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Hartman RI, Trepanowski N, Chang MS, Tepedino K, Gianacas C, McNiff JM, Fung M, Braghiroli NF, Grant-Kels JM. Multicenter prospective blinded melanoma detection study with a handheld elastic scattering spectroscopy device. JAAD Int 2024; 15:24-31. [PMID: 38371666 PMCID: PMC10869922 DOI: 10.1016/j.jdin.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 02/20/2024] Open
Abstract
Background The elastic scattering spectroscopy (ESS) device (DermaSensor Inc., Miami, FL) is a noninvasive, painless, adjunctive tool for skin cancer detection. Objectives To investigate the performance of the ESS device in the detection of melanoma. Methods A prospective, investigator-blinded, multicenter study was conducted at 8 United States (US) and 2 Australian sites. All eligible skin lesions were clinically concerning for melanoma, examined with the ESS device, subsequently biopsied according to dermatologists' standard of care, and evaluated with histopathology. A total of 311 participants with 440 lesions were enrolled, including 44 melanomas (63.6% in situ and 36.4% invasive) and 44 severely dysplastic nevi. Results The observed sensitivity of the ESS device for melanoma detection was 95.5% (95% CI, 84.5% to 98.8%, 42 of 44 melanomas), and the observed specificity was 32.5% (95% CI, 27.2% to 38.3%). The positive and negative predictive values were 16.0% and 98.1%, respectively. Limitations The device was tested in a high-risk population with lesions selected for biopsy based on clinical and dermoscopic assessments of board-certified dermatologists. Most enrolled lesions were pigmented. Conclusion The ESS device's high sensitivity and NPV for the detection of melanoma suggest the device may be a useful adjunctive, point-of-care tool for melanoma detection.
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Affiliation(s)
- Rebecca I. Hartman
- Department of Dermatology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Department of Dermatology, VA Integrated Service Network (VISN-1), Jamaica Plain, Massachusetts
| | - Nicole Trepanowski
- Department of Dermatology, Brigham and Women’s Hospital, Boston, Massachusetts
- Boston University School of Medicine, Boston, Massachusetts
| | - Michael S. Chang
- Department of Dermatology, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | | | - Christopher Gianacas
- The George Institute for Global Health, UNSW Sydney, Sydney, Australia
- School of Population Health, UNSW Sydney, Sydney, Australia
| | - Jennifer M. McNiff
- Departments of Dermatology and Pathology, Yale School of Medicine, New Haven, Connecticut
| | - Maxwell Fung
- University of California Davis School of Medicine, Sacramento, California
| | | | - Jane M. Grant-Kels
- Department of Dermatology, University of Connecticut School of Medicine, Farmington, Connecticut
- Department of Dermatology, University of Florida College of Medicine, Gainesville, Florida
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2
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Manolakos D, Patrick G, Geisse JK, Rabinovitz H, Buchanan K, Hoang P, Rodriguez-Diaz E, Bigio IJ, Cognetta AB. Use of an elastic-scattering spectroscopy and artificial intelligence device in the assessment of lesions suggestive of skin cancer: A comparative effectiveness study. JAAD Int 2024; 14:52-58. [PMID: 38143790 PMCID: PMC10746496 DOI: 10.1016/j.jdin.2023.08.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/23/2023] [Indexed: 12/26/2023] Open
Abstract
Background Skin cancer is the most common form of cancer worldwide. As artificial intelligence (AI) expands its scope within dermatology, leveraging technology may aid skin cancer detection. Objective To assess the safety and effectiveness of an elastic-scattering spectroscopy (ESS) device in evaluating lesions suggestive of skin cancer. Methods This prospective, multicenter clinical validation study was conducted at 4 US investigational sites. Patients with skin lesions suggestive of melanoma and nonmelanoma skin cancers were clinically assessed by expert dermatologists and evaluated by a device using AI algorithms comparing current ESS lesion readings with training data sets. Statistical analyses included sensitivity, specificity, AUROC, negative predictive value (NPV), and positive predictive value (PPV). Results Overall device sensitivity was 97.04%, with subgroup sensitivity of 96.67% for melanoma, 97.22% for basal cell carcinoma, and 97.01% for squamous cell carcinoma. No statistically significant difference was found between the device and dermatologist performance (P = .8203). Overall specificity of the device was 26.22%. Overall NPV of the device was 89.58% and PPV was 57.54%. Conclusion The ESS device demonstrated high sensitivity in detecting skin cancer. Use of this device may assist primary care clinicians in assessing suspicious lesions, potentially reducing skin cancer morbidity and mortality through expedited and enhanced detection and intervention.
