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Pedersen MA, Munk OL, Dias AH, Steffensen JH, Møller AL, Johnsson AL, Hansen KV, Bender D, Jakobsen S, Busk M, Gormsen LC, Tramm T, Borgquist S, Vendelbo MH. Dynamic whole-body [ 18F]FES PET/CT increases lesion visibility in patients with metastatic breast cancer. EJNMMI Res 2024; 14:24. [PMID: 38436824 PMCID: PMC10912074 DOI: 10.1186/s13550-024-01080-y] [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: 01/10/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Correct classification of estrogen receptor (ER) status is essential for prognosis and treatment planning in patients with breast cancer (BC). Therefore, it is recommended to sample tumor tissue from an accessible metastasis. However, ER expression can show intra- and intertumoral heterogeneity. 16α-[18F]fluoroestradiol ([18F]FES) Positron Emission Tomography/Computed Tomography (PET/CT) allows noninvasive whole-body (WB) identification of ER distribution and is usually performed as a single static image 60 min after radiotracer injection. Using dynamic whole-body (D-WB) PET imaging, we examine [18F]FES kinetics and explore whether Patlak parametric images ( K i ) are quantitative and improve lesion visibility. RESULTS This prospective study included eight patients with metastatic ER-positive BC scanned using a D-WB PET acquisition protocol. The kinetics of [18F]FES were best characterized by the irreversible two-tissue compartment model in tumor lesions and in the majority of organ tissues. K i values from Patlak parametric images correlated with K i values from the full kinetic analysis, r2 = 0.77, and with the semiquantitative mean standardized uptake value (SUVmean), r2 = 0.91. Furthermore, parametric K i images had the highest target-to-background ratio (TBR) in 162/164 metastatic lesions and the highest contrast-to-noise ratio (CNR) in 99/164 lesions compared to conventional SUV images. TBR was 2.45 (95% confidence interval (CI): 2.25-2.68) and CNR 1.17 (95% CI: 1.08-1.26) times higher in K i images compared to SUV images. These quantitative differences were seen as reduced background activity in the K i images. CONCLUSION [18F]FES uptake is best described by an irreversible two-tissue compartment model. D-WB [18F]FES PET/CT scans can be used for direct reconstruction of parametric K i images, with superior lesion visibility and K i values comparable to K i values found from full kinetic analyses. This may aid correct ER classification and treatment decisions. Trial registration ClinicalTrials.gov: NCT04150731, https://clinicaltrials.gov/study/NCT04150731.
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Affiliation(s)
- Mette A Pedersen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Ole L Munk
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - André H Dias
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
| | | | - Anders L Møller
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Kim Vang Hansen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
| | - Dirk Bender
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Steen Jakobsen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
| | - Morten Busk
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Lars C Gormsen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Trine Tramm
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | - Signe Borgquist
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Mikkel H Vendelbo
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark.
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
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Tweel JED, Ecclestone BR, Gaouda H, Dinakaran D, Wallace MP, Bigras G, Mackey JR, Reza PH. Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment. Curr Oncol 2023; 30:9760-9771. [PMID: 37999128 PMCID: PMC10670721 DOI: 10.3390/curroncol30110708] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, and fixed tissues. Our objective was to determine whether PARS could provide an image quality sufficient for the diagnostic assessment of breast cancer needle core biopsies (NCB). We PARS imaged and virtually H&E stained seven independent unstained formalin-fixed paraffin-embedded breast NCB sections. These identical tissue sections were subsequently stained with standard H&E and digitally scanned. Both the 40× PARS and H&E whole-slide images were assessed by seven breast cancer pathologists, masked to the origin of the images. A concordance analysis was performed to quantify the diagnostic performances of standard H&E and PARS virtual H&E. The PARS images were deemed to be of diagnostic quality, and pathologists were unable to distinguish the image origin, above that expected by chance. The diagnostic concordance on cancer vs. benign was high between PARS and conventional H&E (98% agreement) and there was complete agreement for within-PARS images. Similarly, agreement was substantial (kappa > 0.6) for specific cancer subtypes. PARS virtual H&E inter-rater reliability was broadly consistent with the published literature on diagnostic performance of conventional histology NCBs across all tested histologic features. PARS was able to image unstained tissues slides that were diagnostically equivalent to conventional H&E. Due to its ability to non-destructively image fixed and fresh tissues, and the suitability of the PARS output for artificial intelligence assistance in diagnosis, this technology has the potential to improve the speed and accuracy of breast cancer diagnosis.
