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Leslie M, Pathak R, Dooley WC, Squires RA, Rui H, Chervoneva I, Tanaka T. Surgical Delay-Associated Mortality Risk Varies by Subtype in Loco-Regional Breast Cancer Patients in SEER-Medicare. RESEARCH SQUARE 2024:rs.3.rs-4171651. [PMID: 38659868 PMCID: PMC11042396 DOI: 10.21203/rs.3.rs-4171651/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Substantial evidence supports that delay of surgery after breast cancer diagnosis is associated with increased mortality risk, leading to the introduction of a new Commission on Cancer quality measure for receipt of surgery within 60 days of diagnosis for non-neoadjuvant patients. Breast cancer subtype is a critical prognostic factor and determines treatment options; however, it remains unknown whether surgical delay-associated breast cancer-specific mortality (BCSM) risk differs by subtype. This retrospective cohort study aimed to assess whether the impact of delayed surgery on survival varies by subtype (hormone [HR]+/HER2-, HR-/HER2-, and HER2+) in patients with loco-regional breast cancer who received surgery as their first treatment between 2010-2017 using the SEER-Medicare. Continuous time to surgery from diagnostic biopsy (TTS; days) in reference to TTS = 30 days. BCSM were evaluated as flexibly dependent on continuous time (days) to surgery from diagnosis (TTS) using Cox proportional hazards and Fine and Gray competing-risk regression models, respectively, by HR status. Inverse propensity score-weighting was used to adjust for demographic, clinical, and treatment variables impacting TTS. Adjusted BCSM risk grew with increasing TTS across all subtypes, however, the pattern and extent of the association varied. HR+/HER2- patients exhibited the most pronounced increase in BCSM risk associated with TTS, with approximately exponential growth after 42 days, with adjusted subdistribution hazard ratios (sHR) of 1.21 (95% CI: 1.06-1.37) at TTS = 60 days, 1.79 (95% CI: 1.40-2.29) at TTS = 90 days, and 2.83 (95% CI: 1.76-4.55) at TTS = 120 days. In contrast, both HER2 + and HR-/HER2- patients showed slower, approximately linear growth in sHR, although non-significant in HR-HER2-.
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Affiliation(s)
- Macall Leslie
- University of Oklahoma Health Sciences Center, Stephenson Cancer Center, 975 NE, 10th, Oklahoma City, OK 73104, USA
| | - Rashmi Pathak
- University of Oklahoma Health Sciences Center, Stephenson Cancer Center, 975 NE, 10th, Oklahoma City, OK 73104, USA
| | - William C Dooley
- University of Oklahoma Health Sciences Center, School of Medicine, Dept. of Surgery, 800 Stanton L. Young Blvd., Oklahoma City, OK 73104, USA
| | - Ronald A Squires
- University of Oklahoma Health Sciences Center, School of Medicine, Dept. of Surgery, 800 Stanton L. Young Blvd., Oklahoma City, OK 73104, USA
| | - Hallgeir Rui
- Thomas Jefferson University, Department of Pharmacology and Experimental Therapeutics, 1015 Chestnut St., Suite 520, Philadelphia, PA 19107, USA
| | - Inna Chervoneva
- Thomas Jefferson University, Division of Biostatistics, Department of Pharmacology and Experimental Therapeutics, 1015 Chestnut St., Suite 520, Philadelphia, PA 19107, USA
| | - Takemi Tanaka
- University of Oklahoma Health Sciences Center, Stephenson Cancer Center, 975 NE, 10th, Oklahoma City, OK 73104, USA
- University of Oklahoma Health Sciences Center, School of Medicine, Dept. of Pathology, 800 Stanton L. Young Blvd., Oklahoma City, OK 73104, USA
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Little MP, Eidemüller M, Kaiser JC, Apostoaei AI. Minimum latency effects for cancer associated with exposures to radiation or other carcinogens. Br J Cancer 2024; 130:819-829. [PMID: 38212483 PMCID: PMC10912293 DOI: 10.1038/s41416-023-02544-z] [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: 06/01/2023] [Revised: 11/27/2023] [Accepted: 12/05/2023] [Indexed: 01/13/2024] Open
Abstract
BACKGROUND In estimating radiation-associated cancer risks a fixed period for the minimum latency is often assumed. Two empirical latency functions have been used to model latency, continuously increasing from 0. A stochastic biologically-based approach yields a still more plausible way of describing latency and can be directly estimated from clinical data. METHODS We derived the parameters for a stochastic biologically-based model from tumour growth data for various cancers, and least-squares fitted the two types of empirical latency function to the stochastic model-predicted cumulative probability. RESULTS There is wide variation in growth rates among tumours, particularly slow for prostate and thyroid cancer and particularly fast for leukaemia. The slow growth rate for prostate and thyroid tumours implies that the number of tumour cells required for clinical detection cannot greatly exceed 106. For all tumours, both empirical latency functions closely approximated the predicted biological model cumulative probability. CONCLUSIONS Our results, illustrating use of a stochastic biologically-based model using clinical data not tied to any particular carcinogen, have implications for estimating latency associated with any mutagen. They apply to tumour growth in general, and may be useful for example, in planning screenings for cancer using imaging techniques.
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Affiliation(s)
- Mark P Little
- Radiation Epidemiology Branch, National Cancer Institute, Bethesda, MD, 20892-9778, USA.
- Faculty of Health and Life Sciences, Oxford Brookes University, Headington Campus, Oxford, OX3 0BP, UK.
| | - Markus Eidemüller
- Federal Office for Radiation Protection, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
| | - J Christian Kaiser
- Federal Office for Radiation Protection, Ingolstädter Landstr. 1, 85764, Neuherberg, Germany
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Kim JG, Haslam B, Diab AR, Sakhare A, Grisot G, Lee H, Holt J, Lee CI, Lotter W, Sorensen AG. Impact of a Categorical AI System for Digital Breast Tomosynthesis on Breast Cancer Interpretation by Both General Radiologists and Breast Imaging Specialists. Radiol Artif Intell 2024; 6:e230137. [PMID: 38323914 PMCID: PMC10982824 DOI: 10.1148/ryai.230137] [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: 04/25/2023] [Revised: 12/26/2023] [Accepted: 01/22/2024] [Indexed: 02/08/2024]
Abstract
Purpose To evaluate performance improvements of general radiologists and breast imaging specialists when interpreting a set of diverse digital breast tomosynthesis (DBT) examinations with the aid of a custom-built categorical artificial intelligence (AI) system. Materials and Methods A fully balanced multireader, multicase reader study was conducted to compare the performance of 18 radiologists (nine general radiologists and nine breast imaging specialists) reading 240 retrospectively collected screening DBT mammograms (mean patient age, 59.8 years ± 11.3 [SD]; 100% women), acquired between August 2016 and March 2019, with and without the aid of a custom-built categorical AI system. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity across general radiologists and breast imaging specialists reading with versus without AI were assessed. Reader performance was also analyzed as a function of breast cancer characteristics and patient subgroups. Results Every radiologist demonstrated improved interpretation performance when reading with versus without AI, with an average AUC of 0.93 versus 0.87, demonstrating a difference in AUC of 0.06 (95% CI: 0.04, 0.08; P < .001). Improvement in AUC was observed for both general radiologists (difference of 0.08; P < .001) and breast imaging specialists (difference of 0.04; P < .001) and across all cancer characteristics (lesion type, lesion size, and pathology) and patient subgroups (race and ethnicity, age, and breast density) examined. Conclusion A categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics. Keywords: Computer-aided Diagnosis, Screening Mammography, Digital Breast Tomosynthesis, Breast Cancer, Screening, Convolutional Neural Network (CNN), Artificial Intelligence Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Jiye G. Kim
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Bryan Haslam
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Abdul Rahman Diab
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Ashwin Sakhare
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Giorgia Grisot
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Hyunkwang Lee
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Jacqueline Holt
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
| | - Christoph I. Lee
- From DeepHealth, RadNet AI Solutions, 212 Elm Street, Somerville, MA 02144 (J.G.K., B.H., A.R.D., A.S., G.G., H.L., W.L., A.G.S.); Atos zData, Newark, Del (A.S.); Delaware Imaging Network, RadNet, Wilmington, Del (J.H.); Department of Radiology, University of Washington School of Medicine, Fred Hutchinson Cancer Center, Seattle, Wash (C.I.L.); Department of Health Systems & Population Health, School of Public Health, University of Washington, Seattle, Wash (C.I.L.); and Dana-Farber Cancer Institute, Harvard Medical School, Boston, Mass (W.L.)
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Peters J, van Dijck JAAM, Elias SG, Otten JDM, Broeders MJM. The prognostic potential of mammographic growth rate of invasive breast cancer in the Nijmegen breast cancer screening cohort. J Med Screen 2024:9691413231222765. [PMID: 38295359 DOI: 10.1177/09691413231222765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Abstract
OBJECTIVES Insight into the aggressiveness of potential breast cancers found in screening may optimize recall decisions. Specific growth rate (SGR), measured on mammograms, may provide valuable prognostic information. This study addresses the association of SGR with prognostic factors and overall survival in patients with invasive carcinoma of no special type (NST) from a screened population. METHODS In this historic cohort study, 293 women with NST were identified from all participants in the Nijmegen screening program (2003-2007). Information on clinicopathological factors was retrieved from patient files and follow-up on vital status through municipalities. On consecutive mammograms, tumor volumes were estimated. After comparing five growth functions, SGR was calculated using the best-fitting function. Regression and multivariable survival analyses described associations between SGR and prognostic factors as well as overall survival. RESULTS Each one standard deviation increase in SGR was associated with an increase in the Nottingham prognostic index by 0.34 [95% confidence interval (CI): 0.21-0.46]. Each one standard deviation increase in SGR increased the odds of a tumor with an unfavorable subtype (based on histologic grade and hormone receptors; odds ratio 2.14 [95% CI: 1.45-3.15]) and increased the odds of diagnosis as an interval cancer (versus screen-detected; odds ratio 1.57 [95% CI: 1.20-2.06]). After a median of 12.4 years of follow-up, 78 deaths occurred. SGR was not associated with overall survival (hazard ratio 1.12 [95% CI: 0.87-1.43]). CONCLUSIONS SGR may indicate prognostically relevant differences in tumor aggressiveness if serial mammograms are available. A potential association with cause-specific survival could not be determined and is of interest for future research.
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Affiliation(s)
- Jim Peters
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jos A A M van Dijck
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sjoerd G Elias
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Johannes D M Otten
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Mireille J M Broeders
- Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands
- Dutch Expert Centre for Screening (LRCB), Nijmegen, The Netherlands
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5
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Vargas-Palacios A, Sharma N, Sagoo GS. Cost-effectiveness requirements for implementing artificial intelligence technology in the Women's UK Breast Cancer Screening service. Nat Commun 2023; 14:6110. [PMID: 37777510 PMCID: PMC10542368 DOI: 10.1038/s41467-023-41754-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Accepted: 09/17/2023] [Indexed: 10/02/2023] Open
Abstract
The UK NHS Women's National Breast Screening programme aims to detect breast cancer early. The reference standard approach requires mammograms to be independently double-read by qualified radiology staff. If two readers disagree, arbitration by an independent reader is undertaken. Whilst this process maximises accuracy and minimises recall rates, the procedure is labour-intensive, adding pressure to a system currently facing a workforce crisis. Artificial intelligence technology offers an alternative to human readers. While artificial intelligence has been shown to be non-inferior versus human second readers, the minimum requirements needed (effectiveness, set-up costs, maintenance, etc) for such technology to be cost-effective in the NHS have not been evaluated. We developed a simulation model replicating NHS screening services to evaluate the potential value of the technology. Our results indicate that if non-inferiority is maintained, the use of artificial intelligence technology as a second reader is a viable and potentially cost-effective use of NHS resources.