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Affiliation(s)
| | | | - John K. Geisse
- Departments of Dermatology and Pathology, University of California San Francisco, San Francisco, California
| | - Harold Rabinovitz
- Skin and Cancer Associates, Plantation, Florida
- Department of Dermatology, University of Miami School of Medicine, Miami, Florida
- Medical College of Georgia, Augusta University, Augusta, Georgia
| | - Kendall Buchanan
- Medical College of Georgia, Augusta University, Augusta, Georgia
| | | | | | - Irving J. Bigio
- Department of Electrical & Computer Engineering, Boston University, Boston, Massachusetts
| | - Armand B. Cognetta
- Florida State University College of Medicine, Tallahassee, Florida
- Dermatology Associates of Tallahassee, Tallahassee, Florida
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3
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Luo N, Zhong X, Su L, Cheng Z, Ma W, Hao P. Artificial intelligence-assisted dermatology diagnosis: From unimodal to multimodal. Comput Biol Med 2023; 165:107413. [PMID: 37703714 DOI: 10.1016/j.compbiomed.2023.107413] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 08/02/2023] [Accepted: 08/28/2023] [Indexed: 09/15/2023]
Abstract
Artificial Intelligence (AI) is progressively permeating medicine, notably in the realm of assisted diagnosis. However, the traditional unimodal AI models, reliant on large volumes of accurately labeled data and single data type usage, prove insufficient to assist dermatological diagnosis. Augmenting these models with text data from patient narratives, laboratory reports, and image data from skin lesions, dermoscopy, and pathologies could significantly enhance their diagnostic capacity. Large-scale pre-training multimodal models offer a promising solution, exploiting the burgeoning reservoir of clinical data and amalgamating various data types. This paper delves into unimodal models' methodologies, applications, and shortcomings while exploring how multimodal models can enhance accuracy and reliability. Furthermore, integrating cutting-edge technologies like federated learning and multi-party privacy computing with AI can substantially mitigate patient privacy concerns in dermatological datasets and further fosters a move towards high-precision self-diagnosis. Diagnostic systems underpinned by large-scale pre-training multimodal models can facilitate dermatology physicians in formulating effective diagnostic and treatment strategies and herald a transformative era in healthcare.
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Affiliation(s)
- Nan Luo
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Xiaojing Zhong
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Luxin Su
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Zilin Cheng
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Wenyi Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
| | - Pingsheng Hao
- Hospital of Chengdu University of Traditional Chinese Medicine, No. 39 Shi-er-qiao Road, Chengdu, 610075, Sichuan, China.
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4
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Jaklitsch E, Thames T, de Campos Silva T, Coll P, Oliviero M, Ferris LK. Clinical Utility of an AI-powered, Handheld Elastic Scattering Spectroscopy Device on the Diagnosis and Management of Skin Cancer by Primary Care Physicians. J Prim Care Community Health 2023; 14:21501319231205979. [PMID: 37933569 PMCID: PMC10631325 DOI: 10.1177/21501319231205979] [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: 08/16/2023] [Revised: 09/18/2023] [Accepted: 09/20/2023] [Indexed: 11/08/2023] Open
Abstract
BACKGROUND Patients with lesions suspicious for skin cancer often present to primary care physicians (PCPs), who may have limited training in skin cancer diagnosis. OBJECTIVE To measure the impact of an adjunctive handheld device for PCPs that employs elastic scattering spectroscopy (ESS) on the diagnosis and management of skin cancer. METHODS Fifty-seven PCPs evaluated 50 clinical images of skin lesions (25 malignant and 25 benign), first without and then with knowledge of the handheld ESS device output, and in each case indicated if a lesion was likely to be benign or malignant. RESULTS The diagnostic sensitivity of the PCPs with and without the use of the ESS device was 88% (95% CI, 84%-92%) and 67% (95% CI, 62%-72%), respectively (P < .0001). In contrast, no significant difference was observed in the diagnostic specificity. The management sensitivity of the physicians with and without the use of the ESS device was 94% (95% CI, 91%-96%) and 81% (95% CI, 77%-85%), respectively (P = .0009). Similarly, no significant difference was observed in the management specificity. CONCLUSION The use of the ESS device may have the potential to help improve skin cancer diagnosis and confidence in management decision-making in a primary care setting.
