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Muñoz-Lerma A, Sánchez-Sánchez R, Ruiz-Vozmediano J, Yebras Cano T, González-Jiménez A, Jurado-Fasoli L. Effect of a multimodal intervention in breast Cancer patients undergoing neoadjuvant therapy: A study protocol of the multimodal project. Contemp Clin Trials 2024; 143:107598. [PMID: 38838986 DOI: 10.1016/j.cct.2024.107598] [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: 02/12/2024] [Revised: 05/28/2024] [Accepted: 06/01/2024] [Indexed: 06/07/2024]
Abstract
BACKGROUND AND AIMS To determine the effect of a multimodal intervention (nutritional behavior change and physical exercise) on quality of life, chemotherapy response rate and tolerance, histopathological level of the tumor, body composition, and biochemical parameters, in patients diagnosed with breast cancer during neoadjuvant chemotherapy treatment, and to compare them with the control group. METHODS Anticipated 80 patients diagnosed with breast cancer aged 18-70 years will be recruited for this randomized, unblinded clinical trial based on a nutritional behavior change and physical exercise in patients during the approximately 6 months in which the patient receives neoadjuvant treatment. Participants will be randomly allocated (1:1) to one of two groups (intervention or control). Primary and secondary outcomes will be assessed before the beginning and after the neoadjuvant treatment (before surgery). The primary outcome is quality of life, whereas secondary outcomes include chemotherapy response rate and tolerance, histopathological level of the tumor and body composition (i.e., visceral adipose tissue activity, bone, lean and fat masses). We will analyze blood parameters (i.e., biochemical, inflammatory, and tumor markers) as exploratory outcomes. CONCLUSION This study will address the influence of a practical and viable multimodal intervention (i.e., nutritional behavior change and physical exercise) on breast cancer patients undergoing neoadjuvant chemotherapy. Given the practical viability of the intervention in real-world settings, our study holds promise for significant scientific and clinical implications.
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Affiliation(s)
- Amelia Muñoz-Lerma
- Servicio de Oncología Médica, Hospital Universitario Virgen de las Nieves, 18004 Granada, Spain.
| | - Rocío Sánchez-Sánchez
- Servicio de Medicina Nuclear, Hospital Universitario Virgen de las Nieves, 18004 Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain
| | - Julia Ruiz-Vozmediano
- Servicio de Oncología Médica, Hospital Universitario Virgen de las Nieves, 18004 Granada, Spain; Instituto de Investigación Biosanitaria ibs.GRANADA, 18012 Granada, Spain; Centro de Oncología Integrativa Onconature, 18418 Granada, Spain
| | | | | | - Lucas Jurado-Fasoli
- Department of Physiology, Faculty of Medicine, University of Granada, 18071 Granada, Andalucía, Spain; Department of Physical Education and Sports, Faculty of Sports Science, Sport and Health University Research Institute (iMUDS), University of Granada, Carretera de Alfacar s/n, 18071 Granada, Spain
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Vaz SC, Woll JPP, Cardoso F, Groheux D, Cook GJR, Ulaner GA, Jacene H, Rubio IT, Schoones JW, Peeters MJV, Poortmans P, Mann RM, Graff SL, Dibble EH, de Geus-Oei LF. Joint EANM-SNMMI guideline on the role of 2-[ 18F]FDG PET/CT in no special type breast cancer : (endorsed by the ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA). Eur J Nucl Med Mol Imaging 2024; 51:2706-2732. [PMID: 38740576 PMCID: PMC11224102 DOI: 10.1007/s00259-024-06696-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 03/20/2024] [Indexed: 05/16/2024]
Abstract
