dysregulated cellular signaling networks, dynamic changes in the tissue microenvironment, epithelial-mesenchymal transitions, protein expression within regulatory pathways, and their part in tumor progression are described in several studies. However, the complexity of metabolic enzyme expression is considerably under evaluated. Alterations in cellular metabolism determine the individual phenotype and behavior of cells, which is a well-recognized hallmark of cancer progression, especially in the adaptation mechanisms underlying therapy resistance. In metabolic symbiosis, cells compete, communicate, and even feed each other, supervised by tumor cells. Metabolic reprogramming forms a unique fingerprint for each tumor tissue, depending on the cellular content and genetic, epigenetic, and microenvironmental alterations of the developing cancer. Based on its sensing and effector functions, the mechanistic target of rapamycin (mTOR) kinase is considered the master regulator of metabolic adaptation. Moreover, mTOR kinase hyperactivity is associated with poor prognosis in various tumor types. In situ metabolic phenotyping in recent studies highlights the importance of metabolic plasticity, mTOR hyperactivity, and their role in tumor progression. In this review, we update recent developments in metabolic phenotyping of the cancer ecosystem, metabolic symbiosis, and plasticity which could provide new research directions in tumor biology. In addition, we suggest pathomorphological and analytical studies relating to metabolic alterations, mTOR activity, and their associations which are necessary to improve understanding of tumor heterogeneity and expand the therapeutic management of cancer.
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Romeo V. Standardization of Quantitative DCE-MRI Parameters Measurement: An Urgent Need for Breast Cancer Imaging. Acad Radiol 2022; 29 Suppl 1:S87-S88. [PMID: 34991941 DOI: 10.1016/j.acra.2021.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2021] [Accepted: 12/05/2021] [Indexed: 11/18/2022]
Affiliation(s)
- Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini 5, Naples, 80138, Italy (V.R.).
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Klimonda Z, Karwat P, Dobruch-Sobczak K, Piotrzkowska-Wróblewska H, Litniewski J. Assessment of breast cancer response to neoadjuvant chemotherapy based on ultrasound backscattering envelope statistics. Med Phys 2021; 49:1047-1054. [PMID: 34954844 DOI: 10.1002/mp.15428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 12/16/2021] [Accepted: 12/16/2021] [Indexed: 12/24/2022] Open
Abstract
PURPOSE Neoadjuvant chemotherapy (NAC) is used in breast cancer before tumor surgery to reduce the size of the tumor and the risk of spreading. Monitoring the effects of NAC is important because in a number of cases the response to therapy is poor and requires a change in treatment. A new method that uses quantitative ultrasound to assess tumor response to NAC has been presented. The aim was to detect NAC unresponsive tumors at an early stage of treatment. METHODS The method assumes that ultrasound scattering is different for responsive and non-responsive tumors. The assessment of the NAC effects was based on the differences between the histograms of the ultrasound echo amplitude recorded from the tumor after each NAC dose and from the tissue phantom, estimated using the Kolmogorov-Smirnov statistics (KSS) and the symmetrical Kullback-Leibler divergence (KLD). After therapy, tumors were resected and histopathologically evaluated. The percentage of residual malignant cells (RMC) was determined and was the basis for assessing the tumor response. The data set included ultrasound data obtained from 37 tumors. The performance of the methods was assessed by means of the area under the receiver operating characteristic curve (AUC). RESULTS For responding tumors a decrease in the mean KLD and KSS values was observed after subsequent doses of NAC. In non-responding tumors the KLD was higher and did not change in subsequent NAC courses. Classification based on the KSS or KLD parameters allowed to detect tumors not responding to NAC after the first dose of the drug, with AUC equal 0.83±0.06 and 0.84±0.07 respectively. After the third dose, the AUC increased to 0.90±0.05 and 0.91±0.04 respectively. CONCLUSIONS The results indicate the potential usefulness of the proposed parameters in assessing the effectiveness of the NAC and early detection of non-responding cases. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Ziemowit Klimonda
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland
| | - Piotr Karwat
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland
| | - Katarzyna Dobruch-Sobczak
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland.,Radiology Department II, Maria Skłodowska-Curie National Research Institute of Oncology, Wawelska 15B, Warsaw, 02-034, Poland
| | - Hanna Piotrzkowska-Wróblewska
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland
| | - Jerzy Litniewski
- Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, Warsaw, 02-106, Poland
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Yan S, Peng H, Yu Q, Chen X, Liu Y, Zhu Y, Chen K, Wang P, Li Y, Zhang X, Meng W. Computer-aided classification of MRI for pathological complete response to neoadjuvant chemotherapy in breast cancer. Future Oncol 2021; 18:991-1001. [PMID: 34894719 DOI: 10.2217/fon-2021-1212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Background: To determine suitable optimal classifiers and examine the general applicability of computer-aided classification to compare the differences between a computer-aided system and radiologists in predicting pathological complete response (pCR) from patients with breast cancer receiving neoadjuvant chemotherapy. Methods: We analyzed a total of 455 masses and used the U-Net network and ResNet to execute MRI segmentation and pCR classification. The diagnostic performance of radiologists, the computer-aided system and a combination of radiologists and computer-aided system were compared using receiver operating characteristic curve analysis. Results: The combination of radiologists and computer-aided system had the best performance for predicting pCR with an area under the curve (AUC) value of 0.899, significantly higher than that of radiologists alone (AUC: 0.700) and computer-aided system alone (AUC: 0.835). Conclusion: An automated classification system is feasible to predict the pCR to neoadjuvant chemotherapy in patients with breast cancer and can complement MRI.
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Affiliation(s)
- Shaolei Yan
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Haiyong Peng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Qiujie Yu
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Xiaodan Chen
- Department of Computer Technology, Harbin Institute of Technology University, 92 West Street, Harbin, Heilongjiang, 150000, China
| | - Yue Liu
- Department of Radiology, Dongzhimen Hospital, Beijing University of Chinese Medicine, No. 5, Haiyuncang, Dongcheng District, Beijing, 100700, China
| | - Ye Zhu
- Department of Obstetrics & Gynecology, Peking University People's Hospital, No. 11 Xizhimen South Street, Xicheng District, Beijing, 100044, China
| | - Kaige Chen
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Ping Wang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Yujiao Li
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Xiushi Zhang
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
| | - Wei Meng
- Radiology Department, Harbin Medical University, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin, Heilongjiang, 150081, China
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