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Affiliation(s)
- Armando Vargas-Palacios
- Academic Unit of Health Economics, University of Leeds, Leeds, UK.
- Centro de Investigación en Ciencias de la Salud, Universidad Anáhuac, Mexico, México.
| | | | - Gurdeep S Sagoo
- Academic Unit of Health Economics, University of Leeds, Leeds, UK
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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6
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Heggland T, Vatten LJ, Opdahl S, Weedon-Fekjær H. Non-progressive breast carcinomas detected at mammography screening: a population study. Breast Cancer Res 2023; 25:80. [PMID: 37403150 PMCID: PMC10318793 DOI: 10.1186/s13058-023-01682-9] [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: 04/23/2023] [Accepted: 06/26/2023] [Indexed: 07/06/2023] Open
Abstract
BACKGROUND Some breast carcinomas detected at screening, especially ductal carcinoma in situ, may have limited potential for progression to symptomatic disease. To determine non-progression is a challenge, but if all screening-detected breast tumors eventually reach a clinical stage, the cumulative incidence at a reasonably high age would be similar for women with or without screening, conditional on the women being alive. METHODS Using high-quality population data with 24 years of follow-up from the gradually introduced BreastScreen Norway program, we studied whether all breast carcinomas detected at mammography screening 50-69 years of age would progress to clinical symptoms within 85 years of age. First, we estimated the incidence rates of breast carcinomas by age in scenarios with or without screening, based on an extended age-period-cohort incidence model. Next, we estimated the frequency of non-progressive tumors among screening-detected cases, by calculating the difference in the cumulative rate of breast carcinomas between the screening and non-screening scenarios at 85 years of age. RESULTS Among women who attended BreastScreen Norway from the age of 50 to 69 years, we estimated that 1.1% of the participants were diagnosed with a breast carcinoma without the potential to progress to symptomatic disease by 85 years of age. This proportion of potentially non-progressive tumors corresponded to 15.7% [95% CI 3.3, 27.1] of breast carcinomas detected at screening. CONCLUSIONS Our findings suggest that nearly one in six breast carcinomas detected at screening may be non-progressive.
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Affiliation(s)
- Torunn Heggland
- Oslo Centre for Biostatistics and Epidemiology [OCBE], Research Support Services, Oslo University Hospital, Oslo, Norway.
- Oslo Centre for Biostatistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
| | - Lars Johan Vatten
- Department of Public Health and Nursing, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Signe Opdahl
- Department of Public Health and Nursing, Faculty of Medicine and Health Science, Norwegian University of Science and Technology, Trondheim, Norway
| | - Harald Weedon-Fekjær
- Oslo Centre for Biostatistics and Epidemiology [OCBE], Research Support Services, Oslo University Hospital, Oslo, Norway
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Halfter K, Schlesinger-Raab A, Schubert-Fritschle G, Hölzel D. Risk of metastasis in breast cancer through delay in start of primary therapy. THE LANCET REGIONAL HEALTH. EUROPE 2023; 29:100645. [PMID: 37153855 PMCID: PMC10151015 DOI: 10.1016/j.lanepe.2023.100645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 05/10/2023]
Affiliation(s)
- Kathrin Halfter
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), Marchioninistraße 15, 81377, Munich, Germany
| | - Anne Schlesinger-Raab
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), Marchioninistraße 15, 81377, Munich, Germany
| | - Gabriele Schubert-Fritschle
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), Marchioninistraße 15, 81377, Munich, Germany
| | - Dieter Hölzel
- Institute for Medical Information Processing, Biometry, and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), Marchioninistraße 15, 81377, Munich, Germany
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Yoon B, Kim H, Oh T, Oh S, Jo S, Kim M, Chun KH, Hwang N, Lee S, Jin S, Atkins A, Yu R, Downes M, Kim JW, Kim H, Evans R, Cheong JH, Fang S. PHGDH preserves one-carbon cycle to confer metabolic plasticity in chemoresistant gastric cancer during nutrient stress. Proc Natl Acad Sci U S A 2023; 120:e2217826120. [PMID: 37192160 PMCID: PMC10214193 DOI: 10.1073/pnas.2217826120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 04/19/2023] [Indexed: 05/18/2023] Open
Abstract
Molecular classification of gastric cancer (GC) identified a subgroup of patients showing chemoresistance and poor prognosis, termed SEM (Stem-like/Epithelial-to-mesenchymal transition/Mesenchymal) type in this study. Here, we show that SEM-type GC exhibits a distinct metabolic profile characterized by high glutaminase (GLS) levels. Unexpectedly, SEM-type GC cells are resistant to glutaminolysis inhibition. We show that under glutamine starvation, SEM-type GC cells up-regulate the 3 phosphoglycerate dehydrogenase (PHGDH)-mediated mitochondrial folate cycle pathway to produce NADPH as a reactive oxygen species scavenger for survival. This metabolic plasticity is associated with globally open chromatin structure in SEM-type GC cells, with ATF4/CEBPB identified as transcriptional drivers of the PHGDH-driven salvage pathway. Single-nucleus transcriptome analysis of patient-derived SEM-type GC organoids revealed intratumoral heterogeneity, with stemness-high subpopulations displaying high GLS expression, a resistance to GLS inhibition, and ATF4/CEBPB activation. Notably, coinhibition of GLS and PHGDH successfully eliminated stemness-high cancer cells. Together, these results provide insight into the metabolic plasticity of aggressive GC cells and suggest a treatment strategy for chemoresistant GC patients.
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Affiliation(s)
- Bo Kyung Yoon
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Biochemistry and Molecular Biology, Yonsei University College of Medicine, Seoul03722, Korea
- Chronic Intractable Disease for Systems Medicine Research Center, Yonsei University College of Medicine, Seoul03722, Korea
| | - Hyeonhui Kim
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
| | - Tae Gyu Oh
- Gene Expression Laboratory, Salk Institute for Biological Sciences, La Jolla, CA92037
| | - Se Kyu Oh
- Kynogen corporation, Suwon16229, Korea
| | - Sugyeong Jo
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
| | - Minki Kim
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
| | - Kyu-Hye Chun
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Biochemistry and Molecular Biology, Yonsei University College of Medicine, Seoul03722, Korea
- Chronic Intractable Disease for Systems Medicine Research Center, Yonsei University College of Medicine, Seoul03722, Korea
| | - Nahee Hwang
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Biochemistry and Molecular Biology, Yonsei University College of Medicine, Seoul03722, Korea
- Chronic Intractable Disease for Systems Medicine Research Center, Yonsei University College of Medicine, Seoul03722, Korea
| | - Suji Lee
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
| | - Suyon Jin
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
| | - Annette R. Atkins
- Gene Expression Laboratory, Salk Institute for Biological Sciences, La Jolla, CA92037
| | - Ruth T. Yu
- Gene Expression Laboratory, Salk Institute for Biological Sciences, La Jolla, CA92037
| | - Michael Downes
- Gene Expression Laboratory, Salk Institute for Biological Sciences, La Jolla, CA92037
| | - Jae-woo Kim
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Biochemistry and Molecular Biology, Yonsei University College of Medicine, Seoul03722, Korea
- Chronic Intractable Disease for Systems Medicine Research Center, Yonsei University College of Medicine, Seoul03722, Korea
| | - Hyunkyung Kim
- Department of Biochemistry and Molecular Biology, Korea University College of Medicine, Seoul02841, Korea
- Department of Biomedical Sciences, BK21 Graduate Program, Korea University College of Medicine, Seoul02841, Korea
| | - Ronald M. Evans
- Gene Expression Laboratory, Salk Institute for Biological Sciences, La Jolla, CA92037
| | - Jae-Ho Cheong
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Biochemistry and Molecular Biology, Yonsei University College of Medicine, Seoul03722, Korea
- Chronic Intractable Disease for Systems Medicine Research Center, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Surgery, Yonsei University College of Medicine, Seoul03722, Korea
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul03722, Korea
- Veraverse Inc., Seoul06162, Korea
| | - Sungsoon Fang
- Graduate school of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul03722, Korea
- Kynogen corporation, Suwon16229, Korea
- Severance Biomedical Science Institute, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul06230, Korea
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Qi T, Cao Y. Dissecting sources of variability in patient response to targeted therapy: anti-HER2 therapies as a case study. Eur J Pharm Sci 2023; 186:106467. [PMID: 37196833 DOI: 10.1016/j.ejps.2023.106467] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/21/2023] [Accepted: 05/14/2023] [Indexed: 05/19/2023]
Abstract
BACKGROUND AND PURPOSE Despite their use to treat cancers with specific genetic aberrations, targeted therapies elicit heterogeneous responses. Sources of variability are critical to targeted therapy drug development, yet there exists no method to discern their relative contribution to response heterogeneity. EXPERIMENTAL APPROACH We use HER2-amplified breast cancer and two agents, neratinib and lapatinib, to develop a platform for dissecting sources of variability in patient response. The platform comprises four components: pharmacokinetics, tumor burden and growth kinetics, clonal composition, and sensitivity to treatment. Pharmacokinetics are simulated using population models to capture variable systemic exposure. Tumor burden and growth kinetics are derived from clinical data comprising over 800,000 women. The fraction of sensitive and resistant tumor cells is informed by HER2 immunohistochemistry. Growth rate-corrected drug potency is used to predict response. We integrate these factors and simulate clinical outcomes for virtual patients. The relative contribution of these factors to response heterogeneity are compared. KEY RESULTS The platform was verified with clinical data, including response rate and progression-free survival (PFS). For both neratinib and lapatinib, the growth rate of resistant clones influenced PFS to a higher degree than systemic drug exposure. Variability in exposure at labeled doses did not significantly influence response. Sensitivity to drug strongly influenced responses to neratinib. Variability in patient HER2 immunohistochemistry scores influenced responses to lapatinib. Exploratory twice daily dosing improved PFS for neratinib but not lapatinib. CONCLUSION AND IMPLICATIONS The platform can dissect sources of variability in response to target therapy, which may facilitate decision-making during drug development.
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Affiliation(s)
- Timothy Qi
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yanguang Cao
- Division of Pharmacotherapy and Experimental Therapeutics, Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer. Cancers (Basel) 2023; 15:cancers15041087. [PMID: 36831426 PMCID: PMC9954278 DOI: 10.3390/cancers15041087] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 02/10/2023] Open
Abstract
Molecular diagnostic tests help clinicians understand the underlying biological mechanisms of their patients' breast cancer (BC) and facilitate clinical management. Several tissue-based mRNA tests are used routinely in clinical practice, particularly for assessing the BC recurrence risk, which can guide treatment decisions. However, blood-based mRNA assays have only recently started to emerge. This review explores the commercially available blood mRNA diagnostic assays for BC. These tests enable differentiation of BC from non-BC subjects (Syantra DX, BCtect), detection of small tumours <10 mm (early BC detection) (Syantra DX), detection of different cancers (including BC) from a single blood sample (multi-cancer blood test Aristotle), detection of BC in premenopausal and postmenopausal women and those with high breast density (Syantra DX), and improvement of diagnostic outcomes of DNA testing (variant interpretation) (+RNAinsight). The review also evaluates ongoing transcriptomic research on exciting possibilities for future assays, including blood transcriptome analyses aimed at differentiating lymph node positive and negative BC, distinguishing BC and benign breast disease, detecting ductal carcinoma in situ, and improving early detection further (expression changes can be detected in blood up to eight years before diagnosing BC using conventional approaches, while future metastatic and non-metastatic BC can be distinguished two years before BC diagnosis).