INTRODUCTION There is much literature about the role of 2-[18F]FDG PET/CT in patients with breast cancer (BC). However, there exists no international guideline with involvement of the nuclear medicine societies about this subject. PURPOSE To provide an organized, international, state-of-the-art, and multidisciplinary guideline, led by experts of two nuclear medicine societies (EANM and SNMMI) and representation of important societies in the field of BC (ACR, ESSO, ESTRO, EUSOBI/ESR, and EUSOMA). METHODS Literature review and expert discussion were performed with the aim of collecting updated information regarding the role of 2-[18F]FDG PET/CT in patients with no special type (NST) BC and summarizing its indications according to scientific evidence. Recommendations were scored according to the National Institute for Health and Care Excellence (NICE) criteria. RESULTS Quantitative PET features (SUV, MTV, TLG) are valuable prognostic parameters. In baseline staging, 2-[18F]FDG PET/CT plays a role from stage IIB through stage IV. When assessing response to therapy, 2-[18F]FDG PET/CT should be performed on certified scanners, and reported either according to PERCIST, EORTC PET, or EANM immunotherapy response criteria, as appropriate. 2-[18F]FDG PET/CT may be useful to assess early metabolic response, particularly in non-metastatic triple-negative and HER2+ tumours. 2-[18F]FDG PET/CT is useful to detect the site and extent of recurrence when conventional imaging methods are equivocal and when there is clinical and/or laboratorial suspicion of relapse. Recent developments are promising. CONCLUSION 2-[18F]FDG PET/CT is extremely useful in BC management, as supported by extensive evidence of its utility compared to other imaging modalities in several clinical scenarios.
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Affiliation(s)
- Sofia C Vaz
- Nuclear Medicine-Radiopharmacology, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal.
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
| | | | - Fatima Cardoso
- Breast Unit, Champalimaud Clinical Center, Champalimaud Foundation, Lisbon, Portugal
| | - David Groheux
- Nuclear Medicine Department, Saint-Louis Hospital, Paris, France
- University Paris-Diderot, INSERM U976, Paris, France
- Centre d'Imagerie Radio-Isotopique (CIRI), La Rochelle, France
| | - Gary J R Cook
- Department of Cancer Imaging, King's College London, London, UK
- King's College London and Guy's & St Thomas' PET Centre, London, UK
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Gary A Ulaner
- Molecular Imaging and Therapy, Hoag Family Cancer Institute, Newport Beach, CA, USA
- University of Southern California, Los Angeles, CA, USA
| | - Heather Jacene
- Dana-Farber Cancer Institute/Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Isabel T Rubio
- Breast Surgical Oncology, Clinica Universidad de Navarra, Madrid, Cancer Center Clinica Universidad de Navarra, Navarra, Spain
| | - Jan W Schoones
- Directorate of Research Policy, Leiden University Medical Center, Leiden, The Netherlands
| | - Marie-Jeanne Vrancken Peeters
- Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Surgery, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Philip Poortmans
- Department of Radiation Oncology, Iridium Netwerk, Antwerp, Belgium
- University of Antwerp, Wilrijk, Antwerp, Belgium
| | - Ritse M Mann
- Radiology Department, RadboudUMC, Nijmegen, The Netherlands
| | - Stephanie L Graff
- Lifespan Cancer Institute, Providence, Rhode Island, USA
- Legorreta Cancer Center at Brown University, Providence, Rhode Island, USA
| | - Elizabeth H Dibble
- Department of Diagnostic Imaging, The Warren Alpert Medical School of Brown University, Providence, Rhode Island, USA.
| | - Lioe-Fee de Geus-Oei
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
- Biomedical Photonic Imaging Group, University of Twente, Enschede, The Netherlands.
- Department of Radiation Science & Technology, Technical University of Delft, Delft, The Netherlands.