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Affiliation(s)
- James E. D. Tweel
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Benjamin R. Ecclestone
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Hager Gaouda
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Deepak Dinakaran
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Michael P. Wallace
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Gilbert Bigras
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2R3, Canada;
| | - John R. Mackey
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Parsin Haji Reza
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
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Ma T, Semsarian CR, Barratt A, Parker L, Pathmanathan N, Nickel B, Bell KJL. Should low-risk DCIS lose the cancer label? An evidence review. Breast Cancer Res Treat 2023; 199:415-433. [PMID: 37074481 PMCID: PMC10175360 DOI: 10.1007/s10549-023-06934-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/30/2023] [Indexed: 04/20/2023]
Abstract
BACKGROUND Population mammographic screening for breast cancer has led to large increases in the diagnosis and treatment of ductal carcinoma in situ (DCIS). Active surveillance has been proposed as a management strategy for low-risk DCIS to mitigate against potential overdiagnosis and overtreatment. However, clinicians and patients remain reluctant to choose active surveillance, even within a trial setting. Re-calibration of the diagnostic threshold for low-risk DCIS and/or use of a label that does not include the word 'cancer' might encourage the uptake of active surveillance and other conservative treatment options. We aimed to identify and collate relevant epidemiological evidence to inform further discussion on these ideas. METHODS We searched PubMed and EMBASE databases for low-risk DCIS studies in four categories: (1) natural history; (2) subclinical cancer found at autopsy; (3) diagnostic reproducibility (two or more pathologist interpretations at a single time point); and (4) diagnostic drift (two or more pathologist interpretations at different time points). Where we identified a pre-existing systematic review, the search was restricted to studies published after the inclusion period of the review. Two authors screened records, extracted data, and performed risk of bias assessment. We undertook a narrative synthesis of the included evidence within each category. RESULTS Natural History (n = 11): one systematic review and nine primary studies were included, but only five provided evidence on the prognosis of women with low-risk DCIS. These studies reported that women with low-risk DCIS had comparable outcomes whether or not they had surgery. The risk of invasive breast cancer in patients with low-risk DCIS ranged from 6.5% (7.5 years) to 10.8% (10 years). The risk of dying from breast cancer in patients with low-risk DCIS ranged from 1.2 to 2.2% (10 years). Subclinical cancer at autopsy (n = 1): one systematic review of 13 studies estimated the mean prevalence of subclinical in situ breast cancer to be 8.9%. Diagnostic reproducibility (n = 13): two systematic reviews and 11 primary studies found at most moderate agreement in differentiating low-grade DCIS from other diagnoses. Diagnostic drift: no studies found. CONCLUSION Epidemiological evidence supports consideration of relabelling and/or recalibrating diagnostic thresholds for low-risk DCIS. Such diagnostic changes would need agreement on the definition of low-risk DCIS and improved diagnostic reproducibility.
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Affiliation(s)
- Tara Ma
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Caitlin R Semsarian
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Alexandra Barratt
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
- Wiser Healthcare, Sydney, Australia
| | - Lisa Parker
- Sydney School of Pharmacy, Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Department of Radiation Oncology, Royal North Shore Hospital, Sydney, Australia
| | - Nirmala Pathmanathan
- Western Sydney Local Health District, Sydney, Australia
- Westmead Breast Cancer Institute, Westmead Hospital, Sydney, Australia
| | - Brooke Nickel
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Katy J L Bell
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia.
