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Sadeghipour N, Tseng J, Anderson K, Ayalasomayajula S, Kozlov A, Ikeda D, DeMartini W, Hori SS. Tumor volume doubling time estimated from digital breast tomosynthesis mammograms distinguishes invasive breast cancers from benign lesions. Eur Radiol 2022; 33:429-439. [PMID: 35779088 DOI: 10.1007/s00330-022-08966-2] [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: 10/28/2021] [Revised: 06/09/2022] [Accepted: 06/13/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVES The aim of this study was to determine whether lesion size metrics on consecutive screening mammograms could predict malignant invasive carcinoma versus benign lesion outcome. METHODS We retrospectively reviewed suspicious screen-detected lesions confirmed by biopsy to be invasive breast cancers or benign that were visible on current and in-retrospect prior screening mammograms performed with digital breast tomosynthesis from 2017 to 2020. Four experienced radiologists recorded mammogram dates, breast density, lesion type, lesion diameter, and morphology on current and prior exams. We used logistic regression models to evaluate the association of invasive breast cancer outcome with lesion size metrics such as maximum dimension, average dimension, volume, and tumor volume doubling time (TVDT). RESULTS Twenty-eight patients with invasive ductal carcinoma or invasive lobular carcinoma and 40 patients with benign lesions were identified. The mean TVDT was significantly shorter for invasive breast cancers compared to benign lesions (0.84 vs. 2.5 years; p = 0.0025). Patients with a TVDT of less than 1 year were shown to have an odds ratio of invasive cancer of 6.33 (95% confidence interval, 2.18-18.43). Logistic regression adjusted for age, lesion maximum dimension, and lesion volume demonstrated that shorter TVDT was the size variable significantly associated with invasive cancer outcome. CONCLUSION Invasive breast cancers detected on current and in-retrospect prior screening mammograms are associated with shorter TVDT compared to benign lesions. If confirmed to be sufficiently predictive of benignity in larger studies, lesions visible on mammograms which in comparison to prior exams have longer TVDTs could potentially avoid additional imaging and/or biopsy. KEY POINTS • We propose tumor volume doubling time as a measure to distinguish benign from invasive breast cancer lesions. • Logistic regression results summarized the utility of the odds ratio in retrospective clinical mammography data.
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Affiliation(s)
- Negar Sadeghipour
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,The Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA.,Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph Tseng
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Kristen Anderson
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,The Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Shivani Ayalasomayajula
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,The Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Andrew Kozlov
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA.,The University of South Florida Morsani College of Medicine, Tampa, FL, USA
| | - Debra Ikeda
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Wendy DeMartini
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon S Hori
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, USA. .,The Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, CA, USA. .,Molecular Imaging Program at Stanford (MIPS), Stanford University School of Medicine, Stanford, CA, USA.
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Overdetection of Breast Cancer. Curr Oncol 2022; 29:3894-3910. [PMID: 35735420 PMCID: PMC9222123 DOI: 10.3390/curroncol29060311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/16/2022] [Accepted: 05/17/2022] [Indexed: 11/25/2022] Open
Abstract
Overdetection (often referred to as overdiagnosis) of cancer is the detection of disease, such as through a screening program, that would otherwise remain occult through an individual’s life. In the context of screening, this could occur for cancers that were slow growing or indolent, or simply because an unscreened individual would have died from some other cause before the cancer had surfaced clinically. The main harm associated with overdetection is the subsequent overdiagnosis and overtreatment of disease. In this article, the phenomenon is reviewed, the methods of estimation of overdetection are discussed and reasons for variability in such estimates are given, with emphasis on an analysis using Canadian data. Microsimulation modeling is used to illustrate the expected time course of cancer detection that gives rise to overdetection. While overdetection exists, the actual amount is likely to be much lower than the estimate used by the Canadian Task Force on Preventive Health Care. Furthermore, the issue is of greater significance in older rather than younger women due to competing causes of death. The particular challenge associated with in situ breast cancer is considered and possible approaches to avoiding overtreatment are suggested.