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3
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Hongwei S, Xinzhong H, Huiqin X, Shuqin X, Ruonan W, Li L, Jianzhong C, Sijin L. Standard deviation of CT radiomic features among malignancies in each individual: prognostic ability in lung cancer patients. J Cancer Res Clin Oncol 2023; 149:7165-7173. [PMID: 36884114 DOI: 10.1007/s00432-023-04649-7] [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: 10/31/2022] [Accepted: 02/12/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE It was reported that individual heterogeneity among malignancies (IHAM) might correlate well to the prognosis of lung cancer; however, seldom radiomic study is on this field. Standard deviation (SD) in statistics could scale average amount of variability of a variable; therefore, we used SD of CT feature (FeatureSD) among primary tumor and malignant lymph nodes (LNs) in an individual to represent IHAM, and its prognostic ability was explored. METHODS The enrolled patients who had accepted PET/CT scans were selected from our previous study (ClinicalTrials.gov, NCT03648151). The patients had primary tumor and at least one LN, and standardized uptake value of LN higher than 2.0 and 2.5 were enrolled as the cohort 1 (n = 94) and 2 (n = 88), respectively. FeatureSD from the combined or thin-section CT were calculated among primary tumor and malignant LNs in each patient, and were separately selected by the survival XGBoost method. Finally, their prognostic ability was compared to the significant patient characteristics identified by the Cox regression. RESULTS In the univariate and multi-variate Cox analysis, surgery, target therapy, and TNM stage were significantly against OS in the both cohorts. In the survival XGBoost analysis of the thin-section CT dataset, none FeatureSD could be repeatably ranked on the top list of the both cohorts. For the combined CT dataset, only one FeatureSD ranked in the top three of both cohorts, but the three significant factors in the Cox regression were not even on the list. Both in the cohort 1 and 2, C-index of the model composed of the three factors could be improved by integrating the continuous FeatureSD; furthermore, that of each factor was obviously lower than FeatureSD. CONCLUSION Standard deviation of CT features among malignant foci within an individual was a powerful prognostic factor in vivo for lung cancer patients.
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Affiliation(s)
- Si Hongwei
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China.
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China.
| | - Hao Xinzhong
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Xu Huiqin
- Department of Medical imaging, Shenzhen Second People's Hospital/the First Affiliated Hospital of Shenzhen University Health Science Center, 518035, Shenzhen, China
| | - Xue Shuqin
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui Province, China
| | - Wang Ruonan
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Li Li
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Cao Jianzhong
- Department of Radiation Oncology, The Cancer Hospital of Shanxi Province, Taiyuan, 030013, Shanxi Province, China
| | - Li Sijin
- Department of Nuclear Medicine, The First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
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Morawitz J, Sigl B, Rubbert C, Bruckmann NM, Dietzel F, Häberle LJ, Ting S, Mohrmann S, Ruckhäberle E, Bittner AK, Hoffmann O, Baltzer P, Kapetas P, Helbich T, Clauser P, Fendler WP, Rischpler C, Herrmann K, Schaarschmidt BM, Stang A, Umutlu L, Antoch G, Caspers J, Kirchner J. Clinical Decision Support for Axillary Lymph Node Staging in Newly Diagnosed Breast Cancer Patients Based on 18F-FDG PET/MRI and Machine Learning. J Nucl Med 2023; 64:304-311. [PMID: 36137756 PMCID: PMC9902847 DOI: 10.2967/jnumed.122.264138] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 08/19/2022] [Accepted: 08/19/2022] [Indexed: 02/04/2023] Open
Abstract
In addition to its high prognostic value, the involvement of axillary lymph nodes in breast cancer patients also plays an important role in therapy planning. Therefore, an imaging modality that can determine nodal status with high accuracy in patients with primary breast cancer is desirable. Our purpose was to investigate whether, in newly diagnosed breast cancer patients, machine-learning prediction models based on simple assessable imaging features on MRI or PET/MRI are able to determine nodal status with performance comparable to that of experienced radiologists; whether such models can be adjusted to achieve low rates of false-negatives such that invasive procedures might potentially be omitted; and whether a clinical framework for decision support based on simple imaging features can be derived from these models. Methods: Between August 2017 and September 2020, 303 participants from 3 centers prospectively underwent dedicated whole-body 18F-FDG PET/MRI. Imaging datasets were evaluated for axillary lymph node metastases based on morphologic and metabolic features. Predictive models were developed for MRI and PET/MRI separately using random forest classifiers on data from 2 centers and were tested on data from the third center. Results: The diagnostic accuracy for MRI features was 87.5% both for radiologists and for the machine-learning algorithm. For PET/MRI, the diagnostic accuracy was 89.3% for the radiologists and 91.2% for the machine-learning algorithm, with no significant differences in diagnostic performance between radiologists and the machine-learning algorithm for MRI (P = 0.671) or PET/MRI (P = 0.683). The most important lymph node feature was tracer uptake, followed by lymph node size. With an adjusted threshold, a sensitivity of 96.2% was achieved by the random forest classifier, whereas specificity, positive predictive value, negative predictive value, and accuracy were 68.2%, 78.1%, 93.8%, and 83.3%, respectively. A decision tree based on 3 simple imaging features could be established for MRI and PET/MRI. Conclusion: Applying a high-sensitivity threshold to the random forest results might potentially avoid invasive procedures such as sentinel lymph node biopsy in 68.2% of the patients.