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4
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Shvetsov N, Grønnesby M, Pedersen E, Møllersen K, Busund LTR, Schwienbacher R, Bongo LA, Kilvaer TK. A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images. Cancers (Basel) 2022; 14:cancers14122974. [PMID: 35740648 PMCID: PMC9221016 DOI: 10.3390/cancers14122974] [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: 05/16/2022] [Revised: 06/06/2022] [Accepted: 06/10/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Tumor tissues sampled from patients contain prognostic and predictive information beyond what is currently being used in clinical practice. Large-scale digitization enables new ways of exploiting this information. The most promising analysis pipelines include deep learning/artificial intelligence (AI). However, to ensure success, AI often requires a time-consuming curation of data. In our approach, we repurposed AI pipelines and training data for cell segmentation and classification to identify tissue-infiltrating lymphocytes (TILs) in lung cancer tissue. We showed that our approach is able to identify TILs and provide prognostic information in an unseen dataset from lung cancer patients. Our methods can be adapted in myriad ways and may help pave the way for the large-scale deployment of digital pathology. Abstract Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17–0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15–0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14–0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.
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Affiliation(s)
- Nikita Shvetsov
- Department of Computer Science, UiT The Arctic University of Norway, N-9038 Tromsø, Norway; (N.S.); (E.P.); (L.A.B.)
| | - Morten Grønnesby
- Department of Medical Biology, UiT The Arctic University of Norway, N-9038 Tromsø, Norway; (M.G.); (L.-T.R.B.); (R.S.)
| | - Edvard Pedersen
- Department of Computer Science, UiT The Arctic University of Norway, N-9038 Tromsø, Norway; (N.S.); (E.P.); (L.A.B.)
| | - Kajsa Møllersen
- Department of Community Medicine, UiT The Arctic University of Norway, N-9038 Tromsø, Norway;
| | - Lill-Tove Rasmussen Busund
- Department of Medical Biology, UiT The Arctic University of Norway, N-9038 Tromsø, Norway; (M.G.); (L.-T.R.B.); (R.S.)
- Department of Clinical Pathology, University Hospital of North Norway, N-9038 Tromsø, Norway
| | - Ruth Schwienbacher
- Department of Medical Biology, UiT The Arctic University of Norway, N-9038 Tromsø, Norway; (M.G.); (L.-T.R.B.); (R.S.)
- Department of Clinical Pathology, University Hospital of North Norway, N-9038 Tromsø, Norway
| | - Lars Ailo Bongo
- Department of Computer Science, UiT The Arctic University of Norway, N-9038 Tromsø, Norway; (N.S.); (E.P.); (L.A.B.)
| | - Thomas Karsten Kilvaer
- Department of Oncology, University Hospital of North Norway, N-9038 Tromsø, Norway
- Department of Clinical Medicine, UiT The Arctic University of Norway, N-9038 Tromsø, Norway
- Correspondence:
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5
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Pettersen HS, Belevich I, Røyset ES, Smistad E, Simpson MR, Jokitalo E, Reinertsen I, Bakke I, Pedersen A. Code-Free Development and Deployment of Deep Segmentation Models for Digital Pathology. Front Med (Lausanne) 2022; 8:816281. [PMID: 35155486 PMCID: PMC8829033 DOI: 10.3389/fmed.2021.816281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 12/24/2021] [Indexed: 11/13/2022] Open
Abstract
Application of deep learning on histopathological whole slide images (WSIs) holds promise of improving diagnostic efficiency and reproducibility but is largely dependent on the ability to write computer code or purchase commercial solutions. We present a code-free pipeline utilizing free-to-use, open-source software (QuPath, DeepMIB, and FastPathology) for creating and deploying deep learning-based segmentation models for computational pathology. We demonstrate the pipeline on a use case of separating epithelium from stroma in colonic mucosa. A dataset of 251 annotated WSIs, comprising 140 hematoxylin-eosin (HE)-stained and 111 CD3 immunostained colon biopsy WSIs, were developed through active learning using the pipeline. On a hold-out test set of 36 HE and 21 CD3-stained WSIs a mean intersection over union score of 95.5 and 95.3% was achieved on epithelium segmentation. We demonstrate pathologist-level segmentation accuracy and clinical acceptable runtime performance and show that pathologists without programming experience can create near state-of-the-art segmentation solutions for histopathological WSIs using only free-to-use software. The study further demonstrates the strength of open-source solutions in its ability to create generalizable, open pipelines, of which trained models and predictions can seamlessly be exported in open formats and thereby used in external solutions. All scripts, trained models, a video tutorial, and the full dataset of 251 WSIs with ~31 k epithelium annotations are made openly available at https://github.com/andreped/NoCodeSeg to accelerate research in the field.
