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Nguyen DL, Greenwood HI, Rahbar H, Grimm LJ. Evolving Treatment Paradigms for Low-Risk Ductal Carcinoma In Situ: Imaging Needs. AJR Am J Roentgenol 2024; 222:e2330503. [PMID: 38090808 DOI: 10.2214/ajr.23.30503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Ductal carcinoma in situ (DCIS) is a nonobligate precursor to invasive cancer that classically presents as asymptomatic calcifications on screening mammography. The increase in DCIS diagnoses with organized screening programs has raised concerns about overdiagnosis, while a patientcentric push for more personalized care has increased awareness about DCIS overtreatment. The standard of care for most new DCIS diagnoses is surgical excision, but nonsurgical management via active monitoring is gaining attention, and multiple clinical trials are ongoing. Imaging, along with demographic and pathologic information, is a critical component of active monitoring efforts. Commonly used imaging modalities including mammography, ultrasound, and MRI, as well as newer modalities such as contrast-enhanced mammography and dedicated breast PET, can provide prognostic information to risk stratify patients for DCIS active monitoring eligibility. Furthermore, radiologists will be responsible for closely surveilling patients on active monitoring and identifying if invasive progression occurs. Active monitoring is a paradigm shift for DCIS care, but the success or failure will rely heavily on the interpretations and guidance of radiologists.
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Affiliation(s)
- Derek L Nguyen
- Department of Diagnostic Radiology, Duke University School of Medicine, Box 3808, Durham, NC 27710
| | - Heather I Greenwood
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA
| | - Habib Rahbar
- Department of Radiology, University of Washington, Seattle, WA
- Fred Hutchinson Cancer Center, Seattle, WA
| | - Lars J Grimm
- Department of Diagnostic Radiology, Duke University School of Medicine, Box 3808, Durham, NC 27710
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Kim MY, Yoen H, Ji H, Park SJ, Kim SM, Han W, Cho N. Ultrafast MRI and T1 and T2 Radiomics for Predicting Invasive Components in Ductal Carcinoma in Situ Diagnosed With Percutaneous Needle Biopsy. Korean J Radiol 2023; 24:1190-1199. [PMID: 38016679 DOI: 10.3348/kjr.2023.0208] [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: 08/18/2022] [Revised: 07/26/2023] [Accepted: 09/05/2023] [Indexed: 11/30/2023] Open
Abstract
OBJECTIVE This study aimed to investigate the feasibility of ultrafast magnetic resonance imaging (MRI) and radiomic features derived from breast MRI for predicting the upstaging of ductal carcinoma in situ (DCIS) diagnosed using percutaneous needle biopsy. MATERIALS AND METHODS Between August 2018 and June 2020, 95 patients with 98 DCIS lesions who underwent preoperative breast MRI, including an ultrafast sequence, and subsequent surgery were included. Four ultrafast MRI parameters were analyzed: time-to-enhancement, maximum slope (MS), area under the curve for 60 s after enhancement, and time-to-peak enhancement. One hundred and seven radiomic features were extracted for the whole tumor on the first post-contrast T1WI and T2WI using PyRadiomics. Clinicopathological characteristics, ultrafast MRI findings, and radiomic features were compared between the pure DCIS and DCIS with invasion groups. Prediction models, incorporating clinicopathological, ultrafast MRI, and radiomic features, were developed. Receiver operating characteristic curve analysis and area under the curve (AUC) were used to evaluate model performance in distinguishing between the two groups using leave-one-out cross-validation. RESULTS Thirty-six of the 98 lesions (36.7%) were confirmed to have invasive components after surgery. Compared to the pure DCIS group, the DCIS with invasion group had a higher nuclear grade (P < 0.001), larger mean lesion size (P = 0.038), larger mean MS (P = 0.002), and different radiomic-related characteristics, including a more extensive tumor volume; higher maximum gray-level intensity; coarser, more complex, and heterogeneous texture; and a greater concentration of high gray-level intensity. No significant differences in AUCs were found between the model incorporating nuclear grade and lesion size (0.687) and the models integrating additional ultrafast MRI and radiomic features (0.680-0.732). CONCLUSION High nuclear grade, larger lesion size, larger MS, and multiple radiomic features were associated with DCIS upstaging. However, the addition of MS and radiomic features to the prediction model did not significantly improve the prediction performance.
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Affiliation(s)
- Min Young 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
| | - Heera Yoen
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Hye Ji
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang Joon Park
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
- MEDICALIP Co. Ltd., Seoul, Republic of Korea
| | - Sun Mi Kim
- Department of Radiology, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Republic of Korea
| | - Wonshik Han
- Department of Surgery and Cancer Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, 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.