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Affiliation(s)
- Janna Morawitz
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany;
| | - Benjamin Sigl
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General Radiology, Medical University of Vienna, Vienna, Austria
| | - Christian Rubbert
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany
| | - Nils-Martin Bruckmann
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany
| | - Frederic Dietzel
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany
| | - Lena J. Häberle
- Institute of Pathology, Medical Faculty, Heinrich Heine University and University Hospital Duesseldorf, Duesseldorf, Germany
| | - Saskia Ting
- Institute of Pathology, University Hospital Essen, West German Cancer Center, University of Duisburg–Essen and the German Cancer Consortium (DKTK), Essen, Germany
| | - Svjetlana Mohrmann
- Department of Gynecology, University of Duesseldorf, Medical Faculty, Duesseldorf, Germany
| | - Eugen Ruckhäberle
- Department of Gynecology, University of Duesseldorf, Medical Faculty, Duesseldorf, Germany
| | - Ann-Kathrin Bittner
- Department of Gynecology and Obstetrics, University Hospital Essen, University of Duisburg–Essen, Essen, Germany
| | - Oliver Hoffmann
- Department of Gynecology and Obstetrics, University Hospital Essen, University of Duisburg–Essen, Essen, Germany
| | - Pascal Baltzer
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General Radiology, Medical University of Vienna, Vienna, Austria
| | - Panagiotis Kapetas
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General Radiology, Medical University of Vienna, Vienna, Austria
| | - Thomas Helbich
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General Radiology, Medical University of Vienna, Vienna, Austria
| | - Paola Clauser
- Department of Biomedical Imaging and Image-Guided Therapy, Division of General Radiology, Medical University of Vienna, Vienna, Austria
| | - Wolfgang P. Fendler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg–Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Christoph Rischpler
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg–Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, University of Duisburg–Essen and German Cancer Consortium (DKTK), Essen, Germany
| | - Benedikt M. Schaarschmidt
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg–Essen, Essen, Germany; and
| | - Andreas Stang
- Institute of Medical Informatics, Biometry, and Epidemiology, Essen University Medical Center, Essen, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, University of Duisburg–Essen, Essen, Germany; and
| | - Gerald Antoch
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany
| | - Julian Caspers
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany
| | - Julian Kirchner
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Duesseldorf, Duesseldorf, Germany
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Prognostic Value of Axillary Lymph Node Texture Parameters Measured by Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography in Locally Advanced Breast Cancer with Neoadjuvant Chemotherapy. Diagnostics (Basel) 2022; 12:diagnostics12102285. [PMID: 36291974 PMCID: PMC9600297 DOI: 10.3390/diagnostics12102285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/14/2022] [Accepted: 09/20/2022] [Indexed: 11/16/2022] Open
Abstract
Background: This study investigated the prognostic value of axillary lymph node (ALN) heterogeneity texture features through 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) in patients with locally advanced breast cancer (LABC). Methods: We retrospectively analyzed 158 LABC patients with FDG-avid, pathology-proven, metastatic ALN who underwent neoadjuvant chemotherapy (NAC) and curative surgery. Tumor and ALN texture parameters were extracted from pretreatment 18F-FDG PET/CT using Chang-Gung Image Texture Analysis software. The least absolute shrinkage and selection operator regression was performed to select the most significant predictive texture parameters. The predictive impact of texture parameters was evaluated for both progression-free survival and pathologic NAC response. Results: The median follow-up period of 36.8 months and progression of disease (PD) was observed in 36 patients. In the univariate analysis, ALN textures (minimum standardized uptake value (SUV) (p = 0.026), SUV skewness (p = 0.038), SUV bias-corrected Kurtosis (p = 0.034), total lesion glycolysis (p = 0.011)), tumor textures (low-intensity size zone emphasis (p = 0.045), minimum SUV (p = 0.047), and homogeneity (p = 0.041)) were significant texture predictors. On the Cox regression analysis, ALN SUV skewness was an independent texture predictor of PD (p = 0.016, hazard ratio 2.3, 95% confidence interval 1.16–4.58). Conclusions: ALN texture feature from pretreatment 18F-FDG PET/CT is useful for the prediction of LABC progression.