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11
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Strandberg R, Abrahamsson L, Isheden G, Humphreys K. Tumour Growth Models of Breast Cancer for Evaluating Early Detection-A Summary and a Simulation Study. Cancers (Basel) 2023; 15:cancers15030912. [PMID: 36765870 PMCID: PMC9913080 DOI: 10.3390/cancers15030912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 01/26/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023] Open
Abstract
With the advent of nationwide mammography screening programmes, a number of natural history models of breast cancers have been developed and used to assess the effects of screening. The first half of this article provides an overview of a class of these models and describes how they can be used to study latent processes of tumour progression from observational data. The second half of the article describes a simulation study which applies a continuous growth model to illustrate how effects of extending the maximum age of the current Swedish screening programme from 74 to 80 can be evaluated. Compared to no screening, the current and extended programmes reduced breast cancer mortality by 18.5% and 21.7%, respectively. The proportion of screen-detected invasive cancers which were overdiagnosed was estimated to be 1.9% in the current programme and 2.9% in the extended programme. With the help of these breast cancer natural history models, we can better understand the latent processes, and better study the effects of breast cancer screening.
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Affiliation(s)
- Rickard Strandberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
- Correspondence: (R.S.); (K.H.)
| | - Linda Abrahamsson
- Center for Primary Health Care Research, Lund University, 205 02 Malmö, Sweden
| | | | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77 Stockholm, Sweden
- Correspondence: (R.S.); (K.H.)
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12
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Gürsoy M, Aslan Ö, Oktay Alfatlı A, Zekioğlu O, Göktepe B. Radiological and clinicopathological findings of breast cancer during the COVID-19 pandemic: a comparative study with the pre-pandemic era. Diagn Interv Radiol 2023; 29:53-58. [PMID: 36959768 PMCID: PMC10679590 DOI: 10.5152/dir.2022.21646] [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: 06/13/2021] [Accepted: 10/18/2021] [Indexed: 01/14/2023]
Abstract
PURPOSE The diagnosis and surgical treatment delays that occurred during the coronavirus disease-2019- (COVID-19) pandemic may have affected breast cancer presentation. This study aimed to determine whether there was a difference in the clinicopathological characteristics of breast cancers during the pandemic by comparing them with similar cases from the previous year. The study also aimed to determine the radiological findings of breast cancers during the pandemic. METHODS A retrospective review was made of patients who underwent surgery for breast cancer between March 11, 2020, and December 11, 2020 (the pandemic group). These patients were compared with similar patients from the previous year (the pre-pandemic group). The postoperative histopathology results of both groups were compared, and the preoperative radiological findings of the pandemic group were defined. RESULTS There were 71 patients in the pandemic group and 219 patients in the pre-pandemic group. The tumor size was significantly greater, lymph node involvement was more frequent, and waiting time for surgery was longer in the pandemic group (P < 0.001, P = 0.044, P = 0.001, respectively). There was no significant difference between the groups in respect of in situ/invasive tumor distribution, histological type and histological grade of tumor, the presence of lymphovascular/perineural invasion, multifocal/multicentric focus, and Breast Imaging Reporting and Data System Classification (P > 0.15). The radiologic findings of breast cancer during the pandemic typically showed characteristics of malignancy. CONCLUSION Patients diagnosed with breast cancer during the COVID-19 pandemic had larger tumor sizes, more frequent lymph node involvement and longer waiting time for surgical treatment. Screening programs should be continued as soon as possible by taking necessary precautions.
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Affiliation(s)
- Merve Gürsoy
- Department of Radiology, İzmir Katip Çelebi University Faculty of Medicine, İzmir, Turkey
| | - Özge Aslan
- Department of Radiology, Ege University Faculty of Medicine, İzmir, Turkey
| | | | - Osman Zekioğlu
- Department of Pathology, Ege University Faculty of Medicine, İzmir, Turkey
| | - Berk Göktepe
- Department of General Surgery, Ege University Faculty of Medicine, İzmir, Turkey
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13
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Breast cancer: emerging principles of metastasis, adjuvant and neoadjuvant treatment from cancer registry data. J Cancer Res Clin Oncol 2023; 149:721-735. [PMID: 36538148 PMCID: PMC9931789 DOI: 10.1007/s00432-022-04369-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 09/17/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE Growing primary breast cancers (PT) can initiate local recurrences (LR), regional lymph nodes (pLN) and distant metastases (MET). Components of these progressions are initiation, frequency, growth duration, and survival. These characteristics describe principles which proposed molecular concepts and hypotheses must align with. METHODS In a population-based retrospective modeling approach using data from the Munich Cancer Registry key steps and factors associated with metastasis were identified and quantified. Analysis of 66.800 patient datasets over four time periods since 1978, reliable evidence is obtained even in small subgroups. Together with results of clinical trials on prevention and adjuvant treatment (AT) principles for the MET process and AT are derived. RESULTS The median growth periods for PT/MET/LR/pLN comes to 12.5/8.8/5/3.5 years, respectively. Even if 30% of METs only appear after 10 years, a pre-diagnosis MET initiation principle not a delayed one should be true. The growth times of PTs and METs vary by a factor of 10 or more but their ratio is robust at about 1.4. Principles of AT are 50% PT eradication, the selective and partial eradication of bone and lung METs. This cannot be improved by extending the duration of the previously known ATs. CONCLUSION A paradigm of ten principles for the MET process and ATs is derived from real world data and clinical trials indicates that there is no rationale for the long-term application of endocrine ATs, risk of PTs by hormone replacement therapies, or cascading initiation of METs. The principles show limits and opportunities for innovation also through alternative interpretations of well-known studies. The outlined MET process should be generalizable to all solid tumors.
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14
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Gasparini A, Humphreys K. A natural history and copula-based joint model for regional and distant breast cancer metastasis. Stat Methods Med Res 2022; 31:2415-2430. [PMID: 36120891 PMCID: PMC9703386 DOI: 10.1177/09622802221122410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The few existing statistical models of breast cancer recurrence and progression to distant metastasis are predominantly based on multi-state modelling. While useful for summarising the risk of recurrence, these provide limited insight into the underlying biological mechanisms and have limited use for understanding the implications of population-level interventions. We develop an alternative, novel, and parsimonious approach for modelling latent tumour growth and spread to local and distant metastasis, based on a natural history model with biologically inspired components. We include marginal sub-models for local and distant breast cancer metastasis, jointly modelled using a copula function. Different formulations (and correlation shapes) are allowed, thus we can incorporate and directly model the correlation between local and distant metastasis flexibly and efficiently. Submodels for the latent cancer growth, the detection process, and screening sensitivity, together with random effects to account for between-patients heterogeneity, are included. Although relying on several parametric assumptions, the joint copula model can be useful for understanding - potentially latent - disease dynamics, obtaining patient-specific, model-based predictions, and studying interventions at a population level, for example, using microsimulation. We illustrate this approach using data from a Swedish population-based case-control study of postmenopausal breast cancer, including examples of useful model-based predictions.
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Affiliation(s)
- Alessandro Gasparini
- Alessandro Gasparini, Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, P.O. Box 281, SE-171 77 Stockholm, Sweden.
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15
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Harding C, Burmistrov D, Pompei M, Pompei F. Estimation of Breast Cancer Overdiagnosis in a U.S. Breast Screening Cohort. Ann Intern Med 2022; 175:W115. [PMID: 36252260 DOI: 10.7326/l22-0274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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16
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Hölzel D, Schubert-Fritschle G, Engel J. Estimation of the Risk of Progression of Breast Cancer After the COVID-19 Lockdown. DEUTSCHES ARZTEBLATT INTERNATIONAL 2022; 119:368-369. [PMID: 36017987 DOI: 10.3238/arztebl.m2022.0165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 10/14/2021] [Accepted: 03/10/2022] [Indexed: 06/15/2023]
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17
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Tomic H, Bjerkén A, Hellgren G, Johnson K, Förnvik D, Zackrisson S, Tingberg A, Dustler M, Bakic PR. Development and evaluation of a method for tumor growth simulation in virtual clinical trials of breast cancer screening. J Med Imaging (Bellingham) 2022; 9:033503. [PMID: 35685119 PMCID: PMC9168969 DOI: 10.1117/1.jmi.9.3.033503] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 05/12/2022] [Indexed: 09/27/2023] Open
Abstract
Purpose: Image-based analysis of breast tumor growth rate may optimize breast cancer screening and diagnosis by suggesting optimal screening intervals and guide the clinical discussion regarding personalized screening based on tumor aggressiveness. Simulation-based virtual clinical trials (VCTs) can be used to evaluate and optimize medical imaging systems and design clinical trials. This study aimed to simulate tumor growth over multiple screening rounds. Approach: This study evaluates a preliminary method for simulating tumor growth. Clinical data on tumor volume doubling time (TVDT) was used to fit a probability distribution ("clinical fit") of TVDTs. Simulated tumors with TVDTs sampled from the clinical fit were inserted into 30 virtual breasts ("simulated cohort") and used to simulate mammograms. Based on the TVDT, two successive screening rounds were simulated for each virtual breast. TVDTs from clinical and simulated mammograms were compared. Tumor sizes in the simulated mammograms were measured by a radiologist in three repeated sessions to estimate TVDT. Results: The mean TVDT was 297 days (standard deviation, SD, 169 days) in the clinical fit and 322 days (SD, 217 days) in the simulated cohort. The mean estimated TVDT was 340 days (SD, 287 days). No significant difference was found between the estimated TVDTs from simulated mammograms and clinical TVDT values ( p > 0.5 ). No significant difference ( p > 0.05 ) was observed in the reproducibility of the tumor size measurements between the two screening rounds. Conclusions: The proposed method for tumor growth simulation has demonstrated close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.
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Affiliation(s)
- Hanna Tomic
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Anna Bjerkén
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Gustav Hellgren
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Kristin Johnson
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Department of Medical Imaging and Physiology, Malmö, Sweden
| | - Daniel Förnvik
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Sophia Zackrisson
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Department of Medical Imaging and Physiology, Malmö, Sweden
| | - Anders Tingberg
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Skåne University Hospital, Radiation Physics, Malmö, Sweden
| | - Magnus Dustler
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
| | - Predrag R. Bakic
- Lund University, Medical Radiation Physics, Department of Translational Medicine, Malmö, Sweden
- Lund University, Diagnostic Radiology, Department of Translational Medicine, Malmö, Sweden
- University of Pennsylvania, Department of Radiology, Philadelphia, Pennsylvania, United States
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18
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Strandberg R, Czene K, Eriksson M, Hall P, Humphreys K. Estimating Distributions of Breast Cancer Onset and Growth in a Swedish Mammography Screening Cohort. Cancer Epidemiol Biomarkers Prev 2022; 31:569-577. [PMID: 35027432 PMCID: PMC9306270 DOI: 10.1158/1055-9965.epi-21-1011] [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: 08/26/2021] [Revised: 11/03/2021] [Accepted: 01/06/2022] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND In recent years, biologically motivated continuous tumor growth models have been introduced for breast cancer screening data. These provide a novel framework from which mammography screening effectiveness can be studied. METHODS We use a newly developed natural history model, which is unique in that it includes a carcinogenesis model for tumor onset, to analyze data from a large Swedish mammography cohort consisting of 65,536 participants, followed for periods of up to 6.5 years. Using patient data on age at diagnosis, tumor size, and mode of detection, as well as screening histories, we estimate distributions of patient's age at onset, (inverse) tumor growth rates, symptomatic detection rates, and screening sensitivities. We also allow the growth rate distribution to depend on the age at onset. RESULTS We estimate that by the age of 75, 13.4% of women have experienced onset. On the basis of a model that accounts for the role of mammographic density in screening sensitivity, we estimated median tumor doubling times of 167 days for tumors with onset occurring at age 40, and 207 days for tumors with onset occurring at age 60. CONCLUSIONS With breast cancer natural history models and population screening data, we can estimate latent processes of tumor onset, tumor growth, and mammography screening sensitivity. We can also study the relationship between the age at onset and tumor growth rates. IMPACT Quantifying the underlying processes of breast cancer progression is important in the era of individualized screening.
