Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context
Summary: Background: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most patients with DCIS undergo treatment res...
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Elsevier
2025-06-01
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| author | Shannon Doyle Esther H. Lips Eric Marcus Lennart Mulder Yat-Hee Liu Francesco Dal Canton Timo Kootstra Maartje M. van Seijen Ihssane Bouybayoune Elinor J. Sawyer Alastair M. Thompson Sarah E. Pinder Clara I. Sánchez Jonas Teuwen Jelle Wesseling Jelle Wesseling Jos Jonkers Jacco van Rheenen Esther H. Lips Marjanka Schmidt Lodewyk F.A. Wessels Proteeti Bhattacharjee Alastair Thompson Serena Nik-Zainal Helen Davies Elinor J. Sawyer Andrew Futreal Nicholas Navin E. Shelley Hwang Fariba Behbod Daniel Rea Hilary Stobart Deborah Collyar Donna Pinto Ellen Verschuur Marja van Oirsouw |
| author_facet | Shannon Doyle Esther H. Lips Eric Marcus Lennart Mulder Yat-Hee Liu Francesco Dal Canton Timo Kootstra Maartje M. van Seijen Ihssane Bouybayoune Elinor J. Sawyer Alastair M. Thompson Sarah E. Pinder Clara I. Sánchez Jonas Teuwen Jelle Wesseling Jelle Wesseling Jos Jonkers Jacco van Rheenen Esther H. Lips Marjanka Schmidt Lodewyk F.A. Wessels Proteeti Bhattacharjee Alastair Thompson Serena Nik-Zainal Helen Davies Elinor J. Sawyer Andrew Futreal Nicholas Navin E. Shelley Hwang Fariba Behbod Daniel Rea Hilary Stobart Deborah Collyar Donna Pinto Ellen Verschuur Marja van Oirsouw |
| author_sort | Shannon Doyle |
| collection | DOAJ |
| description | Summary: Background: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most patients with DCIS undergo treatment resembling IBC. To facilitate identification of low-risk DCIS, we developed deep learning models using histology whole-slide images (WSIs) and clinico-pathological data. Methods: We predicted invasive recurrence in patients with primary, pure DCIS treated with breast-conserving surgery using clinical Cox proportional hazards models and deep learning. Deep learning models were trained end-to-end with only WSIs or in combination with clinical data (integrative). We employed nested k-fold cross-validation (k = 5) on a Dutch multicentre dataset (n = 558). Models were also tested on the UK-based Sloane dataset (n = 94). Findings: Evaluated over 20 years on the Dutch dataset, deep learning models using only WSIs effectively stratified patients into low-risk (no recurrence) and high-risk (invasive recurrence) groups (negative predictive value (NPV) = 0.79 (95% CI: 0.74–0.83); hazard ratio (HR) = 4.48 (95% CI: 3.41–5.88, p < 0.0001); area under the receiver operating characteristic curve (AUC) = 0.75 (95% CI: 0.70–0.79)). Integrative models achieved similar results with slightly enhanced hazard ratios compared to the image-only models (NPV = 0.77 (95% CI 0.73–0.82); HR = 4.85 (95% CI 3.65–6.45, p < 0.0001); AUC = 0.75 (95% CI 0.7–0.79)). In contrast, clinical models were borderline significant (NPV = 0.64 (95% CI 0.59–0.69); HR = 1.37 (95% CI 1.03–1.81, p = 0.041); AUC = 0.57 (95% CI 0.52–0.62)). Furthermore, external validation of the models was unsuccessful, limited by the small size and low number of cases (22/94) in our external dataset, WSI quality, as well as the lack of well-annotated datasets that allow robust validation. Interpretation: Deep learning models using routinely processed WSIs hold promise for DCIS risk stratification, while the benefits of integrating clinical data merit further investigation. Obtaining a larger, high-quality external multicentre dataset would be highly valuable, as successful generalisation of these models could demonstrate their potential to reduce overtreatment in DCIS by enabling active surveillance for women at low risk. Funding: Cancer Research UK, the Dutch Cancer Society (KWF), and the Dutch Ministry of Health, Welfare and Sport. |
| format | Article |
| id | doaj-art-a1f2ed74e43a4f328748f8ae52dcc0a5 |
| institution | OA Journals |
| issn | 2352-3964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
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| series | EBioMedicine |
| spelling | doaj-art-a1f2ed74e43a4f328748f8ae52dcc0a52025-08-20T02:17:05ZengElsevierEBioMedicine2352-39642025-06-0111610575010.1016/j.ebiom.2025.105750Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in contextShannon Doyle0Esther H. Lips1Eric Marcus2Lennart Mulder3Yat-Hee Liu4Francesco Dal Canton5Timo Kootstra6Maartje M. van Seijen7Ihssane Bouybayoune8Elinor J. Sawyer9Alastair M. Thompson10Sarah E. Pinder11Clara I. Sánchez12Jonas Teuwen13Jelle Wesseling14Jelle WesselingJos JonkersJacco van RheenenEsther H. LipsMarjanka SchmidtLodewyk F.A. WesselsProteeti BhattacharjeeAlastair ThompsonSerena Nik-ZainalHelen DaviesElinor J. SawyerAndrew FutrealNicholas NavinE. Shelley HwangFariba BehbodDaniel ReaHilary StobartDeborah CollyarDonna PintoEllen VerschuurMarja van OirsouwDivision of Radiation Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, the NetherlandsDivision of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the NetherlandsDivision of Radiation Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Informatics Institute, University of Amsterdam, Amsterdam, the NetherlandsDivision