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Anderson S, Parker E, Rahbar H, Scheel JR. IV Ductal Carcinoma In Situ, Including its Histologic Subtypes and Grades. CURRENT BREAST CANCER REPORTS 2021. [DOI: 10.1007/s12609-021-00439-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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4
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Siviengphanom S, Gandomkar Z, Lewis SJ, Brennan PC. Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs. Acad Radiol 2021; 29:1228-1247. [PMID: 34799256 DOI: 10.1016/j.acra.2021.09.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 09/14/2021] [Accepted: 09/26/2021] [Indexed: 12/19/2022]
Abstract
RATIONALE AND OBJECTIVES Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival. MATERIALS AND METHODS PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria. RESULTS Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed. CONCLUSION The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool.
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Affiliation(s)
- Somphone Siviengphanom
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia..
| | - Ziba Gandomkar
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia
| | - Sarah J Lewis
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia
| | - Patrick C Brennan
- Discipline of Medical Imaging Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health, University of Sydney, Level 7, Susan Wakil Health Building D18, Sydney, NSW 2006, Australia
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5
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Moyya PD, Asaithambi M. Radiomics- Quantitative Biomarker Analysis for Breast Cancer Diagnosis and Prediction: A Review. Curr Med Imaging 2021; 18:3-17. [PMID: 33655872 DOI: 10.2174/1573405617666210303102526] [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: 10/28/2020] [Revised: 01/06/2021] [Accepted: 01/14/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Cancer of the breast has become a global problem for women's health. Though concerns regarding early detection and accurate diagnosis were raised, an effort is required for precision medicine as well as personalized treatment. In the past years, the area of medicinal imaging has seen an unprecedented growth that leads to an advancement of radiomics, which provides countless quantitative biomarkers extracted from modern diagnostic images, including a detailed tumor characterization of breast malignancy. DISCUSSION In this research, we presented the methodology and implementation of radiomics, together with its future trends and challenges by the basis of published papers. Radiomics could distinguish between malignant from benign tumors, predict prognostic factors, molecular subtypes of breast carcinoma, treatment response to neoadjuvant chemotherapy (NAC), and recurrence survival. The incorporation of quantitative knowledge with clinical, histopathological and genomic information will enable physicians to afford customized care of treatment for patients with breast cancer. CONCLUSION Our research was intended to help physicians and radiologists learn fundamental knowledge about radiomics and also to work collaboratively with researchers to explore evidence for further usage in clinical practice.
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Affiliation(s)
- Priscilla Dinkar Moyya
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014. India
| | - Mythili Asaithambi
- School of Electronics Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu-632014. India
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6
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Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E. Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers (Basel) 2020; 12:cancers12123663. [PMID: 33297357 PMCID: PMC7762258 DOI: 10.3390/cancers12123663] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 12/18/2022] Open
Abstract
(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.
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Affiliation(s)
- Charlems Alvarez-Jimenez
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Alvaro A. Sandino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Correspondence:
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7
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Lee SH, Park H, Ko ES. Radiomics in Breast Imaging from Techniques to Clinical Applications: A Review. Korean J Radiol 2020; 21:779-792. [PMID: 32524780 PMCID: PMC7289696 DOI: 10.3348/kjr.2019.0855] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 01/31/2020] [Accepted: 02/28/2020] [Indexed: 02/06/2023] Open
Abstract
Recent advances in computer technology have generated a new area of research known as radiomics. Radiomics is defined as the high throughput extraction and analysis of quantitative features from imaging data. Radiomic features provide information on the gray-scale patterns, inter-pixel relationships, as well as shape and spectral properties of radiological images. Moreover, these features can be used to develop computational models that may serve as a tool for personalized diagnosis and treatment guidance. Although radiomics is becoming popular and widely used in oncology, many problems such as overfitting and reproducibility issues remain unresolved. In this review, we will outline the steps of radiomics used for oncology, specifically addressing applications for breast cancer patients and focusing on technical issues.
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Affiliation(s)
- Seung Hak Lee
- Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea.,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
| | - Eun Sook Ko
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
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8
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Rahbar H, Lee JM, Lee CI. Optimal Screening in Breast Cancer Survivors With Dense Breasts on Mammography. J Clin Oncol 2020; 38:3833-3840. [PMID: 32706641 PMCID: PMC7676885 DOI: 10.1200/jco.20.01641] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/22/2020] [Indexed: 12/16/2022] Open
Abstract
The Oncology Grand Rounds series is designed to place original reports published in the Journal into clinical context. A case presentation is followed by a description of diagnostic and management challenges, a review of the relevant literature, and a summary of the authors' suggested management approaches. The goal of this series is to help readers better understand how to apply the results of key studies, including those published in Journal of Clinical Oncology, to patients seen in their own clinical practice.