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Affiliation(s)
- Rickard Strandberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Solna, Sweden.,Corresponding Author: Rickard Strandberg, Karolinska Institutet, Box 281, Solna 17177, Sweden. Phone: 468-524-6887; E-mail:
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.,Swedish eScience Research Centre (SeRC), Karolinska Institutet, Solna, Sweden
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19
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Gasparini A, Humphreys K. Estimating latent, dynamic processes of breast cancer tumour growth and distant metastatic spread from mammography screening data. Stat Methods Med Res 2022; 31:862-881. [PMID: 35103530 PMCID: PMC9099158 DOI: 10.1177/09622802211072496] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
We propose a framework for jointly modelling tumour size at diagnosis and time to
distant metastatic spread, from diagnosis, based on latent dynamic sub-models of
growth of the primary tumour and of distant metastatic detection. The framework
also includes a sub-model for screening sensitivity as a function of latent
tumour size. Our approach connects post-diagnosis events to the natural history
of cancer and, once refined, may prove useful for evaluating new interventions,
such as personalised screening regimes. We evaluate our model-fitting procedure
using Monte Carlo simulation, showing that the estimation algorithm can retrieve
the correct model parameters, that key patterns in the data can be captured by
the model even with misspecification of some structural assumptions, and that,
still, with enough data it should be possible to detect strong
misspecifications. Furthermore, we fit our model to observational data from an
extension of a case-control study of post-menopausal breast cancer in Sweden,
providing model-based estimates of the probability of being free from detected
distant metastasis as a function of tumour size, mode of detection (of the
primary tumour), and screening history. For women with screen-detected cancer
and two previous negative screens, the probabilities of being free from detected
distant metastases 5 years after detection and removal of the primary tumour are
0.97, 0.89 and 0.59 for tumours of diameter 5, 15 and 35 mm, respectively. We
also study the probability of having latent/dormant metastases at detection of
the primary tumour, estimating that 33% of patients in our study had such
metastases.
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Affiliation(s)
- Alessandro Gasparini
- Alessandro Gasparini, Department of Medical
Epidemiology and Biostatistics, Karolinska Institutet, PO Box 281, SE-17177,
Stockholm, Sweden.
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20
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Čelešnik H, Potočnik U. Peripheral Blood Transcriptome in Breast Cancer Patients as a Source of Less Invasive Immune Biomarkers for Personalized Medicine, and Implications for Triple Negative Breast Cancer. Cancers (Basel) 2022; 14:cancers14030591. [PMID: 35158858 PMCID: PMC8833511 DOI: 10.3390/cancers14030591] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 01/14/2022] [Accepted: 01/19/2022] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Triple-negative breast cancer (TNBC) is an aggressive and heterogeneous breast cancer (BC) type which is difficult to treat and accompanied by disease recurrence. A better understanding of TNBC and the identification of novel biomarkers is needed to facilitate clinical decisions. Immune-related biomarkers are of particular interest, since immune responses play an important role in BC outcome. Transcriptome studies of peripheral blood cells can help us to understand the systemic immune responses to cancer cells and the mechanisms underlying cancer initiation and progression. They enable the identification of novel immune biomarkers for early cancer detection and personalized BC management and may bring forward new immunotherapy options. Recent transcriptome analyses of peripheral blood cells have delineated novel BC-patient immune subgroups. This categorization has implications for cancer prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Additionally, transcriptome studies have identified TNBC-enriched blood transcriptional signatures that can differentiate TNBC from other classical BC subtypes. Abstract Transcriptome studies of peripheral blood cells can advance our understanding of the systemic immune response to the presence of cancer and the mechanisms underlying cancer onset and progression. This enables the identification of novel minimally invasive immune biomarkers for early cancer detection and personalized cancer management and may bring forward new immunotherapy options. Recent blood gene expression analyses in breast cancer (BC) identified distinct patient subtypes that differed in the immune reaction to cancer and were distinct from the clinical BC subtypes, which are categorized based on expression of specific receptors on tumor cells. Introducing new BC subtypes based on peripheral blood gene expression profiles may be appropriate, since it may assist in BC prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous, and difficult-to-treat disease, and identification of novel biomarkers for this BC is crucial for clinical decision-making. A few studies have reported TNBC-enriched blood transcriptional signatures, mostly related to strong inflammation and augmentation of altered immune signaling, that can differentiate TNBC from other classical BC subtypes and facilitate diagnosis. Future research is geared toward transitioning from expression signatures in unfractionated blood cells to those in immune cell subpopulations.
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Affiliation(s)
- Helena Čelešnik
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova Ulica 17, 2000 Maribor, Slovenia;
- Center for Human Genetics & Pharmacogenomics, Faculty of Medicine, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia
| | - Uroš Potočnik
- Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova Ulica 17, 2000 Maribor, Slovenia;
- Center for Human Genetics & Pharmacogenomics, Faculty of Medicine, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia
- Department for Science and Research, University Medical Centre Maribor, Ljubljanska Ulica 5, 2000 Maribor, Slovenia
- Correspondence: ; Tel.: +386-2-330-5874
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21
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Can Circulating Tumor DNA Support a Successful Screening Test for Early Cancer Detection? The Grail Paradigm. Diagnostics (Basel) 2021; 11:diagnostics11122171. [PMID: 34943407 PMCID: PMC8700281 DOI: 10.3390/diagnostics11122171] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 11/05/2021] [Accepted: 11/16/2021] [Indexed: 01/02/2023] Open
Abstract
Circulating tumor DNA (ctDNA) is a new pan-cancer tumor marker with important applications for patient prognosis, monitoring progression, and assessing the success of the therapeutic response. Another important goal is an early cancer diagnosis. There is currently a debate if ctDNA can be used for early cancer detection due to the small tumor burden and low mutant allele fraction (MAF). We compare our previous calculations on the size of detectable cancers by ctDNA analysis with the latest experimental data from Grail’s clinical trial. Current ctDNA-based diagnostic methods could predictably detect tumors of sizes greater than 10–15 mm in diameter. When tumors are of this size or smaller, their MAF is about 0.01% (one tumor DNA molecule admixed with 10,000 normal DNA molecules). The use of 10 mL of blood (4 mL of plasma) will likely contain less than a complete cancer genome, thus rendering the diagnosis of cancer impossible. Grail’s new data confirm the low sensitivity for early cancer detection (<30% for Stage I–II tumors, <20% for Stage I tumors), but specificity was high at 99.5%. According to these latest data, the sensitivity of the Grail test is less than 20% in Stage I disease, casting doubt if this test could become a viable pan-cancer clinical screening tool.
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22
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Khan SA, Hernandez-Villafuerte KV, Muchadeyi MT, Schlander M. Cost-effectiveness of risk-based breast cancer screening: A systematic review. Int J Cancer 2021; 149:790-810. [PMID: 33844853 DOI: 10.1002/ijc.33593] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 03/09/2021] [Accepted: 03/23/2021] [Indexed: 01/01/2023]
Abstract
To analyse published evidence on the economic evaluation of risk-based screening (RBS), a full systematic literature review was conducted. After a quality appraisal, we compared the cost-effectiveness of risk-based strategies (low-risk, medium-risk and high-risk) with no screening and age-based screening. Studies were also analysed for modelling, risk stratification methods, input parameters, data sources and harms and benefits. The 10 modelling papers analysed were based on screening performance of film-based mammography (FBM) (three); digital mammography (DM) and FBM (two); DM alone (three); DM, ultrasound (US) and magnetic resonance imaging (one) and DM and US (one). Seven studies did not include the cost of risk-stratification, and one did not consider the cost of diagnosis. Disutility was incorporated in only six studies (one for screening and five for diagnosis). None of the studies reported disutility of risk-stratification (being considered as high-risk). Risk-stratification methods varied from only breast density (BD) to the combination of familial risk, genetic susceptibility, lifestyle, previous biopsies, Jewish ancestry and reproductive history. Less or no screening in low-risk women and more frequent mammography screening in high-risk women was more cost-effective compared to no screening and age-based screening. High-risk women screened annually yielded a higher mortality rate reduction and more quality-adjusted life years at the expense of higher cost and false positives. RBS can be cost effective compared to the alternatives. However, heterogeneity among risk-stratification methods, input parameters, and weaknesses in the methodologies hinder the derivation of robust conclusions. Therefore, further studies are warranted to assess newer technologies and innovative risk-stratification methods.
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Affiliation(s)
- Shah Alam Khan
- Division of Health Economics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | | | - Muchandifunga Trust Muchadeyi
- Division of Health Economics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Michael Schlander
- Division of Health Economics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
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23
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Modeling breast cancer survival and metastasis rates from moderate-sized clinical data. Clin Exp Metastasis 2021; 38:77-87. [PMID: 33389336 DOI: 10.1007/s10585-020-10066-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 11/20/2020] [Indexed: 10/22/2022]
Abstract
Predicting time-dependent survival probability of a breast cancer patient using information such as primary tumor size, grade, node spread status, and patient age at the time of surgery can be of immense help in managing life expectations and strategizing postoperative treatment. However, for moderate-sized clinical datasets the application of standard Kaplan-Meier theory to determine survival probability as a function of multiple cofactors can become challenging when continuous variables like tumor diameter and survival time are segmented into a large number of narrow intervals, a problem commonly termed the curse of dimensionality. We circumvent this problem by modeling the patient-to-patient distribution of primary tumor diameter with a realistic, right-skewed function, and then matching the diameter-marginalized survival with the mean Kaplan-Meier survival for the data. We apply this procedure on a recent clinical data from 1875 breast cancer patients and develop parameters that can be readily used to estimate post-surgery survival for an arbitrary time length. Finally, we show that the observed fraction of node-positive patients can be quantitatively explained within a simple tumor growth and metastasis framework. Employing two different tumor growth models from the literature (i.e., Gompertz and logistic growth models), we utilize the observed fraction-node-positive data to determine metastasis rates from the surface of a primary tumor and its patient-to-patient distribution.
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24
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Riggio AI, Varley KE, Welm AL. The lingering mysteries of metastatic recurrence in breast cancer. Br J Cancer 2021; 124:13-26. [PMID: 33239679 PMCID: PMC7782773 DOI: 10.1038/s41416-020-01161-4] [Citation(s) in RCA: 235] [Impact Index Per Article: 78.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 02/07/2023] Open
Abstract
Despite being the hallmark of cancer that is responsible for the highest number of deaths, very little is known about the biology of metastasis. Metastatic disease typically manifests after a protracted period of undetectable disease following surgery or systemic therapy, owing to relapse or recurrence. In the case of breast cancer, metastatic relapse can occur months to decades after initial diagnosis and treatment. In this review, we provide an overview of the known key factors that influence metastatic recurrence, with the goal of highlighting the critical unanswered questions that still need to be addressed to make a difference in the mortality of breast cancer patients.