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Affiliation(s)
- Henrik Sahlin Pettersen
- Department of Pathology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Ilya Belevich
- Electron Microscopy Unit, Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Elin Synnøve Røyset
- Department of Pathology, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Erik Smistad
- Department of Health Research, SINTEF Digital, Trondheim, Norway
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Melanie Rae Simpson
- Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- The Clinical Research Unit for Central Norway, Trondheim, Norway
| | - Eija Jokitalo
- Electron Microscopy Unit, Institute of Biotechnology, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Ingerid Reinertsen
- Department of Health Research, SINTEF Digital, Trondheim, Norway
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Ingunn Bakke
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - André Pedersen
- Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
- Department of Health Research, SINTEF Digital, Trondheim, Norway
- The Cancer Foundation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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6
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Preneoplastic Low-Risk Mammary Ductal Lesions (Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ Spectrum): Current Status and Future Directions. Cancers (Basel) 2022; 14:cancers14030507. [PMID: 35158775 PMCID: PMC8833401 DOI: 10.3390/cancers14030507] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 02/04/2023] Open
Abstract
Intraepithelial mammary ductal neoplasia is a spectrum of disease that varies from atypical ductal hyperplasia (ADH), low-grade (LG), intermediate-grade (IG), to high-grade (HG) ductal carcinoma in situ (DCIS). While ADH has the lowest prognostic significance, HG-DCIS carries the highest risk. Due to widely used screening mammography, the number of intraepithelial mammary ductal neoplastic lesions has increased. The consequence of this practice is the increase in the number of patients who are overdiagnosed and, therefore, overtreated. The active surveillance (AS) trials are initiated to separate lesions that require active treatment from those that can be safely monitored and only be treated when they develop a change in the clinical/radiologic characteristics. At the same time, the natural history of these lesions can be evaluated. This review aims to evaluate ADH/DCIS as a spectrum of intraductal neoplastic disease (risk and histomorphology); examine the controversies of distinguishing ADH vs. DCIS and the grading of DCIS; review the upgrading for both ADH and DCIS with emphasis on the variation of methods of detection and the definitions of upgrading; and evaluate the impact of all these variables on the AS trials.
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7
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Katayama A, Toss MS, Parkin M, Sano T, Oyama T, Quinn CM, Ellis IO, Rakha EA. Nuclear morphology in breast lesions: refining its assessment to improve diagnostic concordance. Histopathology 2021; 80:515-528. [PMID: 34605058 DOI: 10.1111/his.14577] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 09/20/2021] [Accepted: 09/30/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Although evaluation of nuclear morphology plays a crucial role in the diagnosis and categorisation of breast lesions, the criteria used to assess nuclear atypia rely on the subjective evaluation of several features that may result in inter- and intra-observer variation. This study aims to refine the definitions of cytonuclear features in various breast lesions. METHODS ImageJ was used to assess the nuclear morphological features including nuclear diameter, axis length, perimeter, area, circularity, and roundness in 160 breast lesions comprising ductal carcinoma in situ (DCIS), invasive breast carcinoma of no special type (IBC-NST), tubular carcinoma, usual ductal hyperplasia (UDH), columnar cell change (CCC) and flat epithelial atypia (FEA). Reference cells included normal epithelial cells, red blood cells (RBCs) and lymphocytes. RESULTS Reference cells showed size differences not only between normal epithelial cells and RBCs but also between RBCs in varied-sized blood vessels. Nottingham grade nuclear pleomorphism scores 1 and 3 cut-offs in IBC, compared to normal epithelial cells, were <1.2x and >1.4x that of mean maximum Feret's diameter and <1.6x and >2.4x that of mean nuclear area, respectively. Nuclear morphometrics were significantly different in low-grade IBC-NST vs. tubular carcinoma, low-grade DCIS vs. UDH, and in CCC vs. FEA. No differences in the nuclear features between grade matched DCIS and IBC were identified. CONCLUSION This study provides a guide for the assessment of nuclear atypia in breast lesions, refines the comparison with reference cells and highlights the potential diagnostic value of image analysis tools in the era of digital pathology.