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Affiliation(s)
- Erik Jaklitsch
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | | | - Margaret Oliviero
- Dermatology Department, Skin and Cancer Associates, Plantation, FL, USA
| | - Laura Korb Ferris
- Department of Dermatology, University of Pittsburgh, Pittsburgh, PA, USA
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5
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Reistad N, Sturesson C. Distinguishing tumor from healthy tissue in human liver ex vivo using machine learning and multivariate analysis of diffuse reflectance spectra. JOURNAL OF BIOPHOTONICS 2022; 15:e202200140. [PMID: 35860880 DOI: 10.1002/jbio.202200140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/27/2022] [Accepted: 07/12/2022] [Indexed: 06/15/2023]
Abstract
The aim of this work was to evaluate the capability of diffuse reflectance spectroscopy to distinguish malignant liver tissues from surrounding tissues and to determine whether an extended wavelength range (450-1550 nm) offers any advantages over using the conventional wavelength range. Furthermore, multivariate analysis combined with a machine learning algorithm, either linear discriminant analysis or the more advanced support vector machine, was used to discriminate between and classify freshly excised human liver specimens from 18 patients. Tumors were distinguished from surrounding liver tissues with a sensitivity of 99%, specificity of 100%, classification rate of 100% and a Matthews correlation coefficient of 100% using the extended wavelength range and a combination of principal component analysis and support vector techniques. The results indicate that this technology may be useful in clinical applications for real-time tissue diagnostics of tumor margins where rapid classification is important.
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Affiliation(s)
- Nina Reistad
- Department of Physics, Lund University, Lund, Sweden
| | - Christian Sturesson
- Division of Surgery, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
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6
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Rodriguez-Diaz E, Jepeal LI, Baffy G, Lo WK, MashimoMD H, A'amar O, Bigio IJ, Singh SK. Artificial Intelligence-Based Assessment of Colorectal Polyp Histology by Elastic-Scattering Spectroscopy. Dig Dis Sci 2022; 67:613-621. [PMID: 33761089 DOI: 10.1007/s10620-021-06901-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 02/09/2021] [Indexed: 12/09/2022]
Abstract
BACKGROUND Colonoscopic screening and surveillance for colorectal cancer could be made safer and more efficient if endoscopists could predict histology without the need to biopsy and perform histopathology on every polyp. Elastic-scattering spectroscopy (ESS), using fiberoptic probes integrated into standard biopsy tools, can assess, both in vivo and in real time, the scattering and absorption properties of tissue related to its underlying pathology. AIMS The objective of this study was to evaluate prospectively the potential of ESS to predict polyp pathology accurately. METHODS We obtained ESS measurements from patients undergoing screening/surveillance colonoscopy using an ESS fiberoptic probe integrated into biopsy forceps. The integrated forceps were used for tissue acquisition, following current standards of care, and optical measurement. All measurements were correlated to the index pathology. A machine learning model was then applied to measurements from 367 polyps in 169 patients to prospectively evaluate its predictive performance. RESULTS The model achieved sensitivity of 0.92, specificity of 0.87, negative predictive value (NPV) of 0.87, and high-confidence rate (HCR) of 0.84 for distinguishing 220 neoplastic polyps from 147 non-neoplastic polyps of all sizes. Among 138 neoplastic and 131 non-neoplastic polyps ≤ 5 mm, the model achieved sensitivity of 0.91, specificity of 0.88, NPV of 0.89, and HCR of 0.83. CONCLUSIONS Results show that ESS is a viable endoscopic platform for real-time polyp histology, particularly for polyps ≤ 5 mm. ESS is a simple, low-cost, clinically friendly, optical biopsy modality that, when interfaced with minimally obtrusive endoscopic tools, offers an attractive platform for in situ polyp assessment.