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Affiliation(s)
- Alessandra I Riggio
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Katherine E Varley
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA
| | - Alana L Welm
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
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Mammographic sensitivity as a function of tumor size: A novel estimation based on population-based screening data. Breast 2020; 55:69-74. [PMID: 33348148 PMCID: PMC7753195 DOI: 10.1016/j.breast.2020.12.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 11/24/2020] [Accepted: 12/07/2020] [Indexed: 12/26/2022] Open
Abstract
Background Instead of a single value for mammographic sensitivity, a sensitivity function based on tumor size more realistically reflects mammography’s detection capability. Because previous models may have overestimated size-specific sensitivity, we aimed to provide a novel approach to improve sensitivity estimation as a function of tumor size. Methods Using aggregated data on interval and screen-detected cancers, observed tumor sizes were back-calculated to the time of screening using an exponential tumor growth model and a follow-up time of 4 years. From the observed number of detected cancers and an estimation of the number of false-negative cancers, a model for the sensitivity as a function of tumor size was determined. A univariate sensitivity analysis was conducted by varying follow-up time and tumor volume doubling time (TVDT). A systematic review was conducted for external validation of the sensitivity model. Results Aggregated data of 22,915 screen-detected and 10,670 interval breast cancers from the Dutch screening program were used. The model showed that sensitivity increased from 0 to 85% for tumor sizes from 2 to 20 mm. When TVDT was set at the upper and lower limits of the confidence interval, sensitivity for a 20-mm tumor was 74% and 93%, respectively. The estimated sensitivity gave comparable estimates to those from two of three studies identified by our systematic review. Conclusion Derived from aggregated breast screening outcomes data, our model’s estimation of sensitivity as a function of tumor size may provide a better representation of data observed in screening programs than other models. Mammographic sensitivity is a key indicator of screening effectiveness. Previous model using logistic function might overestimate size-specific sensitivity. Our model showed that sensitivity increased from 0 to 85% for tumor sizes from 2 to 20 mm. Our model may provide a better representation of data observed in screening programs.
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Interleukin-6 trans-signaling is a candidate mechanism to drive progression of human DCCs during clinical latency. Nat Commun 2020; 11:4977. [PMID: 33020483 PMCID: PMC7536220 DOI: 10.1038/s41467-020-18701-4] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Accepted: 09/03/2020] [Indexed: 02/07/2023] Open
Abstract
Although thousands of breast cancer cells disseminate and home to bone marrow until primary surgery, usually less than a handful will succeed in establishing manifest metastases months to years later. To identify signals that support survival or outgrowth in patients, we profile rare bone marrow-derived disseminated cancer cells (DCCs) long before manifestation of metastasis and identify IL6/PI3K-signaling as candidate pathway for DCC activation. Surprisingly, and similar to mammary epithelial cells, DCCs lack membranous IL6 receptor expression and mechanistic dissection reveals IL6 trans-signaling to regulate a stem-like state of mammary epithelial cells via gp130. Responsiveness to IL6 trans-signals is found to be niche-dependent as bone marrow stromal and endosteal cells down-regulate gp130 in premalignant mammary epithelial cells as opposed to vascular niche cells. PIK3CA activation renders cells independent from IL6 trans-signaling. Consistent with a bottleneck function of microenvironmental DCC control, we find PIK3CA mutations highly associated with late-stage metastatic cells while being extremely rare in early DCCs. Our data suggest that the initial steps of metastasis formation are often not cancer cell-autonomous, but also depend on microenvironmental signals. Metastatic dissemination in breast cancer patients occurs early in malignant transformation, raising questions about how disseminated cancer cells (DCC) progress at distant sites. Here, the authors show that DCCs in bone marrow are activated via IL6-trans-signaling and thereby acquire stemness traits relevant for metastasis formation.
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Tyuryumina EY, Neznanov AA, Turumin JL. A Mathematical Model to Predict Diagnostic Periods for Secondary Distant Metastases in Patients with ER/PR/HER2/Ki-67 Subtypes of Breast Cancer. Cancers (Basel) 2020; 12:cancers12092344. [PMID: 32825078 PMCID: PMC7563940 DOI: 10.3390/cancers12092344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 08/13/2020] [Accepted: 08/17/2020] [Indexed: 02/07/2023] Open
Abstract
Previously, a consolidated mathematical model of primary tumor (PT) growth and secondary distant metastasis (sdMTS) growth in breast cancer (BC) (CoMPaS) was presented. The aim was to detect the diagnostic periods for visible sdMTS via CoMPaS in patients with different subtypes ER/PR/HER2/Ki-67 (Estrogen Receptor/Progesterone Receptor/Human Epidermal growth factor Receptor 2/Ki-67 marker) of breast cancer. CoMPaS is based on an exponential growth model and complementing formulas, and the model corresponds to the tumor-node-metastasis (TNM) staging system and BC subtypes (ER/PR/HER2/Ki-67). The CoMPaS model reflects (1) the subtypes of BC, such as ER/PR/HER2/Ki-67, and (2) the growth processes of the PT and sdMTSs in BC patients without or with lymph node metastases (MTSs) in accordance with the eighth edition American Joint Committee on Cancer prognostic staging system for breast cancer. CoMPaS correctly describes the growth of the PT in the ER/PR/HER2/Ki-67 subtypes of BC patients and helps to calculate the different diagnostic periods, depending on the tumor volume doubling time of sdMTS, when sdMTSs might appear. CoMPaS and the corresponding software tool can help (1) to start the early treatment of small sdMTSs in BC patients with different tumor subtypes (ER/PR/HER2/Ki-67), and (2) to consider the patient almost healthy if sdMTSs do not appear during the different diagnostic periods.
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Affiliation(s)
- Ella Ya. Tyuryumina
- International Laboratory for Intelligent Systems and Structural Analysis, Faculty of Computer Science, National Research University Higher School of Economics, 109028 Moscow, Russia;
- Correspondence:
| | - Alexey A. Neznanov
- International Laboratory for Intelligent Systems and Structural Analysis, Faculty of Computer Science, National Research University Higher School of Economics, 109028 Moscow, Russia;
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Tumor Volume Kinetic Analyses Might Explain Excellent Prognoses in Young Patients with Papillary Thyroid Carcinoma. J Thyroid Res 2020; 2020:4652767. [PMID: 32733666 PMCID: PMC7383345 DOI: 10.1155/2020/4652767] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/18/2020] [Accepted: 06/23/2020] [Indexed: 11/26/2022] Open
Abstract
Introduction Young patients with papillary thyroid carcinoma (PTC) generally have excellent prognoses despite their often-advanced disease status. The reasons for this excellent prognosis are poorly understood. Objective To investigate the natural history of PTC in young patients, we compared the observed tumor volume-doubling rate (TV-DR) with the hypothetical tumor volume-doubling rate (hTV-DR) before presentation in young PTC patients. DR is an inverse of the doubling time and indicates the number of doublings that occur in a unit of time. A negative value indicates the number of times the volume is reduced by half per unit time. Methods We enrolled 20 patients with the following characteristics: age ≤19 years, diagnosed with PTC according to the cytology results between 2013 and 2018 and followed-up with periodical ultrasound examinations for ≥3 months before surgery for various reasons. Seventeen patients later underwent surgery confirming the diagnosis. We calculated TV-DRs using serial measurements of tumor diameters after presentation and hTV-DRs using tumor diameters and patients' age at presentation, assuming that a single cancer cell was present at the patient's birth and that the tumor grew at a constant rate. These values indicate the lowest growth rates necessary for a single cancer cell to achieve the full tumor size at presentation. Results Thirteen patients had positive TV-DRs (/year) ranging from 0.09 to 1.89, indicating tumor growth, and the remaining seven patients had negative values (−0.08 to −1.21), indicating regression. The median TV-DR was 0.29. The hTV-DRs (1.48–2.66, median 1.71) were significantly larger than the TV-DRs (p < 0.001), indicating much faster growth before presentation. Conclusions These data suggest that deceleration of tumor growth had already occurred at presentation in the majority of the cases. This might explain why disease-specific survival is excellent despite frequent findings of advanced disease in young patients with PTC.
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Le TTT, Adler FR. Is mammography screening beneficial: An individual-based stochastic model for breast cancer incidence and mortality. PLoS Comput Biol 2020; 16:e1008036. [PMID: 32628726 PMCID: PMC7365474 DOI: 10.1371/journal.pcbi.1008036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/16/2020] [Accepted: 06/09/2020] [Indexed: 11/18/2022] Open
Abstract
The benefits of mammography screening have been controversial, with conflicting findings from various studies. We hypothesize that unmeasured heterogeneity in tumor aggressiveness underlies these conflicting results. Based on published data from the Canadian National Breast Screening Study (CNBSS), we develop and parameterize an individual-based mechanistic model for breast cancer incidence and mortality that tracks five stages of breast cancer progression and incorporates the effects of age on breast cancer incidence and all-cause mortality. The model accurately reproduces the reported outcomes of the CNBSS. By varying parameters, we predict that the benefits of mammography depend on the effectiveness of cancer treatment and tumor aggressiveness. In particular, patients with the most rapidly growing or potentially largest tumors have the highest benefit and least harm from the screening, with only a relatively small effect of age. However, the model predicts that confining mammography to populations with a high risk of acquiring breast cancer increases the screening benefit only slightly compared with the full population.
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Affiliation(s)
- Thuy T. T. Le
- Department of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, Utah, United States of America
- Department of Health Management and Policy, School of Public Health, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Frederick R. Adler
- Department of Mathematics and School of Biological Sciences, University of Utah, Salt Lake City, Utah, United States of America
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Engel J, Schubert-Fritschle G, Emeny R, Hölzel D. Breast cancer: are long-term and intermittent endocrine therapies equally effective? J Cancer Res Clin Oncol 2020; 146:2041-2049. [PMID: 32472445 PMCID: PMC7324413 DOI: 10.1007/s00432-020-03264-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 05/14/2020] [Indexed: 12/29/2022]
Abstract
Purpose In breast cancer (BC), the duration of endocrine adjuvant therapies (AT) has been extended continuously up to 10 years. We present an alternative explanation for the effect, which could enable shorter treatments. Method The relevant literature on chemoprevention and (neo-)adjuvant therapy was reviewed. Data for initiation and growth of primary and contralateral BCs and their metastases (MET) were considered. Also, population-based data from the Munich Cancer Registry for MET-free survival, time trends of MET patterns, and survival achieved by improved ATs are used to estimate all events in the long-term follow-up. Results Extended ATs (EAT) that continue after 1, 2, or 5 years reduce mortality only slightly. The effect is delayed, occurring more than 5 years after extension. EATs does not affect the prognosis of 1stBCs, they preventively eradicate contralateral 2ndBCs and thus their future life-threatening METs. Because chemoprevention can eradicate BCs from the smallest clusters to almost detectable BCs, ATs can be temporarily suspended without imposing harm. Results equal to EATs can be achieved by short-term ATs of the 1stBC and by repeated neo-ATs targeted at the indefinitely developing 2ndBCs. Considering this potential in de-escalation, a 70–80% reduction of overtreatment seems possible. Conclusion Knowledge of initiation and growth of tumors with known effects of neo-ATs suggest that intermittent endocrine ATs may achieve the same results as EATs but with improved quality of life and survival because of fewer side effects and better compliance. The challenge for developments of repeated ATs becomes: how short is short enough.