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Affiliation(s)
- Ayaka Katayama
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK.,Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Michael S Toss
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK
| | - Matthew Parkin
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK
| | - Takaaki Sano
- Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Tetsunari Oyama
- Diagnostic Pathology, Gunma University Graduate School of Medicine, Maebashi, Japan
| | - Cecily M Quinn
- Department of Histopathology, St Vincent's University Hospital, University College, Dublin, Ireland
| | - Ian O Ellis
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK.,Department of Histopathology, Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK
| | - Emad A Rakha
- Translational Medical Sciences Unit, School of Medicine, University of Nottingham, City Hospital, Nottingham, UK.,Department of Histopathology, Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, UK
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8
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Galili B, Samohi S, Yakhini Z. On the stability of log-rank test under labeling errors. Bioinformatics 2021; 37:4451-4459. [PMID: 34255820 PMCID: PMC8652036 DOI: 10.1093/bioinformatics/btab495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/25/2021] [Accepted: 07/02/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation Log-rank test is a widely used test that serves to assess the statistical significance
of observed differences in survival, when comparing two or more groups. The log-rank
test is based on several assumptions that support the validity of the calculations. It
is naturally assumed, implicitly, that no errors occur in the labeling of the samples.
That is, the mapping between samples and groups is perfectly correct. In this work, we
investigate how test results may be affected when considering some errors in the
original labeling. Results We introduce and define the uncertainty that arises from labeling errors in log-rank
test. In order to deal with this uncertainty, we develop a novel algorithm for
efficiently calculating a stability interval around the original log-rank
P-value and prove its correctness. We demonstrate our algorithm on
several datasets. Availability and implementation We provide a Python implementation, called LoRSI, for calculating the stability
interval using our algorithm https://github.com/YakhiniGroup/LoRSI. Supplementary information Supplementary data are
available at Bioinformatics online.
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Affiliation(s)
- Ben Galili
- Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel
| | - Samohi Samohi
- Arazi School of Computer Science, Interdisciplinary Center, Herzliya, Israel
| | - Zohar Yakhini
- Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel.,Arazi School of Computer Science, Interdisciplinary Center, Herzliya, Israel
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9
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Van Bockstal MR, Berlière M, Duhoux FP, Galant C. Interobserver Variability in Ductal Carcinoma In Situ of the Breast. Am J Clin Pathol 2020; 154:596-609. [PMID: 32566938 DOI: 10.1093/ajcp/aqaa077] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVES Since most patients with ductal carcinoma in situ (DCIS) of the breast are treated upon diagnosis, evidence on its natural progression to invasive carcinoma is limited. It is estimated that around half of the screen-detected DCIS lesions would have remained indolent if they had never been detected. Many patients with DCIS are therefore probably overtreated. Four ongoing randomized noninferiority trials explore active surveillance as a treatment option. Eligibility for these trials is mainly based on histopathologic features. Hence, the call for reproducible histopathologic assessment has never sounded louder. METHODS Here, the available classification systems for DCIS are discussed in depth. RESULTS This comprehensive review illustrates that histopathologic evaluation of DCIS is characterized by significant interobserver variability. Future digitalization of pathology, combined with development of deep learning algorithms or so-called artificial intelligence, may be an innovative solution to tackle this problem. However, implementation of digital pathology is not within reach for each laboratory worldwide. An alternative classification system could reduce the disagreement among histopathologists who use "conventional" light microscopy: the introduction of dichotomous histopathologic assessment is likely to increase interobserver concordance. CONCLUSIONS Reproducible histopathologic assessment is a prerequisite for robust risk stratification and adequate clinical decision-making. Two-tier histopathologic assessment might enhance the quality of care.