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Chen K, Yin G, Xu W. Predictive Value of 18F-FDG PET/CT-Based Radiomics Model for Occult Axillary Lymph Node Metastasis in Clinically Node-Negative Breast Cancer. Diagnostics (Basel) 2022; 12:diagnostics12040997. [PMID: 35454045 PMCID: PMC9030613 DOI: 10.3390/diagnostics12040997] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Revised: 04/08/2022] [Accepted: 04/13/2022] [Indexed: 01/26/2023] Open
Abstract
Background: To develop and validate a radiomics model based on 18F-FDG PET/CT images to preoperatively predict occult axillary lymph node (ALN) metastases in patients with invasive ductal breast cancer (IDC) with clinically node-negative (cN0); Methods: A total of 180 patients (mean age, 55 years; range, 31–82 years) with pathologically proven IDC and a preoperative 18F-FDG PET/CT scan from January 2013 to January 2021 were included in this retrospective study. According to the intraoperative pathological results of ALN, we divided patients into the true-negative group and ALN occult metastasis group. Radiomics features were extracted from PET/CT images using Pyradiomics implemented in Python, t-tests, and LASSO were used to screen the feature, and the random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and k-nearest neighbor (KNN) were used to build the prediction models. The best-performing model was further tested by the permutation test; Results: Among the four models, RF had the best prediction results, the AUC range of RF was 0.661–0.929 (mean AUC, 0.817), and the accuracy range was 65.3–93.9% (mean accuracy, 81.2%). The p-values of the permutation tests for the RF model with maximum and minimum accuracy were less than 0.01; Conclusions: The developed RF model was able to predict occult ALN metastases in IDC patients based on preoperative 18F-FDG PET/CT radiomic features.
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Affiliation(s)
- Kun Chen
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
| | - Guotao Yin
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
| | - Wengui Xu
- Department of Molecular Imaging and Nuclear Medicine, Tianjin Medical University Cancer Institute and Hospital, Huanhuxi Road, Hexi Distinct, Tianjin 300060, China; (K.C.); (G.Y.)
- National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for China, Tianjin 300060, China
- Correspondence: or
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Song BI. A machine learning-based radiomics model for the prediction of axillary lymph-node metastasis in breast cancer. Breast Cancer 2021; 28:664-671. [PMID: 33454875 DOI: 10.1007/s12282-020-01202-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/02/2020] [Indexed: 02/08/2023]
Abstract
OBJECTIVE The aim of this study was to develop and validate machine learning-based radiomics model for predicting axillary lymph-node (ALN) metastasis in invasive ductal breast cancer (IDC) using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS A total of 100 consecutive IDC patients who underwent surgical resection of primary tumor with sentinel lymph-node biopsy and/or ALN dissection without any neoadjuvant treatment were analyzed. Volume of interests (VOIs) were drawn more than 2.5 of standardized uptake value in the primary tumor on the PET scan using 3D slicer. Pyradiomics package was used for the extraction of texture features in python. The radiomics prediction model for ALN metastasis was developed in 75 patients of the training cohort and validated in 25 patients of the test cohort. XGBoost algorithm was utilized to select features and build radiomics model. The sensitivity, specificity, and accuracy of the predictive model were calculated. RESULTS ALN metastasis was found in 43 patients (43%). The sensitivity, specificity, and accuracy of F-18 FDG PET/CT for the diagnosis of ALN metastasis in the entire patients were 55.8%, 93%, and 77%, respectively. The radiomics model for the prediction of ALN metastasis was successfully developed. The sensitivity, specificity, and accuracy of the radiomics model for the prediction of ALN metastasis in the test cohorts were 90.9%, 71.4%, and 80%, respectively. CONCLUSION The machine learning-based radiomics model showed good sensitivity for the prediction of ALN metastasis and could assist the preoperative individualized prediction of ALN status in patients with IDC.