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Affiliation(s)
- Jutta Engel
- Munich Cancer Registry (MCR), Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), 81377, Munich, Germany
| | - Gabriele Schubert-Fritschle
- Munich Cancer Registry (MCR), Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), 81377, Munich, Germany
| | - Rebecca Emeny
- The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Lebanon, NH, 03756, USA
| | - Dieter Hölzel
- Munich Cancer Registry (MCR), Institute for Medical Information Processing, Biometry and Epidemiology (IBE), Ludwig-Maximilians-University (LMU), 81377, Munich, Germany.
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Butner JD, Fuentes D, Ozpolat B, Calin GA, Zhou X, Lowengrub J, Cristini V, Wang Z. A Multiscale Agent-Based Model of Ductal Carcinoma In Situ. IEEE Trans Biomed Eng 2020; 67:1450-1461. [PMID: 31603768 PMCID: PMC8445608 DOI: 10.1109/tbme.2019.2938485] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
OBJECTIVE we present a multiscale agent-based model of Ductal Carcinoma in Situ (DCIS) in order to gain a detailed understanding of the cell-scale population dynamics, phenotypic distributions, and the associated interplay of important molecular signaling pathways that are involved in DCIS ductal invasion into the duct cavity (a process we refer to as duct advance rate here). METHODS DCIS is modeled mathematically through a hybridized discrete cell-scale model and a continuum molecular scale model, which are explicitly linked through a bidirectional feedback mechanism. RESULTS we find that duct advance rates occur in two distinct phases, characterized by an early exponential population expansion, followed by a long-term steady linear phase of population expansion, a result that is consistent with other modeling work. We further found that the rates were influenced most strongly by endocrine and paracrine signaling intensity, as well as by the effects of cell density induced quiescence within the DCIS population. CONCLUSION our model analysis identified a complex interplay between phenotypic diversity that may provide a tumor adaptation mechanism to overcome proliferation limiting conditions, allowing for dynamic shifts in phenotypic populations in response to variation in molecular signaling intensity. Further, sensitivity analysis determined DCIS axial advance rates and calcification rates were most sensitive to cell cycle time variation. SIGNIFICANCE this model may serve as a useful tool to study the cell-scale dynamics involved in DCIS initiation and intraductal invasion, and may provide insights into promising areas of future experimental research.
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Identification of human peripheral blood monocyte gene markers for early screening of solid tumors. PLoS One 2020; 15:e0230905. [PMID: 32226026 PMCID: PMC7105127 DOI: 10.1371/journal.pone.0230905] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2019] [Accepted: 03/12/2020] [Indexed: 01/12/2023] Open
Abstract
As cancer mortality is high in most regions of the world, early screening of cancer has become increasingly important. Minimally invasive screening programs that use peripheral blood mononuclear cells (PBMCs) are a new and reliable strategy that can achieve early detection of tumors by identifying marker genes. From 797 datasets, four (GSE12771, GSE24536, GSE27562, and GSE42834) including 428 samples, 236 solid tumor cases, and 192 healthy controls were chosen according to the inclusion criteria. A total of 285 genes from among 440 reported genes were selected by meta-analysis. Among them, 4 of the top significantly differentially expressed genes (ANXA1, IFI44, IFI44L, and OAS1) were identified as marker genes of PBMCs. Pathway enrichment analysis identified, two significant pathways, the 'primary immunodeficiency' pathway and the 'cytokine-cytokine receptor interaction' pathway. Protein- protein interaction (PPI) network analysis revealed the top 27 hubs with a degree centrality greater than 23 to be hub genes. We also identified 3 modules in Molecular Complex Detection (MCODE) analysis: Cluster 1 (related to ANXA1), Cluster 2 (related to IFI44 and IFI44L) and Cluster 3 (related to OAS1). Among the 4 marker genes, IFI44, IFI44L, and OAS1 are potential diagnostic biomarkers, even though their results were not as remarkable as those for ANXA1 in our study. ANXA1 is involved in the immunosuppressive mechanism in tumor-bearing hosts and may be used in a new strategy involving the use of the host's own immunity to achieve tumor suppression.
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Engel J, Weichert W, Jung A, Emeny R, Hölzel D. Lymph node infiltration, parallel metastasis and treatment success in breast cancer. Breast 2019; 48:1-6. [DOI: 10.1016/j.breast.2019.07.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Revised: 07/28/2019] [Accepted: 07/31/2019] [Indexed: 02/05/2023] Open
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Fiala C, Kulasingam V, Diamandis EP. Circulating Tumor DNA for Early Cancer Detection. J Appl Lab Med 2019; 3:300-313. [PMID: 33636948 DOI: 10.1373/jalm.2018.026393] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 05/22/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND Cancer cells release circulating tumor DNA (ctDNA) into the bloodstream, which can now be quantified and examined using novel high-throughput sequencing technologies. This has led to the emergence of the "liquid biopsy," which proposes to analyze this genetic material and extract information on a patient's cancer using a simple blood draw. CONTENT ctDNA has been detected in many advanced cancers. It has also been proven to be a highly sensitive indicator of relapse and prognosis. Sequencing the genetic material has also led to the discovery of mutations targetable by existing therapies. Although ctDNA screening is more expensive, it is showing promise against circulating tumor cells and traditional cancer biomarkers. ctDNA has also been detected in other bodily fluids, including cerebrospinal fluid, urine, saliva, and stool. The utility of ctDNA for early cancer detection is being studied. However, a blood test for cancer faces heavy obstacles, such as extremely low ctDNA concentrations in early-stage disease and benign mutations caused by clonal hematopoiesis, causing both sensitivity and specificity concerns. Nonetheless, companies and academic laboratories are highly active in developing such a test. CONCLUSION Currently, ctDNA is unlikely to perform at the high level of sensitivity and specificity required for early diagnosis and population screening. However, ctDNA in blood and other fluids has important clinical applications for cancer monitoring, prognosis, and selection of therapy that require further investigation.
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Affiliation(s)
- Clare Fiala
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Vathany Kulasingam
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Clinical Biochemistry, University Health Network, Toronto, Ontario, Canada
| | - Eleftherios P Diamandis
- Department of Pathology and Laboratory Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada.,Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Department of Clinical Biochemistry, University Health Network, Toronto, Ontario, Canada
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Strandberg JR, Humphreys K. Statistical models of tumour onset and growth for modern breast cancer screening cohorts. Math Biosci 2019; 318:108270. [PMID: 31627176 DOI: 10.1016/j.mbs.2019.108270] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 10/02/2019] [Accepted: 10/03/2019] [Indexed: 11/26/2022]
Abstract
Historically, multi-state Markov models have been used to study breast cancer incidence and mammography screening effectiveness. In recent years, more biologically motivated continuous tumour growth models have emerged as alternatives. However, a number of challenges remain for these models to make use of the wealth of information available in large mammography cohort data. In particular, methodology is needed to address random left truncation and individual, asynchronous screening. We present a comprehensive continuous random effects model for the natural history of breast cancer. It models the unobservable processes of tumour onset, tumour growth, screening sensitivity, and symptomatic detection. We show how the unknown model parameter values can be jointly estimated using a prospective cohort with diagnostic data on age and tumour size at diagnosis, and individual screening histories. We also present a microsimulation study calibrated to population breast cancer incidence data, and to data on mode of detection and tumour size. We highlight the importance of adjusting for random left truncation, derive tumour doubling time distributions for screen-detected and interval cancers, and present results concerning the relationship between tumour presence time and age at diagnosis.
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Affiliation(s)
- J Rickard Strandberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Solna SE-171 77, Sweden.
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Box 281, Solna SE-171 77, Sweden
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Bhattarai S, Klimov S, Aleskandarany MA, Burrell H, Wormall A, Green AR, Rida P, Ellis IO, Osan RM, Rakha EA, Aneja R. Machine learning-based prediction of breast cancer growth rate in vivo. Br J Cancer 2019; 121:497-504. [PMID: 31395950 PMCID: PMC6738119 DOI: 10.1038/s41416-019-0539-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 07/07/2019] [Accepted: 07/11/2019] [Indexed: 01/04/2023] Open
Abstract
Background Determining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen. Methods A serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort. Results SM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours. Conclusion Our Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications.
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Affiliation(s)
- Shristi Bhattarai
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA
| | - Sergey Klimov
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA
| | - Mohammed A Aleskandarany
- Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital Campus, Nottingham, NG5 1PB, UK
| | - Helen Burrell
- Nottingham Breast Institute, Nottingham University Hospitals NHS Trust, Nottingham City hospital, Nottingham, NG5 1PB, UK
| | - Anthony Wormall
- Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital Campus, Nottingham, NG5 1PB, UK
| | - Andrew R Green
- Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital Campus, Nottingham, NG5 1PB, UK
| | - Padmashree Rida
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA
| | - Ian O Ellis
- Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital Campus, Nottingham, NG5 1PB, UK
| | - Remus M Osan
- Mathematics and Statistics, Georgia State University, Atlanta, GA, 30303, USA
| | - Emad A Rakha
- Nottingham Breast Cancer Research Centre, Division of Cancer and Stem Cells, School of Medicine, University of Nottingham and Nottingham University Hospitals NHS Trust, City Hospital Campus, Nottingham, NG5 1PB, UK.
| | - Ritu Aneja
- Department of Biology, Georgia State University, Atlanta, GA, 30303, USA.
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Alonzo-Proulx O, Mainprize JG, Harvey JA, Yaffe MJ. Investigating the feasibility of stratified breast cancer screening using a masking risk predictor. Breast Cancer Res 2019; 21:91. [PMID: 31399056 PMCID: PMC6688203 DOI: 10.1186/s13058-019-1179-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 07/26/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Women with dense breasts face a double risk for breast cancer; they are at a higher risk for development of breast cancer than those with less dense breasts, and there is a greater chance that mammography will miss detection of a cancer in dense breasts due to the masking effect of surrounding fibroglandular tissue. These women may be candidates for supplemental screening. In this study, a masking risk model that was previously developed is tested on a cohort of cancer-free women to assess potential efficiency of stratification. METHODS Three masking risk models based on (1) BI-RADS density, (2) volumetric breast density (VBD), and (3) a combination of VBD and detectability were applied to stratify the mammograms of 1897 cancer-free women. The fraction of cancer-free women whose mammograms were deemed by the algorithm to be masked and who would be considered for supplemental imaging was computed as was the corresponding fraction in a screened population of interval (masked) cancers that would be potentially detected by supplemental imaging. RESULTS Of the models tested, the combined VBD/detectability model offered the highest efficiency for stratification to supplemental imaging. It predicted that 725 supplemental screens would be performed per interval cancer potentially detected, at an operating point that allowed detection of 64% of the interval cancers. In comparison, stratification based on the upper two BI-RADS density categories required 1117 supplemental screenings per interval cancer detected to capture 64% of interval cancers. CONCLUSION The combined VBD/detectability models perform better than BI-RADS and offer a continuum of operating points, suggesting that this model may be effective in guiding a stratified screening environment.