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Affiliation(s)
- Mieke R Van Bockstal
- Department of Pathology, Brussels, Belgium
- Breast Clinic, Brussels, Belgium
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
| | - Martine Berlière
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
- Laboratory of Experimental Cancer Research, Department of Radiation Oncology and Experimental Cancer Research, Ghent University, Ghent, Belgium
| | - Francois P Duhoux
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
- Laboratory of Experimental Cancer Research, Department of Radiation Oncology and Experimental Cancer Research, Ghent University, Ghent, Belgium
- Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium
| | - Christine Galant
- Department of Pathology, Brussels, Belgium
- Department of Medical Oncology, King Albert II Cancer Institute, Cliniques universitaires Saint-Luc, Brussels, Belgium
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10
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Ductal Carcinoma In Situ Management: All or Nothing, or Something in between? CURRENT BREAST CANCER REPORTS 2019. [DOI: 10.1007/s12609-019-0306-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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Dzobo K, Adotey S, Thomford NE, Dzobo W. Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2019; 24:247-263. [PMID: 31313972 DOI: 10.1089/omi.2019.0038] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Historically, the term "artificial intelligence" dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.
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Affiliation(s)
- Kevin Dzobo
- International Centre for Genetic Engineering and Biotechnology (ICGEB), Cape Town Component, Wernher and Beit Building (South), UCT Medical Campus, Anzio Road, Observatory 7925, Cape Town, South Africa.,Division of Medical Biochemistry and Institute of Infectious Disease and Molecular Medicine, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Sampson Adotey
- International Development Innovation Network, D-Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Nicholas E Thomford
- Pharmacogenetics Research Group, Division of Human Genetics, Department of Pathology and Institute of Infectious Diseases and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Observatory 7925, Cape Town, South Africa
| | - Witness Dzobo
- Pathology and Immunology Department, University Hospital Southampton, Mail Point B, Tremona Road, Southampton, UK.,University of Portsmouth, Faculty of Science, St Michael's Building, White Swan Road, Portsmouth, UK
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12
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de Boer LL, Kho E, Nijkamp J, Van de Vijver KK, Sterenborg HJCM, ter Beek LC, Ruers TJM. Method for coregistration of optical measurements of breast tissue with histopathology: the importance of accounting for tissue deformations. JOURNAL OF BIOMEDICAL OPTICS 2019; 24:1-12. [PMID: 31347338 PMCID: PMC6995961 DOI: 10.1117/1.jbo.24.7.075002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/09/2019] [Indexed: 05/24/2023]
Abstract
For the validation of optical diagnostic technologies, experimental results need to be benchmarked against the gold standard. Currently, the gold standard for tissue characterization is assessment of hematoxylin and eosin (H&E)-stained sections by a pathologist. When processing tissue into H&E sections, the shape of the tissue deforms with respect to the initial shape when it was optically measured. We demonstrate the importance of accounting for these tissue deformations when correlating optical measurement with routinely acquired histopathology. We propose a method to register the tissue in the H&E sections to the optical measurements, which corrects for these tissue deformations. We compare the registered H&E sections to H&E sections that were registered with an algorithm that does not account for tissue deformations by evaluating both the shape and the composition of the tissue and using microcomputer tomography data as an independent measure. The proposed method, which did account for tissue deformations, was more accurate than the method that did not account for tissue deformations. These results emphasize the need for a registration method that accounts for tissue deformations, such as the method presented in this study, which can aid in validating optical techniques for clinical use.