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Affiliation(s)
- Habib Rahbar
- Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, Seattle, WA
| | - Janie M. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, Seattle, WA
| | - Christoph I. Lee
- Department of Radiology, University of Washington School of Medicine, Seattle Cancer Care Alliance, Seattle, WA
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9
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Shehata M, Grimm L, Ballantyne N, Lourenco A, Demello LR, Kilgore MR, Rahbar H. Ductal Carcinoma in Situ: Current Concepts in Biology, Imaging, and Treatment. JOURNAL OF BREAST IMAGING 2019; 1:166-176. [PMID: 31538141 DOI: 10.1093/jbi/wbz039] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Indexed: 12/27/2022]
Abstract
Ductal carcinoma in situ (DCIS) of the breast is a group of heterogeneous epithelial proliferations confined to the milk ducts that nearly always present in asymptomatic women on breast cancer screening. A stage 0, preinvasive breast cancer, increased detection of DCIS was initially hailed as a means to prevent invasive breast cancer through surgical treatment with adjuvant radiation and/or endocrine therapies. However, controversy in the medical community has emerged in the past two decades that a fraction of DCIS represents overdiagnosis, leading to unnecessary treatments and resulting morbidity. The imaging hallmarks of DCIS include linearly or segmentally distributed calcifications on mammography or nonmass enhancement on breast MRI. Imaging features have been shown to reflect the biological heterogeneity of DCIS lesions, with recent studies indicating MRI may identify a greater fraction of higher-grade lesions than mammography does. There is strong interest in the surgical, imaging, and oncology communities to better align DCIS management with biology, which has resulted in trials of active surveillance and therapy that is less aggressive. However, risk stratification of DCIS remains imperfect, which has limited the development of precision therapy approaches matched to DCIS aggressiveness. Accordingly, there are opportunities for breast imaging radiologists to assist the oncology community by leveraging advanced imaging techniques to identify appropriate patients for the less aggressive DCIS treatments.
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Affiliation(s)
- Mariam Shehata
- University of Washington School of Medicine, Department of Radiology, Seattle, WA
| | - Lars Grimm
- Duke University Medical School, Department of Radiology, Durham, NC
| | - Nancy Ballantyne
- Duke University Medical School, Department of Radiology, Durham, NC
| | - Ana Lourenco
- Brown University Medical School, Department of Radiology, Providence, RI
| | - Linda R Demello
- Brown University Medical School, Department of Radiology, Providence, RI
| | - Mark R Kilgore
- University of Washington School of Medicine, Department of Anatomic Pathology, Seattle, WA.,Seattle Cancer Care Alliance, Seattle, WA
| | - Habib Rahbar
- University of Washington School of Medicine, Department of Radiology, Seattle, WA.,Seattle Cancer Care Alliance, Seattle, WA
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10
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Whitney HM, Taylor NS, Drukker K, Edwards AV, Papaioannou J, Schacht D, Giger ML. Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset. Acad Radiol 2019; 26:202-209. [PMID: 29754995 DOI: 10.1016/j.acra.2018.04.019] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2017] [Revised: 03/23/2018] [Accepted: 04/19/2018] [Indexed: 10/16/2022]
Abstract
RATIONALE AND OBJECTIVES The objective of this study was to demonstrate improvement in distinguishing between benign lesions and luminal A breast cancers in a large clinical breast magnetic resonance imaging database by using quantitative radiomics over maximum linear size alone. MATERIALS AND METHODS In this retrospective study, 264 benign lesions and 390 luminal A breast cancers were automatically segmented from dynamic contrast-enhanced breast magnetic resonance images. Thirty-eight radiomic features were extracted. Tenfold cross validation was performed to assess the ability to distinguish between lesions and cancers using maximum linear size alone and lesion signatures obtained with stepwise feature selection and a linear discriminant analysis classifier including and excluding size features. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit. RESULTS For maximum linear size alone, AUC and 95% confidence interval was 0.684 (0.642, 0.724) compared to 0.728 (0.687, 0.766) (P = 0.005) and 0.729 (0.689, 0.767) (P = 0.005) for lesion signature feature selection protocols including and excluding size features, respectively. The features of irregularity and entropy were chosen in all folds when size features were included and excluded. AUC for the radiomic signature using feature selection from all features was statistically equivalent to using feature selection from all features excluding size features, within an equivalence margin of 2%. CONCLUSIONS Inclusion of multiple radiomic features, automatically extracted from magnetic resonance images, in a lesion signature significantly improved the ability to distinguish between benign lesions and luminal A breast cancers, compared to using maximum linear size alone. The radiomic features of irregularity and entropy appear to play an important but not a solitary role within the context of feature selection and computer-aided diagnosis.