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Miceli R, Mercado CL, Hernandez O, Chhor C. Active Surveillance for Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ. JOURNAL OF BREAST IMAGING 2023; 5:396-415. [PMID: 38416903 DOI: 10.1093/jbi/wbad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Indexed: 03/01/2024]
Abstract
Atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are relatively common breast lesions on the same spectrum of disease. Atypical ductal hyperblasia is a nonmalignant, high-risk lesion, and DCIS is a noninvasive malignancy. While a benefit of screening mammography is early cancer detection, it also leads to increased biopsy diagnosis of noninvasive lesions. Previously, treatment guidelines for both entities included surgical excision because of the risk of upgrade to invasive cancer after surgery and risk of progression to invasive cancer for DCIS. However, this universal management approach is not optimal for all patients because most lesions are not upgraded after surgery. Furthermore, some DCIS lesions do not progress to clinically significant invasive cancer. Overtreatment of high-risk lesions and DCIS is considered a burden on patients and clinicians and is a strain on the health care system. Extensive research has identified many potential histologic, clinical, and imaging factors that may predict ADH and DCIS upgrade and thereby help clinicians select which patients should undergo surgery and which may be appropriate for active surveillance (AS) with imaging. Additionally, multiple clinical trials are currently underway to evaluate whether AS for DCIS is feasible for a select group of patients. Recent advances in MRI, artificial intelligence, and molecular markers may also have an important role to play in stratifying patients and delineating best management guidelines. This review article discusses the available evidence regarding the feasibility and limitations of AS for ADH and DCIS, as well as recent advances in patient risk stratification.
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Affiliation(s)
- Rachel Miceli
- NYU Langone Health, Department of Radiology, New York, NY, USA
| | | | | | - Chloe Chhor
- NYU Langone Health, Department of Radiology, New York, NY, USA
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Ohashi A, Kataoka M, Iima M, Honda M, Ota R, Urushibata Y, Nickel MD, Toi M, Zackrisson S, Nakamoto Y. Comparison of Ultrafast Dynamic Contrast-Enhanced (DCE) MRI with Conventional DCE MRI in the Morphological Assessment of Malignant Breast Lesions. Diagnostics (Basel) 2023; 13:diagnostics13061105. [PMID: 36980417 PMCID: PMC10046990 DOI: 10.3390/diagnostics13061105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 03/17/2023] Open
Abstract
Ultrafast (UF) dynamic contrast-enhanced (DCE)-MRI offers the potential for a faster and, therefore, less expensive examination of breast lesions; however, there are no reports that have evaluated whether UF DCE-MRI can be used the same as conventional DCE-MRI in the reading of morphological information. This study evaluated the agreement in morphological information obtained from malignant breast mass lesions between UF DCE-MRI and conventional DCE-MRI. UF DCE-MRI data were obtained over the first 60 s post-contrast injection, followed by the conventional DCE images. Two readers evaluated the size and morphology of the lesions in the final phase of the UF DCE-MRI and the early phase of the conventional DCE-MRI. Inter-method agreement in morphological information was evaluated for the two readers using the intraclass correlation coefficient for size, and the kappa statistics for the morphological descriptors. Differences in the proportion of each descriptor were examined using Fisher’s test of independence. Most inter-method agreements were higher than substantial. UF DCE-MRI showed a circumscribed margin and homogeneous enhancement more often than conventional imaging. However, the percentages of readings showing the same morphology assessment between the UF DCE-MRI and conventional DCE-MRI were 71.2% (136/191) for Reader 1 and 69.1% (132/191) for Reader 2. We conclude that UF DCE-MRI may replace conventional DCE-MRI to evaluate the morphological information of malignant breast mass lesions.
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Affiliation(s)
- Akane Ohashi
- Department of Translational Medicine, Diagnostic Radiology, Lund University, 225 02 Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, 225 02 Malmö, Sweden
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Masako Kataoka
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
- Correspondence: ; Tel.: +81-75-751-3760
| | - Mami Iima
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
- Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, Kyoto 606-8507, Japan
| | - Maya Honda
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
- Department of Diagnostic Radiology, Kansai Electric Power Hospital, Osaka 553-0003, Japan
| | - Rie Ota
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
- Department of Radiology, Tenri Hospital, Nara 632-8552, Japan
| | | | | | - Masakazu Toi
- Department of Breast Surgery, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
| | - Sophia Zackrisson
- Department of Translational Medicine, Diagnostic Radiology, Lund University, 225 02 Malmö, Sweden
- Department of Imaging and Functional Medicine, Skåne University Hospital, 225 02 Malmö, Sweden
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto 606-8507, Japan
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Do LN, Lee HJ, Im C, Park JH, Lim HS, Park I. Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms. Tomography 2022; 9:1-11. [PMID: 36648988 PMCID: PMC9844271 DOI: 10.3390/tomography9010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 12/13/2022] [Accepted: 12/16/2022] [Indexed: 12/24/2022] Open
Abstract
The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strategies are different between pure DCIS without invasive component and upgraded DCIS. We demonstrated the potential of using deep learning models for differentiating between upgraded versus pure DCIS in DCIS diagnosed by core-needle biopsy. Preoperative axial dynamic contrast-enhanced magnetic resonance imaging (MRI) data from 352 lesions were used to train, validate, and test three different types of deep learning models. The highest performance was achieved by Recurrent Residual Convolutional Neural Network using Regions of Interest (ROIs) with an accuracy of 75.0% and area under the receiver operating characteristic curve (AUC) of 0.796. Our results suggest that the deep learning approach may provide an assisting tool to predict the histologic upgrade of DCIS and provide personalized treatment strategies to patients with underestimated invasive disease.