of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the NetherlandsDivision of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the NetherlandsDepartment of Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, the NetherlandsDepartment of Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, the NetherlandsDepartment of Pathology, Leiden University Medical Center, Leiden, the NetherlandsSchool of Cancer & Pharmaceutical Sciences, King's College London, UKSchool of Cancer & Pharmaceutical Sciences, King's College London, UKDepartment of Surgery, Baylor College of Medicine, Houston, TX, USASchool of Cancer & Pharmaceutical Sciences, King's College London, UKInformatics Institute, University of Amsterdam, Amsterdam, the NetherlandsDivision of Radiation Oncology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Department of Medical Imaging, Radboud University Nijmegen, Nijmegen, the NetherlandsDivision of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Department of Pathology, Netherlands Cancer Institute – Antoni van Leeuwenhoek, Amsterdam, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands; Corresponding author. Plesmanlaan 121, Room D2.038, Amsterdam 1066 CX, the Netherlands.Summary: Background: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most patients with DCIS undergo treatment resembling IBC. To facilitate identification of low-risk DCIS, we developed deep learning models using histology whole-slide images (WSIs) and clinico-pathological data. Methods: We predicted invasive recurrence in patients with primary, pure DCIS treated with breast-conserving surgery using clinical Cox proportional hazards models and deep learning. Deep learning models were trained end-to-end with only WSIs or in combination with clinical data (integrative). We employed nested k-fold cross-validation (k = 5) on a Dutch multicentre dataset (n = 558). Models were also tested on the UK-based Sloane dataset (n = 94). Findings: Evaluated over 20 years on the Dutch dataset, deep learning models using only WSIs effectively stratified patients into low-risk (no recurrence) and high-risk (invasive recurrence) groups (negative predictive value (NPV) = 0.79 (95% CI: 0.74–0.83); hazard ratio (HR) = 4.48 (95% CI: 3.41–5.88, p < 0.0001); area under the receiver operating characteristic curve (AUC) = 0.75 (95% CI: 0.70–0.79)). Integrative models achieved similar results with slightly enhanced hazard ratios compared to the image-only models (NPV = 0.77 (95% CI 0.73–0.82); HR = 4.85 (95% CI 3.65–6.45, p < 0.0001); AUC = 0.75 (95% CI 0.7–0.79)). In contrast, clinical models were borderline significant (NPV = 0.64 (95% CI 0.59–0.69); HR = 1.37 (95% CI 1.03–1.81, p = 0.041); AUC = 0.57 (95% CI 0.52–0.62)). Furthermore, external validation of the models was unsuccessful, limited by the small size and low number of cases (22/94) in our external dataset, WSI quality, as well as the lack of well-annotated datasets that allow robust validation. Interpretation: Deep learning models using routinely processed WSIs hold promise for DCIS risk stratification, while the benefits of integrating clinical data merit further investigation. Obtaining a larger, high-quality external multicentre dataset would be highly valuable, as successful generalisation of these models could demonstrate their potential to reduce overtreatment in DCIS by enabling active surveillance for women at low risk. Funding: Cancer Research UK, the Dutch Cancer Society (KWF), and the Dutch Ministry of Health, Welfare and Sport.http://www.sciencedirect.com/science/article/pii/S235239642500194XDeep learningDuctal carcinoma in situMultiomic integrationRisk prediction |
| spellingShingle | Shannon Doyle Esther H. Lips Eric Marcus Lennart Mulder Yat-Hee Liu Francesco Dal Canton Timo Kootstra Maartje M. van Seijen Ihssane Bouybayoune Elinor J. Sawyer Alastair M. Thompson Sarah E. Pinder Clara I. Sánchez Jonas Teuwen Jelle Wesseling Jelle Wesseling Jos Jonkers Jacco van Rheenen Esther H. Lips Marjanka Schmidt Lodewyk F.A. Wessels Proteeti Bhattacharjee Alastair Thompson Serena Nik-Zainal Helen Davies Elinor J. Sawyer Andrew Futreal Nicholas Navin E. Shelley Hwang Fariba Behbod Daniel Rea Hilary Stobart Deborah Collyar Donna Pinto Ellen Verschuur Marja van Oirsouw Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context EBioMedicine Deep learning Ductal carcinoma in situ Multiomic integration Risk prediction |
| title | Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context |
| title_full | Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context |
| title_fullStr | Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context |
| title_full_unstemmed | Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context |
| title_short | Deep learning for predicting invasive recurrence of ductal carcinoma in situ: leveraging histopathology images and clinical featuresResearch in context |
| title_sort | deep learning for predicting invasive recurrence of ductal carcinoma in situ leveraging histopathology images and clinical featuresresearch in context |
| topic | Deep learning Ductal carcinoma in situ Multiomic integration Risk prediction |
| url | http://www.sciencedirect.com/science/article/pii/S235239642500194X |
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