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Affiliation(s)
- Olivier Alonzo-Proulx
- Physical Sciences, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
| | - James G. Mainprize
- Physical Sciences, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
| | - Jennifer A. Harvey
- Department of Radiology and Medical Imaging, University of Virginia Health System, Charlottesville, VA USA
| | - Martin J. Yaffe
- Physical Sciences, Sunnybrook Research Institute, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
- Department of Medical Biophysics, University of Toronto, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5 Canada
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Dos-Santos-Silva I, De Stavola BL, Renna NL, Nogueira MC, Aquino EML, Bustamante-Teixeira MT, Azevedo E Silva G. Ethnoracial and social trends in breast cancer staging at diagnosis in Brazil, 2001-14: a case only analysis. Lancet Glob Health 2019; 7:e784-e797. [PMID: 31097280 PMCID: PMC6527520 DOI: 10.1016/s2214-109x(19)30151-2] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 01/15/2019] [Accepted: 02/28/2019] [Indexed: 12/25/2022]
Abstract
BACKGROUND Policies for early detection of breast cancer, including clinical breast examinations and mammographic screening, were introduced in Brazil in 2004, but their effect on disease stage at diagnosis is unclear. We aimed to assess whether these policies have led to a decrease in the prevalence of late-stage breast cancer at diagnosis. METHODS In this case only analysis, using an anonymised nationwide hospital based-cancer registry network, we identified women aged 18-89 years who had been diagnosed with an invasive breast cancer in Brazil during 2001-14. We extracted individual patient-level data on patient demographics, tumour variables, and health-care provider variables for the centre where the patient was diagnosed. Our objectives were to estimate the prevalence of late-stage breast cancer (TNM stage III or IV) at diagnosis overall, across age groups, and by ethnoracial and social strata (ie, self-reported ethnoracial group, as white, black, brown, Asian, or Indigenous, and educational level, marital status, and region of residence) across the study period, and compare these estimates with international data from high-income countries (Norway and the USA). We used logistic regression to estimate odds ratios (ORs) for late-stage versus early-stage (TNM stage I or II) breast cancer at diagnosis in relation to relevant exposures, either minimally adjusted (for age, year of diagnosis, and region of residence) or fully adjusted (for all patient, tumour, and health-care provider variables). FINDINGS We identified 247 719 women who were diagnosed with invasive breast cancer between Jan 1, 2001, and Dec 31, 2014, with a mean age at diagnosis of 55·4 years (SD 13·3), of whom 36·2% (n=89 550) identified as white, 29·8% (n=73 826) as black or brown, and 0·7% (n=1639) as Asian or Indigenous. Prevalence of late-stage breast cancer at diagnosis remained high throughout 2001-14, at approximately 40%, was inversely associated with educational level (p value for linear trend <0·0001), and was higher for women who identified as black (minimally adjusted OR 1·61, 95% CI 1·53-1·70; fully adjusted OR 1·45, 95% CI 1·38-1·54) and brown (minimally adjusted OR 1·26, 95% CI 1·22-1·30; fully adjusted OR 1·18, 1·14-1·23) than those who identified as white. The predicted prevalence of late-stage cancer at diagnosis was highest for women who were black or brown with little or no formal education (48·8%, 95% CI 48·2-49·5) and lowest for women who were white with university education (29·4%, 28·2-30·6), but both these prevalences were higher than that of all women diagnosed with breast cancer in Norway before the introduction of mammography screening (ie, 16·3%, 95% CI 15·4%-17·2% in 1970-74). Similar ethnoracial and social patterns emerged in analyses restricted to the age group targeted by screening (50-69 years). INTERPRETATION The persistently high prevalence of late-stage breast cancer at diagnosis across all ethnoracial and social strata in Brazil, although more substantially among the most disadvantaged populations, implies that early detection policies might have had little effect on breast cancer mortality so far, and highlights the need to focus primarily on timely diagnosis of symptomatic breast cancer rather than on screening for asymptomatic disease. FUNDING Newton Fund, Research Councils UK, and Conselho Nacional das Fundações Estaduais de Amparo à Pesquisa.
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Affiliation(s)
- Isabel Dos-Santos-Silva
- Department of Non-Communicable Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK.
| | - Bianca L De Stavola
- Population, Policy and Practice Programme, University College London Great Ormond Street Institute of Child Health, London, UK
| | - Nelson L Renna
- Departamento de Epidemiologia, Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Mário C Nogueira
- Departamento de Saúde Coletiva, Faculdade de Medicina, Universidade Federal de Juíz de Fora, Juíz de Fora, Brazil
| | - Estela M L Aquino
- Instituto de Saúde Coletiva, Universidade Federal da Bahia, Salvador, Bahia, Brazil
| | | | - Gulnar Azevedo E Silva
- Departamento de Epidemiologia, Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brazil
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Grimm LJ, Miller MM, Thomas SM, Liu Y, Lo JY, Hwang ES, Hyslop T, Ryser MD. Growth Dynamics of Mammographic Calcifications: Differentiating Ductal Carcinoma in Situ from Benign Breast Disease. Radiology 2019; 292:77-83. [PMID: 31112087 DOI: 10.1148/radiol.2019182599] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Background Most ductal carcinoma in situ (DCIS) lesions are first detected on screening mammograms as calcifications. However, false-positive biopsy rates for calcifications range from 30% to 87%. Improved methods to differentiate benign from malignant calcifications are thus needed. Purpose To quantify the growth rates of DCIS and benign breast disease that manifest as mammographic calcifications. Materials and Methods All calcifications (n = 2359) for which a stereotactic biopsy was performed from 2008 through 2015 at Duke University Medical Center were retrospectively identified. Mammograms from all cases of DCIS (n = 404) were reviewed for calcifications that were visible on mammograms taken at least 6 months before biopsy. Women with at least one prior mammogram with visible calcifications were age- and race-matched 1:2 to women with a benign breast biopsy and calcifications visible on prior mammograms. The long axis of the calcifications was measured on all mammograms. Multivariable adjusted linear mixed-effects models estimated the association of calcification growth rates with patholo findings. Hierarchical clustering accounted for matching benign and DCIS groups. Results A total of 74 DCIS calcifications and 148 benign calcifications were included for final analysis. The median patient age was 62 years (interquartile range, 51-71 years). No significant difference in breast density (P > .05) or number of available mammograms (P > .05) was detected between groups. Calcifications associated with DCIS were larger than those associated with benign breast disease at biopsy (median, 10 mm vs 6 mm, respectively; P < .001). After adjustment, the relative annual increase in the long-axis length of DCIS calcifications was greater than that of benign breast calcifications (96% [95% confidence interval: 72%, 224%] vs 68% [95% confidence interval: 56%, 80%] per year, respectively; P < .001). Conclusion Ductal carcinoma in situ calcifications are more extensive at diagnosis and grow faster in extent than those associated with benign breast disease. The rate of calcification change may help to discriminate benign from malignant calcifications. © RSNA, 2019 Online supplemental material is available for this article.
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Affiliation(s)
- Lars J Grimm
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - Matthew M Miller
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - Samantha M Thomas
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - Yiling Liu
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - Joseph Y Lo
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - E Shelley Hwang
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - Terry Hyslop
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
| | - Marc D Ryser
- From the Departments of Radiology (L.J.G., J.Y.L.), Biostatistics & Bioinformatics (S.M.T., Y.L., T.H.), Surgery (E.S.H.), and Population Health Sciences (M.D.R.), Duke University Medical Center, 40 Duke Medicine Circle, DUMC Box 3808, Durham, NC 27710; and the Department of Radiology (M.M.M.), University of Virginia Health System, 1215 Lee St, Charlottesville, VA 22903
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Prediction of Cancer Masking in Screening Mammography Using Density and Textural Features. Acad Radiol 2019; 26:608-619. [PMID: 30100155 DOI: 10.1016/j.acra.2018.06.011] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2018] [Revised: 06/11/2018] [Accepted: 06/12/2018] [Indexed: 11/20/2022]
Abstract
RATIONALE AND OBJECTIVES High mammographic density reduces the diagnostic accuracy of screening mammography due to masking of tumors, resulting in possible delayed diagnosis and missed cancers. Women with high masking risk could be preselected for alternative screening regimens less susceptible to masking. In this study, various models to predict masking status are presented based on biometric and image-based parameters. MATERIALS AND METHODS For a cohort of 67 nonscreen-detected (cancers detected via other means after a negative mammogram) and 147 screen-detected invasive cancers, quantitative volumetric breast density, BI-RADS density, and the distribution and appearance of dense tissue through statistical and texture metrics were measured. Age and Body Mass Index were recorded. Stepwise multivariate logistic regressions were computed to select those parameters that predicted nonscreen-detected cancers. Accuracy of the models was evaluated using the area under receiver operator characteristic curve (AUC). RESULTS Using BI-RADS density alone to predict masking risk yielded an AUC of 0.64 (95% confidence interval [0.57-0.70]). Age-adjusted BI-RADS density or volumetric breast density had AUCs of 0.72 [0.64-0.79] and 0.71 [0.62-0.78], respectively. A model extracted from the full pool of variables had an AUC of 0.75 [0.67-0.82]. CONCLUSION The optimal model predicts masking more accurately than density alone, suggesting that texture metrics may be useful in models to guide a stratified screening strategy.
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Feng X, Li J, Li H, Chen H, Li F, Liu Q, You ZH, Zhou F. Age Is Important for the Early-Stage Detection of Breast Cancer on Both Transcriptomic and Methylomic Biomarkers. Front Genet 2019; 10:212. [PMID: 30984234 PMCID: PMC6448048 DOI: 10.3389/fgene.2019.00212] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2018] [Accepted: 02/27/2019] [Indexed: 12/27/2022] Open
Abstract
Patients at different ages have different rates of cell development and metabolisms. As a result, age should be an essential part of how a disease diagnosis model is trained and optimized. Unfortunately, most of the existing studies have not taken age into account. This study demonstrated that disease diagnosis models could be improved by merely applying individual models for patients of different age groups. Both transcriptomes and methylomes of the TCGA breast cancer dataset (TCGA-BRCA) were utilized for the analysis procedure of feature selection and classification. Our experimental data strongly suggested that disease diagnosis modeling should integrate patient age into the whole experimental design.
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Affiliation(s)
- Xin Feng
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Jialiang Li
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Han Li
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Hang Chen
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Fei Li
- BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Quewang Liu
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
| | - Zhu-Hong You
- Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Ürümqi, China
| | - Fengfeng Zhou
- BioKnow Health Informatics Lab, College of Computer Science and Technology, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China.,BioKnow Health Informatics Lab, College of Software, Jilin University, Changchun, China.,Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, China
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Abrahamsson L, Isheden G, Czene K, Humphreys K. Continuous tumour growth models, lead time estimation and length bias in breast cancer screening studies. Stat Methods Med Res 2019; 29:374-395. [DOI: 10.1177/0962280219832901] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Comparisons of survival times between screen-detected and symptomatically detected breast cancer cases are subject to lead time and length biases. Whilst the existence of these biases is well known, correction procedures for these are not always clear, as are not the interpretation of these biases. In this paper we derive, based on a recently developed continuous tumour growth model, conditional lead time distributions, using information on each individual's tumour size, screening history and percent mammographic density. We show how these distributions can be used to obtain an individual-based (conditional) procedure for correcting survival comparisons. In stratified analyses, our correction procedure works markedly better than a previously used unconditional lead time correction, based on multi-state Markov modelling. In a study of postmenopausal invasive breast cancer patients, we estimate that, in large (>12 mm) tumours, the multi-state Markov model correction over-corrects five-year survival by 2–3 percentage points. The traditional view of length bias is that tumours being present in a woman's breast for a long time, due to being slow-growing, have a greater chance of being screen-detected. This gives a survival advantage for screening cases which is not due to the earlier detection by screening. We use simulated data to share the new insight that, not only the tumour growth rate but also the symptomatic tumour size will affect the sampling procedure, and thus be a part of the length bias through any link between tumour size and survival. We explain how this has a bearing on how observable breast cancer-specific survival curves should be interpreted. We also propose an approach for correcting survival comparisons for the length bias.