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Affiliation(s)
- Bong-Il Song
- Department of Nuclear Medicine, Keimyung University Dongsan Hospital, Keimyung University School of Medicine, 1095 Dalgubeol-daero, Dalseo-gu, Daegu, 42601, Republic of Korea.
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Jacene HA, DiPiro PJ, Bellon J, Hu J, Cheng SC, Warren L, Schlosnagle E, Nakhlis F, Rosenbluth JM, Yeh E, Overmoyer B. Discrepancy between FDG-PET/CT and contrast-enhanced CT in the staging of patients with inflammatory breast cancer: implications for treatment planning. Breast Cancer Res Treat 2020; 181:383-390. [PMID: 32318957 DOI: 10.1007/s10549-020-05631-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Accepted: 04/07/2020] [Indexed: 01/10/2023]
Abstract
PURPOSE Optimizing treatment strategies for patients with inflammatory breast cancer (IBC) relies on accurate initial staging. This study compared contrast-enhanced computed tomography (ce-CT) and FDG-PET/CT for initial staging of IBC to determine the frequency of discordance between the two imaging modalities and potential impact on management. METHODS 81 patients with IBC underwent FDG-PET/CT and ce-CT prior to starting treatment. FDG-PET/CT and ce-CT scans were independently reviewed for locoregional and distant metastases and findings recorded by anatomic site as negative, equivocal, or positive for breast cancer involvement. Each paired ce-CT and FDG-PET/CT case was classified as concordant or discordant for findings. Discordant findings were subclassified as (a) related to the presence or absence of distant metastases; (b) affecting the locoregional radiation therapy plan; or (c) due to incidental findings not related to IBC. RESULTS There were 47 discordant findings between ce-CT and FDG-PET/CT in 41 of 81 patients (50.6%). Thirty (63.8%) were related to the presence or absence of distant metastases; most commonly disease detection on FDG-PET/CT but not ce-CT (n = 12). FDG-PET/CT suggested alterations of the locoregional radiation therapy plan designed by CT alone in 15 patients. FDG-PET/CT correctly characterized 5 of 7 findings equivocal for metastatic IBC on ce-CT. CONCLUSIONS This study demonstrates differences between ce-CT and FDG-PET/CT for initial staging of IBC and how these differences potentially affect patient management. Preliminary data suggest that FDG-PET/CT may be the imaging modality of choice for initial staging of IBC. Prospective trials testing initial staging with FDG-PET/CT versus important clinical end-points in IBC are warranted.
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Affiliation(s)
- Heather A Jacene
- Department of Imaging and Radiology, Dana-Farber Cancer Institute/Brigham and Women's Hospital, 450 Brookline Avenue, Boston, MA, 02215, USA.