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Crivelli P, Ledda RE, Parascandolo N, Fara A, Soro D, Conti M. A New Challenge for Radiologists: Radiomics in Breast Cancer. BIOMED RESEARCH INTERNATIONAL 2018; 2018:6120703. [PMID: 30402486 PMCID: PMC6196984 DOI: 10.1155/2018/6120703] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 08/24/2018] [Accepted: 09/09/2018] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Over the last decade, the field of medical imaging experienced an exponential growth, leading to the development of radiomics, with which innumerable quantitative features are obtained from digital medical images, providing a comprehensive characterization of the tumor. This review aims to assess the role of this emerging diagnostic tool in breast cancer, focusing on the ability of radiomics to predict malignancy, response to neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk of recurrence. EVIDENCE ACQUISITION A literature search on PubMed and on Cochrane database websites to retrieve English-written systematic reviews, review articles, meta-analyses, and randomized clinical trials published from August 2013 up to July 2018 was carried out. RESULTS Twenty papers (19 retrospective and 1 prospective studies) conducted with different conventional imaging modalities were included. DISCUSSION The integration of quantitative information with clinical, histological, and genomic data could enable clinicians to provide personalized treatments for breast cancer patients. Current limitations of a routinely application of radiomics are represented by the limited knowledge of its basics concepts among radiologists and by the lack of efficient and standardized systems of feature extraction and data sharing.
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Affiliation(s)
- Paola Crivelli
- Department of Biomedical Sciences, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Roberta Eufrasia Ledda
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Nicola Parascandolo
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Alberto Fara
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Daniela Soro
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
| | - Maurizio Conti
- Department of Clinical and Experimental Medicine, Institute of Radiological Sciences, University of Sassari, Sassari, Italy
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12
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Penzias G, Singanamalli A, Elliott R, Gollamudi J, Shih N, Feldman M, Stricker PD, Delprado W, Tiwari S, Böhm M, Haynes AM, Ponsky L, Fu P, Tiwari P, Viswanath S, Madabhushi A. Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings. PLoS One 2018; 13:e0200730. [PMID: 30169514 PMCID: PMC6118356 DOI: 10.1371/journal.pone.0200730] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 07/02/2018] [Indexed: 12/29/2022] Open
Abstract
Translation of radiomics into the clinic may require a more comprehensive understanding of the underlying morphologic tissue characteristics they reflect. In the context of prostate cancer (PCa), some studies have correlated gross histological measurements of gland lumen, epithelium, and nuclei with disease appearance on MRI. Quantitative histomorphometry (QH), like radiomics for radiologic images, is the computer based extraction of features for describing tumor morphology on digitized tissue images. In this work, we attempt to establish the histomorphometric basis for radiomic features for prostate cancer by (1) identifying the radiomic features from T2w MRI most discriminating of low vs. intermediate/high Gleason score, (2) identifying QH features correlated with the most discriminating radiomic features previously identified, and (3) evaluating the discriminative ability of QH features found to be correlated with spatially co-localized radiomic features. On a cohort of 36 patients (23 for training, 13 for validation), Gabor texture features were identified as being most predictive of Gleason grade on MRI (AUC of 0.69) and gland lumen shape features were identified as the most predictive QH features (AUC = 0.75). Our results suggest that the PCa grade discriminability of Gabor features is a consequence of variations in gland shape and morphology at the tissue level.