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Affiliation(s)
- Lisanne L. de Boer
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Esther Kho
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Jasper Nijkamp
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Koen K. Van de Vijver
- The Netherlands Cancer Institute, Department of Pathology, Amsterdam, The Netherlands
- Ghent University Hospital, Department of Pathology, Gent, Belgium
| | - Henricus J. C. M. Sterenborg
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
- Amsterdam University Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Leon C. ter Beek
- The Netherlands Cancer Institute, Department of Medical Physics, Amsterdam, The Netherlands
| | - Theo J. M. Ruers
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
- University of Twente, Faculty of Science and Technology, Enschede, The Netherlands
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13
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Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, Flanders AE, Lungren MP, Mendelson DS, Rudie JD, Wang G, Kandarpa K. A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop. Radiology 2019; 291:781-791. [PMID: 30990384 PMCID: PMC6542624 DOI: 10.1148/radiol.2019190613] [Citation(s) in RCA: 175] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2019] [Revised: 03/24/2019] [Accepted: 03/25/2019] [Indexed: 01/08/2023]
Abstract
Imaging research laboratories are rapidly creating machine learning systems that achieve expert human performance using open-source methods and tools. These artificial intelligence systems are being developed to improve medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection, computer-aided classification, and radiogenomics. In August 2018, a meeting was held in Bethesda, Maryland, at the National Institutes of Health to discuss the current state of the art and knowledge gaps and to develop a roadmap for future research initiatives. Key research priorities include: 1, new image reconstruction methods that efficiently produce images suitable for human interpretation from source data; 2, automated image labeling and annotation methods, including information extraction from the imaging report, electronic phenotyping, and prospective structured image reporting; 3, new machine learning methods for clinical imaging data, such as tailored, pretrained model architectures, and federated machine learning methods; 4, machine learning methods that can explain the advice they provide to human users (so-called explainable artificial intelligence); and 5, validated methods for image de-identification and data sharing to facilitate wide availability of clinical imaging data sets. This research roadmap is intended to identify and prioritize these needs for academic research laboratories, funding agencies, professional societies, and industry.
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Affiliation(s)
- Curtis P. Langlotz
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Bibb Allen
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Bradley J. Erickson
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Jayashree Kalpathy-Cramer
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Keith Bigelow
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Tessa S. Cook
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Adam E. Flanders
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Matthew P. Lungren
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - David S. Mendelson
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Jeffrey D. Rudie
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Ge Wang
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
| | - Krishna Kandarpa
- From the Department of Radiology, Stanford University, Stanford, CA 94305 (C.P.L., M.P.L.); Department of Radiology, Grandview Medical Center, Birmingham, Ala (B.A.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass (J.K.C.); GE Healthcare, Chicago, Ill (K.B.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (T.S.C., J.D.R.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY (D.S.M.); Biomedical Imaging Center, Rensselaer Polytechnic Institute, Troy, NY (G.W.); and National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Washington, DC (K.K.)
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14
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Rojas KE, Fortes TA, Borgen PI. Leveraging the variable natural history of ductal carcinoma in situ (DCIS) to select optimal therapy. Breast Cancer Res Treat 2018; 174:307-313. [PMID: 30536119 DOI: 10.1007/s10549-018-05080-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Accepted: 11/29/2018] [Indexed: 11/27/2022]
Abstract
PURPOSE Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive ductal carcinoma. The authors sought to discuss the evidence suggesting that not all DCIS will progress to invasive disease if left untreated. RESULTS Four lines of evidence align to suggest that not all of this in-situ disease progresses to invasive cancer: its prevalence on screening mammography, studies of missed diagnoses, incidental findings in autopsy specimens, and large retrospective reviews of those treated with excision alone. CONCLUSION A clearer understanding of the variable history of DCIS coupled with advances in genomic profiling of the disease holds the promise of reducing widespread over-treatment of this non-invasive cancer. Additionally, identification of higher risk of recurrence subsets may select patients for whom more aggressive treatment may be appropriate.