| | - Pamela J DiPiro
- Department of Imaging and Radiology, Dana-Farber Cancer Institute/Brigham and Women's Hospital, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Jennifer Bellon
- Department of Radiation Oncology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Jiani Hu
- Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Su-Chun Cheng
- Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Laura Warren
- Department of Radiation Oncology, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Emily Schlosnagle
- Inflammatory Breast Cancer Program, Medical Oncology, Susan F. Smith Center for Women's Cancers, Dana-Farber Cancer Institute, Boston, USA
| | - Faina Nakhlis
- Inflammatory Breast Cancer Program, Medical Oncology, Susan F. Smith Center for Women's Cancers, Dana-Farber Cancer Institute, Boston, USA.,Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Jennifer M Rosenbluth
- Inflammatory Breast Cancer Program, Medical Oncology, Susan F. Smith Center for Women's Cancers, Dana-Farber Cancer Institute, Boston, USA
| | - Eren Yeh
- Department of Imaging and Radiology, Dana-Farber Cancer Institute/Brigham and Women's Hospital, 450 Brookline Avenue, Boston, MA, 02215, USA
| | - Beth Overmoyer
- Inflammatory Breast Cancer Program, Medical Oncology, Susan F. Smith Center for Women's Cancers, Dana-Farber Cancer Institute, Boston, USA
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Jacene HA, Youn T, DiPiro PJ, Hu J, Cheng SC, Franchetti Y, Shah H, Bellon JR, Warren L, Schlosnagle E, Nakhlis F, Rosenbluth J, Yeh E, Overmoyer B. Metabolic Characterization of Inflammatory Breast Cancer With Baseline FDG-PET/CT: Relationship With Pathologic Response After Neoadjuvant Chemotherapy, Receptor Status, and Tumor Grade. Clin Breast Cancer 2019; 19:146-155. [DOI: 10.1016/j.clbc.2018.11.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2018] [Revised: 10/15/2018] [Accepted: 11/19/2018] [Indexed: 10/27/2022]
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Choi HJ, Lee KH, Kim NH, Kim JH, Hyun IY, Ryu JS. The usefulness of combined axial and coronal computed tomography for the evaluation of metastatic supraclavicular lymph nodes. Clin Imaging 2015; 39:608-12. [PMID: 25940644 DOI: 10.1016/j.clinimag.2015.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2015] [Accepted: 04/10/2015] [Indexed: 12/18/2022]
Abstract
The purpose is to assess the value of adding coronal images for the identification of metastatic supraclavicular lymph nodes (LNs). Two radiologists reviewed axial images and combined axial and coronal images using thoracic computed tomography (CT) of 386 patients whose maximum standardized uptake value measured in a supraclavicular LN was ≥2.0 on a positron emission tomography. We compared sensitivity and agreement between readers before and after the addition of coronal images. For combined images, agreement was almost perfect (κ=0.982), and sensitivity was significantly higher (90.4%, P<.001). Interpreting both axial and coronal images improves the diagnostic accuracy for supraclavicular metastasis.
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Affiliation(s)
- Hyun Jin Choi
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Inhang-ro 27, Jung-gu, Incheon, Korea
| | - Kyung Hee Lee
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Inhang-ro 27, Jung-gu, Incheon, Korea
| | - Na Hee Kim
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Inhang-ro 27, Jung-gu, Incheon, Korea
| | - Jun Ho Kim
- Department of Radiology, Inha University Hospital, Inha University School of Medicine, Inhang-ro 27, Jung-gu, Incheon, Korea.
| | - In Young Hyun
- Department of Nuclear Medicine, Inha University Hospital, Inha University School of Medicine, Inhang-ro 27, Jung-gu, Incheon, Korea
| | - Jeong-Seon Ryu
- Department of Internal Medicine, Inha University Hospital, Inha University School of Medicine, Inhang-ro 27, Jung-gu, Incheon, Korea
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11
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García Vicente AM, Soriano Castrejón Á, Cruz Mora MÁ, González Ageitos A, Muñoz Sánchez MDM, León Martín A, Espinosa Aunión R, Relea Calatayud F, Muñoz Madero V, Chacón López-Muñiz I, Cordero García JM, Jiménez Londoño GA. Semi-quantitative lymph node assessment of 18F-FDG PET/CT in locally advanced breast cancer: correlation with biological prognostic factors. Eur J Nucl Med Mol Imaging 2012; 40:72-9. [DOI: 10.1007/s00259-012-2244-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2012] [Accepted: 08/23/2012] [Indexed: 10/27/2022]
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