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Affiliation(s)
- Linda Abrahamsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Gabriel Isheden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Isheden G, Abrahamsson L, Andersson T, Czene K, Humphreys K. Joint models of tumour size and lymph node spread for incident breast cancer cases in the presence of screening. Stat Methods Med Res 2019; 28:3822-3842. [PMID: 30606087 PMCID: PMC6745622 DOI: 10.1177/0962280218819568] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Continuous growth models show great potential for analysing cancer screening
data. We recently described such a model for studying breast cancer tumour
growth based on modelling tumour size at diagnosis, as a function of screening
history, detection mode, and relevant patient characteristics. In this article,
we describe how the approach can be extended to jointly model tumour size and
number of lymph node metastases at diagnosis. We propose a new class of lymph
node spread models which are biologically motivated and describe how they can be
extended to incorporate random effects to allow for heterogeneity in underlying
rates of spread. Our final model provides a dramatically better fit to empirical
data on 1860 incident breast cancer cases than models in current use. We
validate our lymph node spread model on an independent data set consisting of
3961 women diagnosed with invasive breast cancer.
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Affiliation(s)
- Gabriel Isheden
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Linda Abrahamsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Therese Andersson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
| | - Keith Humphreys
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden
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Utility of circulating tumor DNA in cancer diagnostics with emphasis on early detection. BMC Med 2018; 16:166. [PMID: 30285732 PMCID: PMC6167864 DOI: 10.1186/s12916-018-1157-9] [Citation(s) in RCA: 155] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2018] [Accepted: 08/17/2018] [Indexed: 02/06/2023] Open
Abstract
Various recent studies have focused on analyzing tumor genetic material released into the blood stream, known as circulating tumor DNA (ctDNA). Herein, we describe current research on the application of ctDNA to cancer management, including prognosis determination, monitoring for treatment efficacy/relapse, treatment selection, and quantification of tumor size and disease burden. Specifically, we examine the utility of ctDNA for early cancer diagnostics focusing on the development of a blood test to detect cancer in asymptomatic individuals by sequencing and analyzing mutations in ctDNA. Next, we discuss the prospect of using ctDNA to test for cancer, and present our calculations based on previously published empirical findings in cancer and prenatal diagnostics. We show that very early stage (asymptomatic) tumors are not likely to release enough ctDNA to be detectable in a typical blood draw of 10 mL. Data are also presented showing that mutations in circulating free DNA can be found in healthy individuals and will likely be very difficult to distinguish from those associated with cancer.We conclude that the ctDNA test, in addition to its high cost and complexity, will likely suffer from the same issues of low sensitivity and specificity as traditional biomarkers when applied to population screening and early (asymptomatic) cancer diagnosis.
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Abstract
BACKGROUND Even small delays in the treatment of breast cancer are a frequently expressed concern of patients. Knowledge about this subject is important for clinicians to counsel patients appropriately and realistically, while also optimizing care. Although data and quality measures regarding time to chemotherapy and radiotherapy have been present for some time, data regarding surgical care are more recent and no standard exists. This review was written to discuss our current knowledge about the relationship of treatment times to outcomes. METHODS The published medical literature addressing delays and optimal times to treatment was reviewed in the context of our current time-dependent standards for chemotherapy and radiotherapy. The surgical literature and the lack of a time-dependent surgical standard also were discussed, suggesting a possible standard. RESULTS Risk factors for delay are numerous, and tumor doubling times are both difficult to determine and unhelpful to assess the impact of longer treatment times on outcomes. Evaluation components also have a time cost and are inextricable from the patient's workup. Although the published literature has lack of uniformity, optimal times to each modality are strongly suggested by emerging data, supporting the current quality measures. Times to surgery, chemotherapy, and radiotherapy all have a measurable impact on outcomes, including disease-free survival, disease-specific survival, and overall survival. CONCLUSIONS Delays have less of an impact than often thought but have a measurable impact on outcomes. Optimal times from diagnosis are < 90 days for surgery, < 120 days for chemotherapy, and, where chemotherapy is administered, < 365 days for radiotherapy.
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Affiliation(s)
- Richard J Bleicher
- Department of Surgical Oncology, Room C-308, Fox Chase Cancer Center, Philadelphia, PA, USA.
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Does breast cancer growth rate really depend on tumor subtype? Measurement of tumor doubling time using serial ultrasonography between diagnosis and surgery. Breast Cancer 2018; 26:206-214. [PMID: 30259332 DOI: 10.1007/s12282-018-0914-0] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Accepted: 09/20/2018] [Indexed: 10/28/2022]
Abstract
BACKGROUND Breast cancer growth is generally expected to differ between tumor subtypes. We aimed to evaluate tumor doubling time (DT) using ultrasonography and verify whether each tumor subtype has a unique DT. METHODS This retrospective study included 265 patients with invasive breast cancer who received serial ultrasonography between diagnosis and surgery. Tumor diameters were measured in three directions and DTs were calculated according to an exponential growth model using the volume change during serial ultrasonography. We investigated the relationships between DT, tumor subtype, and histopathological factors. RESULTS Volumes did not change in 95 (36%) of 265 tumors and increased in 170 (64%) tumors during serial ultrasonography (mean interval, 56.9 days). The mean volume increases of all tumors and volume-increased tumors were 22.1% and 34.5%, respectively. Triple-negative tumors had greater volume increases (40% vs. 20%, p = 0.001) and shorter DT (124 vs. 185 days, p = 0.027) than estrogen receptor (ER)+/human epidermal growth factor receptor 2 (HER2)- tumors. Volume-increased tumors had higher Ki-67 indices than those of volume-stable tumors in ER+/HER2- (p = 0.002) and ER+/HER2+ tumors (p = 0.011) and higher histological grades in all tumors except triple-negative tumors (p < 0.001). Triple-negative tumors with DTs < 90 days (short-DT) showed higher Ki-67 indices than those with DTs > 90 days (long-DT) (p = 0.008). In ER+/HER2- tumors, histological grades were higher for short-DT than for long-DT tumors (p = 0.022). CONCLUSION Differences in tumor DT depending on breast cancer subtype, Ki-67 index, and histological grade were confirmed using serial ultrasonography even during preoperative short interval.
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A'Hern R. Cancer Biology and Survival Analysis in Cancer Trials: Restricted Mean Survival Time Analysis versus Hazard Ratios. Clin Oncol (R Coll Radiol) 2018; 30:e75-e80. [DOI: 10.1016/j.clon.2018.04.011] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2017] [Revised: 03/16/2018] [Accepted: 04/13/2018] [Indexed: 10/28/2022]
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Tyuryumina EY, Neznanov AA. Consolidated mathematical growth model of the primary tumor and secondary distant metastases of breast cancer (CoMPaS). PLoS One 2018; 13:e0200148. [PMID: 29979733 PMCID: PMC6034839 DOI: 10.1371/journal.pone.0200148] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2018] [Accepted: 06/20/2018] [Indexed: 11/28/2022] Open
Abstract
The goal of this research is to improve the accuracy of predicting the breast cancer (BC) process using the original mathematical model referred to as CoMPaS. The CoMPaS is the original mathematical model and the corresponding software built by modelling the natural history of the primary tumor (PT) and secondary distant metastases (MTS), it reflects the relations between the PT and MTS. The CoMPaS is based on an exponential growth model and consists of a system of determinate nonlinear and linear equations and corresponds to the TNM classification. It allows us to calculate the different growth periods of PT and MTS: 1) a non-visible period for PT, 2) a non-visible period for MTS, and 3) a visible period for MTS. The CoMPaS has been validated using 10-year and 15-year survival clinical data considering tumor stage and PT diameter. The following are calculated by CoMPaS: 1) the number of doublings for the non-visible and visible growth periods of MTS and 2) the tumor volume doubling time (days) for the non-visible and visible growth periods of MTS. The diameters of the PT and secondary distant MTS increased simultaneously. In other words, the non-visible growth period of the secondary distant MTS shrinks, leading to a decrease of the survival of patients with breast cancer. The CoMPaS correctly describes the growth of the PT for patients at the T1aN0M0, T1bN0M0, T1cN0M0, T2N0M0 and T3N0M0 stages, who does not have MTS in the lymph nodes (N0). Additionally, the CoMPaS helps to consider the appearance and evolution period of secondary distant MTS (M1). The CoMPaS correctly describes the growth period of PT corresponding to BC classification (parameter T), the growth period of secondary distant MTS and the 10-15-year survival of BC patients considering the BC stage (parameter M).
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Affiliation(s)
- Ella Ya. Tyuryumina
- International Laboratory for Intelligent Systems and Structural Analysis, Faculty of Computer science, National Research University Higher School of Economics, Moscow, Russia
- * E-mail:
| | - Alexey A. Neznanov
- International Laboratory for Intelligent Systems and Structural Analysis, Faculty of Computer science, National Research University Higher School of Economics, Moscow, Russia
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49
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Factors Contributing to Late-Stage Breast Cancer Presentation in sub-Saharan Africa. CURRENT BREAST CANCER REPORTS 2018. [DOI: 10.1007/s12609-018-0278-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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50
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Jiang L, Gilbert J, Langley H, Moineddin R, Groome PA. Is being diagnosed at a dedicated breast assessment unit associated with a reduction in the time to diagnosis for symptomatic breast cancer patients? Eur J Cancer Care (Engl) 2018; 27:e12864. [PMID: 29873137 DOI: 10.1111/ecc.12864] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/17/2018] [Indexed: 11/27/2022]
Abstract
The length of the cancer diagnostic interval can affect a patient's survival and psychosocial well-being. Ontario Diagnostic Assessment Units (DAUs) were designed to expedite the diagnostic process through coordinated care. We examined the effect of DAUs on the diagnostic interval among female patients with symptomatic breast cancer in Ontario using the Ontario Cancer Registry linked to administrative healthcare data. The diagnostic interval was defined as the time from patients' first referral or test to the cancer diagnosis. DAU use was determined based on the hospital where the breast biopsy/surgery was performed. Multivariable quantile regression and logistic regression analyses adjusted for possible confounders. Forty-seven per cent of patients were diagnosed in a DAU and 53% in usual care (UC). DAUs achieved the Canadian timeliness targets more often than UC (71.7% vs. 58.1%, respectively). DAU use was associated with a 10-day (95% CI: 7.8-11.9) reduction in the median diagnostic interval. This effect increased to 19 days for patients at the 75th percentile and 22 days for those at the 90th percentile of the diagnostic interval distribution. Use of an Ontario DAU is associated with a shorter time to diagnosis in patients with symptomatic breast cancer, especially for those who would otherwise wait the longest.
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Affiliation(s)
- Li Jiang
- Division of Cancer Care and Epidemiology, Cancer Research Institute at Queen's University, Kingston, ON, Canada.,Institute for Clinical Evaluative Sciences, Toronto, ON, Canada
| | | | - Hugh Langley
- South East Regional Cancer Program, Kingston General Hospital, Kingston, ON, Canada
| | - Rahim Moineddin
- Department of Family and Community Medicine, University of Toronto, Toronto, ON, Canada
| | - Patti A Groome
- Division of Cancer Care and Epidemiology, Cancer Research Institute at Queen's University, Kingston, ON, Canada.,Institute for Clinical Evaluative Sciences, Toronto, ON, Canada
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