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Affiliation(s)
- Luu-Ngoc Do
- Department of Radiology, Chonnam National University, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea
| | - Hyo-Jae Lee
- Department of Radiology, Chonnam National University Hospital, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea
| | - Chaeyeong Im
- Department of Medicine, Chonnam National University, Gwangju 61469, Republic of Korea
| | - Jae Hyeok Park
- Department of Medicine, Chonnam National University, Gwangju 61469, Republic of Korea
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Gwangju 58128, Republic of Korea
- Correspondence: (H.S.L.); (I.P.)
| | - Ilwoo Park
- Department of Radiology, Chonnam National University, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea
- Department of Radiology, Chonnam National University Hospital, 42 Jebong-ro, Dong-gu, Gwangju 61469, Republic of Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju 61186, Republic of Korea
- Department of Data Science, Chonnam National University, Gwangju 61186, Republic of Korea
- Correspondence: (H.S.L.); (I.P.)
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Lee HJ, Park JH, Nguyen AT, Do LN, Park MH, Lee JS, Park I, Lim HS. Prediction of the histologic upgrade of ductal carcinoma in situ using a combined radiomics and machine learning approach based on breast dynamic contrast-enhanced magnetic resonance imaging. Front Oncol 2022; 12:1032809. [PMID: 36408141 PMCID: PMC9667063 DOI: 10.3389/fonc.2022.1032809] [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: 08/31/2022] [Accepted: 10/14/2022] [Indexed: 11/07/2022] Open
Abstract
Objective To investigate whether support vector machine (SVM) trained with radiomics features based on breast magnetic resonance imaging (MRI) could predict the upgrade of ductal carcinoma in situ (DCIS) diagnosed by core needle biopsy (CNB) after surgical excision. Materials and methods This retrospective study included a total of 349 lesions from 346 female patients (mean age, 54 years) diagnosed with DCIS by CNB between January 2011 and December 2017. Based on histological confirmation after surgery, the patients were divided into pure (n = 198, 56.7%) and upgraded DCIS (n = 151, 43.3%). The entire dataset was randomly split to training (80%) and test sets (20%). Radiomics features were extracted from the intratumor region-of-interest, which was semi-automatically drawn by two radiologists, based on the first subtraction images from dynamic contrast-enhanced T1-weighted MRI. A least absolute shrinkage and selection operator (LASSO) was used for feature selection. A 4-fold cross validation was applied to the training set to determine the combination of features used to train SVM for classification between pure and upgraded DCIS. Sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC) were calculated to evaluate the model performance using the hold-out test set. Results The model trained with 9 features (Energy, Skewness, Surface Area to Volume ratio, Gray Level Non Uniformity, Kurtosis, Dependence Variance, Maximum 2D diameter Column, Sphericity, and Large Area Emphasis) demonstrated the highest 4-fold mean validation accuracy and AUC of 0.724 (95% CI, 0.619–0.829) and 0.742 (0.623–0.860), respectively. Sensitivity, specificity, accuracy, and AUC using the test set were 0.733 (0.575–0.892) and 0.7 (0.558–0.842), 0.714 (0.608–0.820) and 0.767 (0.651–0.882), respectively. Conclusion Our study suggested that the combined radiomics and machine learning approach based on preoperative breast MRI may provide an assisting tool to predict the histologic upgrade of DCIS.
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Affiliation(s)
- Hyo-jae Lee
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
| | - Jae Hyeok Park
- Department of Medicine, Chonnam National University, Gwangju, South Korea
| | - Anh-Tien Nguyen
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
| | - Luu-Ngoc Do
- Department of Radiology, Chonnam National University, Gwangju, South Korea
| | - Min Ho Park
- Department of Medicine, Chonnam National University, Gwangju, South Korea
- Department of Surgery, Chonnam National University Hwasun Hospital, Hwasun, South Korea
| | - Ji Shin Lee
- Department of Medicine, Chonnam National University, Gwangju, South Korea
- Department of Pathology, Chonnam National University Hwasun Hospital, Hwasun, South Korea
| | - Ilwoo Park
- Department of Radiology, Chonnam National University Hospital, Gwangju, South Korea
- Department of Radiology, Chonnam National University, Gwangju, South Korea
- Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea
- Department of Data Science, Chonnam National University, Gwangju, South Korea
- *Correspondence: Ilwoo Park, ; Hyo Soon Lim,
| | - Hyo Soon Lim
- Department of Radiology, Chonnam National University, Gwangju, South Korea
- Department of Radiology, Chonnam National University Hwasun Hospital, Hwasun, South Korea
- *Correspondence: Ilwoo Park, ; Hyo Soon Lim,
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Kim ES, Cho N, Kim SY, Lee SH, Chang JM, Kim YS, Ha SM, Moon WK. Added value of ultrafast sequence in abbreviated breast MRI surveillance in women with a personal history of breast cancer: A multireader study. Eur J Radiol 2022; 151:110322. [DOI: 10.1016/j.ejrad.2022.110322] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/31/2022] [Accepted: 04/12/2022] [Indexed: 12/15/2022]
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