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Affiliation(s)
- Eladio Rodriguez-Diaz
- Research Service, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Lisa I Jepeal
- Research Service, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA
| | - György Baffy
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Wai-Kit Lo
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Hiroshi MashimoMD
- Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA.,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA
| | - Ousama A'amar
- Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA
| | - Irving J Bigio
- Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA.,Department of Medicine, Boston University School of Medicine, 72 E. Concord St., Boston, MA, 02118, USA
| | - Satish K Singh
- Research Service, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA. .,Department of Biomedical Engineering, College of Engineering, Boston University, 44 Cummington Mall, Boston, MA, 02215, USA. .,Department of Medicine, Section of Gastroenterology, VA Boston Healthcare System, 150 South Huntington Ave., Boston, MA, 02130, USA. .,Department of Medicine, Brigham and Women's Hospital Boston and Harvard Medical School, 25 Shattuck St., Boston, MA, 02115, USA. .,Department of Medicine, Boston University School of Medicine, 72 E. Concord St., Boston, MA, 02118, USA.
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7
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Rodriguez-Diaz E, Kaanan S, Vanley C, Qureshi T, Bigio IJ. Toward optical spectroscopy-guided lung biopsy: Demonstration of tissue-type classification. JOURNAL OF BIOPHOTONICS 2021; 14:e202100132. [PMID: 34245106 DOI: 10.1002/jbio.202100132] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/08/2021] [Accepted: 07/07/2021] [Indexed: 06/13/2023]
Abstract
The diagnostic yield of standard tissue-sampling modalities of suspected lung cancers, whether by bronchoscopy or interventional radiology, can be nonoptimal, varying with the size and location of lesions. What is needed is an insitu sensor, integrated in the biopsy tool, to objectively distinguish among tissue types in real time, not to replace biopsy with an optical diagnostic, but to verify that the sampling tool is properly located within the target lesion. We investigated the feasibility of elastic scattering spectroscopy (ESS), coupled with machine learning, to distinguish lung lesions from the various nearby tissue types, in a study with freshly-excised lung tissues from surgical resections. Optical spectra were recorded with an ESS fiberoptic probe in different areas of the resected pulmonary tissues, including benign-margin tissue sites as well as the periphery and core of the lesion. An artificial-intelligence model was used to analyze, retrospectively, 2032 measurements from excised tissues of 35 patients. With high accuracy, ESS was able to distinguish alveolar tissue from bronchi, alveolar tissue from lesions, and bronchi from lesions. This ex vivo study indicates promise for ESS fiberoptic probes to be integrated with surgical intervention tools, to improve reliability of pulmonary lesion targeting.
