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Hashiba KA, Mercaldo S, Venkatesh SL, Bahl M. Prediction of Surgical Upstaging Risk of Ductal Carcinoma In Situ Using Machine Learning Models. JOURNAL OF BREAST IMAGING 2023; 5:695-702. [PMID: 38046928 PMCID: PMC10689255 DOI: 10.1093/jbi/wbad071] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Indexed: 12/05/2023]
Abstract
Objective The purpose of this study was to build machine learning models to predict surgical upstaging risk of ductal carcinoma in situ (DCIS) to invasive cancer and to compare model performance to eligibility criteria used by the Comparison of Operative versus Monitoring and Endocrine Therapy (COMET) active surveillance trial. Methods Medical records were retrospectively reviewed of all women with DCIS at core-needle biopsy who underwent surgery from 2007 to 2016 at an academic medical center. Multivariable regression and machine learning models were developed to evaluate upstaging-related features and their performance was compared with that achieved using the COMET trial eligibility criteria. Results Of 1387 women (mean age, 57 years; range, 27-89 years), the upstaging rate of DCIS was 17% (235/1387). On multivariable analysis, upstaging-associated features were presentation of DCIS as a palpable area of concern, imaging finding of a mass, and nuclear grades 2 or 3 at biopsy (P < 0.05). If COMET trial eligibility criteria were applied to our study cohort, then 496 women (42%, 496/1175) would have been eligible for the trial, with an upstaging rate of 12% (61/496). Of the machine learning models, none had a significantly lower upstaging rate than 12%. However, if using the models to determine eligibility, then a significantly larger proportion of women (56%-87%) would have been eligible for active surveillance. Conclusion Use of machine learning models to determine eligibility for the COMET trial identified a larger proportion of women eligible for surveillance compared with current eligibility criteria while maintaining similar upstaging rates.
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Affiliation(s)
| | - Sarah Mercaldo
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| | - Sheila L Venkatesh
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
| | - Manisha Bahl
- Massachusetts General Hospital, Department of Radiology, Boston, MA, USA
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Hashiba KA, Bahl M. Ipsilateral tumor recurrence risk in women with ductal carcinoma in situ: application of the Van Nuys Prognostic Index and the Memorial Sloan Kettering Cancer Center nomogram. Breast Cancer Res Treat 2023; 202:185-190. [PMID: 37518825 DOI: 10.1007/s10549-023-07036-5] [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: 05/25/2023] [Accepted: 07/05/2023] [Indexed: 08/01/2023]
Abstract
PURPOSE To apply the Van Nuys Prognostic Index (VNPI) and the Memorial Sloan Kettering Cancer Center (MSKCC) ductal carcinoma in situ (DCIS) nomogram to DCIS patients with known long-term outcomes. METHODS A retrospective review was performed of consecutive patients diagnosed with DCIS from 2007 to 2014. Included patients underwent breast-conserving surgery (BCS) and were followed with imaging for at least five years. For each patient, the VNPI and MSKCC nomogram risk estimates were determined. In addition, variables used in both models were compared between women with and without recurrences using the Wilcoxon signed-rank test and the Pearson's chi-squared test. RESULTS Over the eight-year period, 456 women (average age 57 years, range 30-87) underwent BCS for DCIS. Thirty-one (6.8%) experienced an ipsilateral recurrence. The average VNPI scores were 7 (range 5-9) and 7 (range 4-10) for women with and without a recurrence (p = 0.14), respectively, with 4-6, 7-9, and 10-12 being the low, moderate, and high-risk groups, respectively. Per the MSKCC nomogram, the average five-year recurrence risks were 5% (range 1-12%) and 4% (range 1-38%) for women with and without a recurrence (p = 0.09), respectively. The recurrence risk-related variables were younger patient age, need for one or more re-excision surgeries, and use of endocrine therapy for 0 to less than five years after surgery. CONCLUSION Ipsilateral tumor recurrence risk estimates based on the VNPI and MSKCC nomogram are similar between women with DCIS who did and did not have a recurrence, suggesting that more robust prognostic models are needed.
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Affiliation(s)
- Kimberlee A Hashiba
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA
| | - Manisha Bahl
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, WAC 240, Boston, MA, 02114, USA.
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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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Lamb LR, Mercaldo S, Kim G, Hovis K, Oseni TO, Bahl M. Predicting ipsilateral recurrence in women treated for ductal carcinoma in situ using machine learning and multivariable logistic regression models. Clin Imaging 2022; 92:94-100. [DOI: 10.1016/j.clinimag.2022.08.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 08/05/2022] [Accepted: 08/31/2022] [Indexed: 11/29/2022]
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Hovis K, Mercaldo S, Kim G, Lamb LR, Oseni TO, Bahl M. Contralateral breast cancer after curative-intent treatment for ductal carcinoma in situ: Rate and associated clinicopathological and imaging risk factors. Clin Imaging 2021; 82:179-192. [PMID: 34872008 DOI: 10.1016/j.clinimag.2021.11.018] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/24/2021] [Accepted: 11/14/2021] [Indexed: 12/28/2022]
Abstract
PURPOSE Patients who have ductal carcinoma in situ (DCIS) are undergoing bilateral mastectomy at increasing rates. One of the reasons is to minimize contralateral breast cancer (CBC) risk. The purpose of this study is to determine the rate of and risk factors associated with CBC in women treated for DCIS. METHODS A retrospective study was performed of women with DCIS at surgery from 2007 to 2014 who had at least five-year follow-up. Patient attributes, imaging findings, histopathology results, and surgical and long-term outcomes were collected. Features associated with a CBC were assessed with multivariable logistic regression models. RESULTS 613 women (mean 56 years, range 30-87) with DCIS underwent breast-conserving surgery (BCS) (n = 426), unilateral mastectomy (n = 101), or bilateral mastectomy (n = 86), with mean follow-up of 7.9 years. Of the 527 women who had BCS or unilateral mastectomy, 7.4% (n = 39) developed a CBC (DCIS in 12 and invasive cancer in 27). 4.1% (5/122) of women treated with adjuvant endocrine therapy developed a CBC, compared to 8.4% (34/405) who were not treated (p = .11). Features associated with CBC risk were younger age at menarche (adjusted odds ratio [aOR] of 0.76, p = .03) and low nuclear grade of DCIS (aOR of 5.43 for grade 1 versus 3, p = .01). CONCLUSION In women treated for DCIS, the overall rate of CBC was low at 7.4%. Younger age at menarche and low nuclear grade of DCIS had significant associations with higher CBC risk.
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Affiliation(s)
- Keegan Hovis
- Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street (WAC 240), Boston, MA 02114, USA
| | - Sarah Mercaldo
- Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street (WAC 240), Boston, MA 02114, USA
| | - Geunwon Kim
- Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street (WAC 240), Boston, MA 02114, USA
| | - Leslie R Lamb
- Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street (WAC 240), Boston, MA 02114, USA
| | - Tawakalitu O Oseni
- Division of Surgical Oncology, Department of Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Manisha Bahl
- Division of Breast Imaging, Department of Radiology, Massachusetts General Hospital, 55 Fruit Street (WAC 240), Boston, MA 02114, USA.
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