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Affiliation(s)
- Gregory Penzias
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Asha Singanamalli
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Robin Elliott
- University Hospitals, Cleveland, OH, United States of America
| | - Jay Gollamudi
- University Hospitals, Cleveland, OH, United States of America
| | - Natalie Shih
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | - Michael Feldman
- Department of Pathology, University of Pennsylvania, Philadelphia, PA, United States of America
| | | | - Warick Delprado
- Douglass Hanly Moir Pathology, Macquarie Park, NSW, Australia
| | - Sarita Tiwari
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Maret Böhm
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Anne-Maree Haynes
- Garvan Institute of Medical Research/The Kinghorn Cancer Centre, Darlinghurst, NSW, Australia
| | - Lee Ponsky
- University Hospitals, Cleveland, OH, United States of America
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States of America
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Satish Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States of America
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13
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Harris EER. Precision Medicine for Breast Cancer: The Paths to Truly Individualized Diagnosis and Treatment. Int J Breast Cancer 2018; 2018:4809183. [PMID: 29862084 PMCID: PMC5971283 DOI: 10.1155/2018/4809183] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2017] [Accepted: 03/12/2018] [Indexed: 12/27/2022] Open
Abstract
Precision medicine in oncology seeks to individualize each patient's treatment regimen based on an accurate assessment of the risk of recurrence or progression of that person's cancer. Precision will be achieved at each phase of care, from detection to diagnosis to surgery, systemic therapy, and radiation therapy, to survivorship and follow-up care. The precision arises from detailed knowledge of the inherent biological propensities of each tumor, rather than generalizing treatment approaches based on phenotypic, or even genotypic, categories. Extensive research is being conducted in multiple disciplines, including radiology, pathology, molecular biology, and surgical, medical, and radiation oncology. Clinical trial design is adapting to the new paradigms and moving away from grouping heterogeneous patient populations into limited treatment comparison arms. This review touches on several areas invested in clinical research. This special issue highlights the specific work of a number of groups working on precision medicine for breast cancer.
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Affiliation(s)
- Eleanor E. R. Harris
- Department of Radiation Oncology, Case Western Reserve University and University Hospitals, Cleveland, OH, USA
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14
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Díaz Candamio MJ, Jha S, Martel Villagrán J. Overdiagnosis in imaging. RADIOLOGIA 2018; 60:362-367. [PMID: 29685554 DOI: 10.1016/j.rx.2018.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 03/11/2018] [Accepted: 03/15/2018] [Indexed: 01/22/2023]
Abstract
Overdiagnosis, more than an error regarding the diagnosis, is an error regarding the prognosis. We cannot know what consequences some lesions that we detect by imaging would have on our patients' lives if they were left untreated. As long as it is not possible for imaging techniques to differentiate between lesions that will have an indolent course from those that will have an aggressive course, there will be overdiagnosis. Advanced imaging techniques, radiomics, and radiogenomics, together with artificial intelligence, promise advances in this sense. In the meantime, it is important that radiologists be careful to ensure that only strictly necessary imaging tests are done. Moreover, we need to participate, together with patients, in making multidisciplinary decisions about diagnosis and clinical management. Finally, of course, we need to continue to contribute to the technological and scientific advance of our profession, so that we can continue to improve the diagnosis and early detection of abnormalities, especially those that require treatment.
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Affiliation(s)
| | - S Jha
- Hospital of The University of Pennsylvania, Silverstein, Filadelfia, Estados Unidos
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15
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Wallis M. How do we manage overdiagnosis/overtreatment in breast screening? Clin Radiol 2018; 73:372-380. [DOI: 10.1016/j.crad.2017.09.016] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 09/18/2017] [Indexed: 12/18/2022]
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16
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Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer. PLoS One 2018; 13:e0193871. [PMID: 29596496 PMCID: PMC5875760 DOI: 10.1371/journal.pone.0193871] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 02/19/2018] [Indexed: 12/21/2022] Open
Abstract
In breast cancer, well-known gene expression subtypes have been related to a specific clinical outcome. However, their impact on the breast tissue phenotype has been poorly studied. Here, we investigate the association of imaging data of tumors to gene expression signatures from 71 patients with breast cancer that underwent pre-treatment digital mammograms and tumor biopsies. From digital mammograms, a semi-automated radiogenomics analysis generated 1,078 features describing the shape, signal distribution, and texture of tumors along their contralateral image used as control. From tumor biopsy, we estimated the OncotypeDX and PAM50 recurrence scores using gene expression microarrays. Then, we used multivariate analysis under stringent cross-validation to train models predicting recurrence scores. Few univariate features reached Spearman correlation coefficients above 0.4. Nevertheless, multivariate analysis yielded significantly correlated models for both signatures (correlation of OncotypeDX = 0.49 ± 0.07 and PAM50 = 0.32 ± 0.10 in stringent cross-validation and OncotypeDX = 0.83 and PAM50 = 0.78 for a unique model). Equivalent models trained from the unaffected contralateral breast were not correlated suggesting that the image signatures were tumor-specific and that overfitting was not a considerable issue. We also noted that models were improved by combining clinical information (triple negative status and progesterone receptor). The models used mostly wavelets and fractal features suggesting their importance to capture tumor information. Our results suggest that molecular-based recurrence risk and breast cancer subtypes have observable radiographic phenotypes. To our knowledge, this is the first study associating mammographic information to gene expression recurrence signatures.