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Affiliation(s)
| | - Samer Kaanan
- Providence Mission Hospital, Mission Viejo, California, USA
| | | | | | - Irving J Bigio
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, USA
- Department of Electrical & Computer Engineering, Boston University, Boston, Massachusetts, USA
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8
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Blundo A, Cignoni A, Banfi T, Ciuti G. Comparative Analysis of Diagnostic Techniques for Melanoma Detection: A Systematic Review of Diagnostic Test Accuracy Studies and Meta-Analysis. Front Med (Lausanne) 2021; 8:637069. [PMID: 33968951 PMCID: PMC8103840 DOI: 10.3389/fmed.2021.637069] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/17/2021] [Indexed: 11/24/2022] Open
Abstract
Melanoma has the highest mortality rate among skin cancers, and early-diagnosis is essential to maximize survival rate. The current procedure for melanoma diagnosis is based on dermoscopy, i.e., a qualitative visual inspection of lesions with intrinsic limited diagnostic reliability and reproducibility. Other non-invasive diagnostic techniques may represent valuable solutions to retrieve additional objective information of a lesion. This review aims to compare the diagnostic performance of non-invasive techniques, alternative to dermoscopy, for melanoma detection in clinical settings. A systematic review of the available literature was performed using PubMed, Scopus and Google scholar databases (2010-September 2020). All human, in-vivo, non-invasive studies using techniques, alternative to dermoscopy, for melanoma diagnosis were included with no restriction on the recruited population. The reference standard was histology but dermoscopy was accepted only in case of benign lesions. Attributes of the analyzed studies were compared, and the quality was evaluated using CASP Checklist. For studies in which the investigated technique was implemented as a diagnostic tool (DTA studies), the QUADAS-2 tool was applied. For DTA studies that implemented a melanoma vs. other skin lesions classification task, a meta-analysis was performed reporting the SROC curves. Sixty-two references were included in the review, of which thirty-eight were analyzed using QUADAS-2. Study designs were: clinical trials (13), retrospective studies (10), prospective studies (8), pilot studies (10), multitiered study (1); the remain studies were proof of concept or had undefined study type. Studies were divided in categories based on the physical principle employed by each diagnostic technique. Twenty-nine out of thirty-eight DTA studies were included in the meta-analysis. Heterogeneity of studies' types, testing strategy, and diagnostic task limited the systematic comparison of the techniques. Based on the SROC curves, spectroscopy achieved the best performance in terms of sensitivity (93%, 95% CI 92.8-93.2%) and specificity (85.2%, 95%CI 84.9-85.5%), even though there was high concern regarding robustness of metrics. Reflectance-confocal-microscopy, instead, demonstrated higher robustness and a good diagnostic performance (sensitivity 88.2%, 80.3-93.1%; specificity 65.2%, 55-74.2%). Best practice recommendations were proposed to reduce bias in future DTA studies. Particular attention should be dedicated to widen the use of alternative techniques to conventional dermoscopy.
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Affiliation(s)
- Alessia Blundo
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Arianna Cignoni
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Tommaso Banfi
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, Pisa, Italy
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9
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Mashimo H, Gordon SR, Singh SK. Advanced endoscopic imaging for detecting and guiding therapy of early neoplasias of the esophagus. Ann N Y Acad Sci 2020; 1482:61-76. [PMID: 33184872 DOI: 10.1111/nyas.14523] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 10/08/2020] [Accepted: 10/13/2020] [Indexed: 12/16/2022]
Abstract
Esophageal cancers, largely adenocarcinoma in Western countries and squamous cell cancer in Asia, present a significant burden of disease and remain one of the most lethal of cancers. Key to improving survival is the development and adoption of new imaging modalities to identify early neoplastic lesions, which may be small, multifocal, subsurface, and difficult to detect by standard endoscopy. Such advanced imaging is particularly relevant with the emergence of ablative techniques that often require multiple endoscopic sessions and may be complicated by bleeding, pain, strictures, and recurrences. Assessing the specific location, depth of involvement, and features correlated with neoplastic progression or incomplete treatment may optimize treatments. While not comprehensive of all endoscopic imaging modalities, we review here some of the recent advances in endoscopic luminal imaging, particularly with surface contrast enhancement using virtual chromoendoscopy, highly magnified subsurface imaging with confocal endomicroscopy, optical coherence tomography, elastic scattering spectroscopy, angle-resolved low-coherence interferometry, and light scattering spectroscopy. While there is no single ideal imaging modality, various multimodal instruments are also being investigated. The future of combining computer-aided assessments, molecular markers, and improved imaging technologies to help localize and ablate early neoplastic lesions shed hope for improved disease outcome.
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Affiliation(s)
- Hiroshi Mashimo
- VA Boston Healthcare System, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Stuart R Gordon
- Dartmouth-Hitchcock Medical Center, Dartmouth University, Lebanon, New Hampshire
| | - Satish K Singh
- VA Boston Healthcare System, Boston, Massachusetts.,Boston University School of Medicine, Boston, Massachusetts
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