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Affiliation(s)
- Kristin E Rojas
- Department of Surgery, Brooklyn Breast Cancer Program of Maimonides Medical Center, 745 64th Street, Brooklyn, NY, 11220, USA.
| | - Thais A Fortes
- Department of Surgery, Brooklyn Breast Cancer Program of Maimonides Medical Center, 745 64th Street, Brooklyn, NY, 11220, USA
| | - Patrick I Borgen
- Department of Surgery, Brooklyn Breast Cancer Program of Maimonides Medical Center, 745 64th Street, Brooklyn, NY, 11220, USA
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15
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Brawley OW. On assessing the effect of breast cancer screening schemes. Cancer 2017; 123:3656-3659. [PMID: 28832972 DOI: 10.1002/cncr.30840] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/20/2017] [Accepted: 05/08/2017] [Indexed: 11/07/2022]
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16
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Luo J, Johnston BS, Kitsch AE, Hippe DS, Korde LA, Javid S, Lee JM, Peacock S, Lehman CD, Partridge SC, Rahbar H. Ductal Carcinoma in Situ: Quantitative Preoperative Breast MR Imaging Features Associated with Recurrence after Treatment. Radiology 2017; 285:788-797. [PMID: 28914599 DOI: 10.1148/radiol.2017170587] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Purpose To investigate whether specific imaging features on breast magnetic resonance (MR) images are associated with ductal carcinoma in situ (DCIS) recurrence risk after definitive treatment. Materials and Methods Patients with DCIS who underwent preoperative dynamic contrast material-enhanced (DCE) MR imaging between 2004 and 2014 with ipsilateral recurrence more than 6 months after definitive surgical treatment were retrospectively identified. For each patient, a control subject with DCIS that did not recur was identified and matched on the basis of clinical, histopathologic, and treatment features known to affect recurrence risk. On DCE MR images, lesion characteristics (longest diameter, functional tumor volume [FTV], peak percentage enhancement [PE], peak signal enhancement ratio [SER], and washout fraction) and normal tissue features (background parenchymal enhancement [BPE] volume, mean BPE) were quantitatively measured. MR imaging features were compared between patients and control subjects by using the Wilcoxon signed-rank test, with adjustment for multiple comparisons. Results Of 415 subjects with DCIS who underwent preoperative MR imaging, 14 experienced recurrence and 11 had an identifiable matching control subject (final cohort, 11 patients and 11 control subjects). Median time to recurrence was 14 months, and median follow-up for control subjects was 102 months. When compared with matched control subjects, patients with DCIS recurrence exhibited significantly greater FTV (median, 9.3 cm3 vs 1.3 cm3, P = .01), lesion peak SER (median, 1.7 vs 1.2; P = .03), and mean BPE (median, 58.3% vs 41.1%; P = .02). Conclusion Quantitative lesion and normal breast tissue characteristics at preoperative MR imaging in women with newly diagnosed DCIS show promise for association with breast cancer recurrence after treatment. © RSNA, 2017.
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Affiliation(s)
- Jing Luo
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Brian S Johnston
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Averi E Kitsch
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Daniel S Hippe
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Larissa A Korde
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Sara Javid
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Janie M Lee
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Sue Peacock
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Constance D Lehman
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Savannah C Partridge
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
| | - Habib Rahbar
- From the Departments of Radiology (J.L., B.S.J., A.E.K., D.S.H., J.M.L., S.P., S.C.P., H.R.), Medicine, Division of Oncology (L.A.K.), and Surgery, Division of Surgical Oncology (S.J.), University of Washington School of Medicine, Seattle Cancer Care Alliance, 825 Eastlake Ave East, Seattle, WA 98109-1023; and Department of Radiology, Massachusetts General Hospital, Boston, Mass (C.D.L.)
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