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Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS. Rapid review: radiomics and breast cancer. Breast Cancer Res Treat 2018; 169:217-229. [PMID: 29396665 DOI: 10.1007/s10549-018-4675-4] [Citation(s) in RCA: 161] [Impact Index Per Article: 26.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2017] [Accepted: 01/16/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE To perform a rapid review of the recent literature on radiomics and breast cancer (BC). METHODS A rapid review, a streamlined approach to systematically identify and summarize emerging studies was done (updated 27 September 2017). Clinical studies eligible for inclusion were those that evaluated BC using a radiomics approach and provided data on BC diagnosis (detection or characterization) or BC prognosis (response to therapy, morbidity, mortality), or provided data on technical challenges (software application: open source, repeatability of results). Descriptive statistics, results, and radiomics quality score (RQS) are presented. RESULTS N = 17 retrospective studies, all published after 2015, provided BC-related radiomics data on 3928 patients evaluated with a radiomics approach. Most studies were done for diagnosis and/or characterization (65%, 11/17) or to aid in prognosis (41%, 7/17). The mean number of radiomics features considered was 100. Mean RQS score was 11.88 ± 5.8 (maximum value 36). The RQS criteria related to validation, gold standard, potential clinical utility, cost analysis, and open science data had the lowest scores. The majority of studies n = 16/17 (94%) provided correlation with histological outcomes and staging variables or biomarkers. Only 4/17 (23%) studies provided evidence of correlation with genomic data. Magnetic resonance imaging (MRI) was used in most studies n = 14/17 (82%); however, ultrasound (US), mammography, or positron emission tomography with 2-deoxy-2-[fluorine-18]fluoro-D-glucose integrated with computed tomography (18F FDG PET/CT) was also used. Much heterogeneity was found for software usage. CONCLUSIONS The study of radiomics in BC patients is a new and emerging translational research topic. Radiomics in BC is frequently done to potentially improve diagnosis and characterization, mostly using MRI. Substantial quality limitations were found; high-quality prospective and reproducible studies are needed to further potential application.
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Affiliation(s)
- Francesca Valdora
- Department of Health Sciences, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | - Nehmat Houssami
- Sydney School of Public Health, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Federica Rossi
- Department of Health Sciences, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy
| | | | - Alberto Stefano Tagliafico
- Department of Health Sciences, University of Genova, Via L.B. Alberti 2, 16132, Genoa, Italy. .,Ospedale Policlinico San Martino IST, Genoa, Italy.
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18
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Seidenwurm D, Breslau J. Recall Rate Benchmark for Screening Breast MR Imaging in Community Practice. Radiology 2018; 286:728-729. [PMID: 29356636 DOI: 10.1148/radiol.2017172354] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- David Seidenwurm
- Sutter Imaging, Sutter Medical Group, 1500 Expo Pkwy, Sacramento, CA 95819
| | - Jonathan Breslau
- Sutter Imaging, Sutter Medical Group, 1500 Expo Pkwy, Sacramento, CA 95819
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Chavez de Paz Villanueva C, Bonev V, Senthil M, Solomon N, Reeves ME, Garberoglio CA, Namm JP, Lum SS. Factors Associated With Underestimation of Invasive Cancer in Patients With Ductal Carcinoma In Situ: Precautions for Active Surveillance. JAMA Surg 2017; 152:1007-1014. [PMID: 28700803 DOI: 10.1001/jamasurg.2017.2181] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Importance Recent recognition of the overdiagnosis and overtreatment of ductal carcinoma in situ (DCIS) detected by mammography has led to the development of clinical trials randomizing women with non-high-grade DCIS to active surveillance, defined as imaging surveillance with or without endocrine therapy, vs standard surgical care. Objective To determine the factors associated with underestimation of invasive cancer in patients with a clinical diagnosis of non-high-grade DCIS that would preclude active surveillance. Design, Setting, and Participants A retrospective cohort study was conducted using records from the National Cancer Database from January 1, 1998, to December 31, 2012, of female patients 40 to 99 years of age with a clinical diagnosis of non-high-grade DCIS who underwent definitive surgical treatment. Data analysis was conducted from November 1, 2015, to February 4, 2017. Exposures Patients with an upgraded diagnosis of invasive carcinoma vs those with a diagnosis of DCIS based on final surgical pathologic findings. Main Outcomes and Measures The proportions of cases with an upgraded diagnosis of invasive carcinoma from final surgical pathologic findings were compared by tumor, host, and system characteristics. Results Of 37 544 women (mean [SD] age, 59.3 [12.4] years) presenting with a clinical diagnosis of non-high-grade DCIS, 8320 (22.2%) had invasive carcinoma based on final pathologic findings. Invasive carcinomas were more likely to be smaller (>0.5 to ≤1.0 cm vs ≤0.5 cm: odds ratio [OR], 0.73; 95% CI, 0.67-0.79; >1.0 to ≤2.0 cm vs ≤0.5 cm: OR, 0.42; 95% CI, 0.39-0.46; >2.0 to ≤5.0 cm vs ≤0.5 cm: OR, 0.19; 95% CI, 0.17-0.22; and >5.0 cm vs ≤0.5 cm: OR, 0.11; 95% CI, 0.08-0.15) and lower grade (intermediate vs low: OR, 0.75; 95% CI, 0.69-0.80). Multivariate logistic regression analysis demonstrated that younger age (60-79 vs 40-49 years: OR, 0.84; 95% CI, 0.77-0.92; and ≥80 vs 40 to 49 years: OR, 0.76; 95% CI, 0.64-0.91), negative estrogen receptor status (positive vs negative: OR, 0.39; 95% CI, 0.34-0.43), treatment at an academic facility (academic vs community: OR, 2.08; 95% CI, 1.82-2.38), and higher annual income (>$63 000 vs <$38 000: OR, 1.14; 95% CI, 1.02-1.28) were significantly associated with an upgraded diagnosis of invasive carcinoma based on final pathologic findings. Conclusions and Relevance When selecting patients for active surveillance of DCIS, factors other than tumor biology associated with invasive carcinoma based on final pathologic findings may need to be considered. At the time of randomization to active surveillance, a significant proportion of patients with non-high-grade DCIS will harbor invasive carcinoma.
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Affiliation(s)
| | - Valentina Bonev
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
| | - Maheswari Senthil
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
| | - Naveenraj Solomon
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
| | - Mark E Reeves
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
| | - Carlos A Garberoglio
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
| | - Jukes P Namm
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
| | - Sharon S Lum
- Division of Surgical Oncology, Department of Surgery, Loma Linda University School of Medicine, Loma Linda, California
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Narod S, Ahmed H, Sopik V. Wherein the authors attempt to minimize the confusion generated by their study "Breast cancer mortality after a diagnosis of ductal carcinoma in situ" by several commentators who disagree with them and a few who don't: a qualitative study. Curr Oncol 2017; 24:e255-e260. [PMID: 28874895 PMCID: PMC5576464 DOI: 10.3747/co.24.3626] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Various parties might wish to measure the impact of a given paper for the purpose of assigning merit to an author or institution [...]
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Affiliation(s)
- S.A. Narod
- Women’s College Research Institute
- Dalla Lana School of Public Health, University of Toronto; and
| | - H. Ahmed
- Women’s College Research Institute
- Institute of Medical Science, University of Toronto, Toronto; ON
| | - V. Sopik
- Women’s College Research Institute
- Institute of Medical Science, University of Toronto, Toronto; ON
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