1
|
Veluponnar D, de Boer LL, Dashtbozorg B, Jong LJS, Geldof F, Guimaraes MDS, Sterenborg HJCM, Vrancken-Peeters MJTFD, van Duijnhoven F, Ruers T. Margin assessment during breast conserving surgery using diffuse reflectance spectroscopy. J Biomed Opt 2024; 29:045006. [PMID: 38665316 PMCID: PMC11045169 DOI: 10.1117/1.jbo.29.4.045006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/03/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024]
Abstract
Significance During breast-conserving surgeries, it is essential to evaluate the resection margins (edges of breast specimen) to determine whether the tumor has been removed completely. In current surgical practice, there are no methods available to aid in accurate real-time margin evaluation. Aim In this study, we investigated the diagnostic accuracy of diffuse reflectance spectroscopy (DRS) combined with tissue classification models in discriminating tumorous tissue from healthy tissue up to 2 mm in depth on the actual resection margin of in vivo breast tissue. Approach We collected an extensive dataset of DRS measurements on ex vivo breast tissue and in vivo breast tissue, which we used to develop different classification models for tissue classification. Next, these models were used in vivo to evaluate the performance of DRS for tissue discrimination during breast conserving surgery. We investigated which training strategy yielded optimum results for the classification model with the highest performance. Results We achieved a Matthews correlation coefficient of 0.76, a sensitivity of 96.7% (95% CI 95.6% to 98.2%), a specificity of 90.6% (95% CI 86.3% to 97.9%) and an area under the curve of 0.98 by training the optimum model on a combination of ex vivo and in vivo DRS data. Conclusions DRS allows real-time margin assessment with a high sensitivity and specificity during breast-conserving surgeries.
Collapse
Affiliation(s)
- Dinusha Veluponnar
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| | - Lisanne L. de Boer
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
| | - Behdad Dashtbozorg
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
| | - Lynn-Jade S. Jong
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| | - Freija Geldof
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| | - Marcos Da Silva Guimaraes
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Pathology, Amsterdam, The Netherlands
| | - Henricus J. C. M. Sterenborg
- Amsterdam University Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | | | - Frederieke van Duijnhoven
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
| | - Theo Ruers
- Netherlands Cancer Institute, Antoni van Leeuwenhoek, Department of Surgery, Image-Guided Surgery, Amsterdam, The Netherlands
- University of Twente, Department of Nanobiophysics, Faculty of Science and Technology, Enschede, The Netherlands
| |
Collapse
|
2
|
Veluponnar D, Dashtbozorg B, Jong LJS, Geldof F, Da Silva Guimaraes M, Vrancken Peeters MJTFD, van Duijnhoven F, Sterenborg HJCM, Ruers TJM, de Boer LL. Diffuse reflectance spectroscopy for accurate margin assessment in breast-conserving surgeries: importance of an optimal number of fibers. Biomed Opt Express 2023; 14:4017-4036. [PMID: 37799696 PMCID: PMC10549728 DOI: 10.1364/boe.493179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 06/05/2023] [Accepted: 06/05/2023] [Indexed: 10/07/2023]
Abstract
During breast-conserving surgeries, it remains challenging to accomplish adequate surgical margins. We investigated different numbers of fibers for fiber-optic diffuse reflectance spectroscopy to differentiate tumorous breast tissue from healthy tissue ex vivo up to 2 mm from the margin. Using a machine-learning classification model, the optimal performance was obtained using at least three emitting fibers (Matthew's correlation coefficient (MCC) of 0.73), which was significantly higher compared to the performance of using a single-emitting fiber (MCC of 0.48). The percentage of correctly classified tumor locations varied from 75% to 100% depending on the tumor percentage, the tumor-margin distance and the number of fibers.
Collapse
Affiliation(s)
- Dinusha Veluponnar
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Lynn-Jade S. Jong
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Freija Geldof
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Marcos Da Silva Guimaraes
- Department of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | | | - Frederieke van Duijnhoven
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Henricus J. C. M. Sterenborg
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, Netherlands
| | - Theo J. M. Ruers
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Lisanne L. de Boer
- Department of Surgery,
Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| |
Collapse
|
3
|
Jong LJS, Post AL, Veluponnar D, Geldof F, Sterenborg HJCM, Ruers TJM, Dashtbozorg B. Tissue Classification of Breast Cancer by Hyperspectral Unmixing. Cancers (Basel) 2023; 15:2679. [PMID: 37345015 DOI: 10.3390/cancers15102679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 04/26/2023] [Accepted: 05/04/2023] [Indexed: 06/23/2023] Open
Abstract
(1) Background: Assessing the resection margins during breast-conserving surgery is an important clinical need to minimize the risk of recurrent breast cancer. However, currently there is no technique that can provide real-time feedback to aid surgeons in the margin assessment. Hyperspectral imaging has the potential to overcome this problem. To classify resection margins with this technique, a tissue discrimination model should be developed, which requires a dataset with accurate ground-truth labels. However, establishing such a dataset for resection specimens is difficult. (2) Methods: In this study, we therefore propose a novel approach based on hyperspectral unmixing to determine which pixels within hyperspectral images should be assigned to the ground-truth labels from histopathology. Subsequently, we use this hyperspectral-unmixing-based approach to develop a tissue discrimination model on the presence of tumor tissue within the resection margins of ex vivo breast lumpectomy specimens. (3) Results: In total, 372 measured locations were included on the lumpectomy resection surface of 189 patients. We achieved a sensitivity of 0.94, specificity of 0.85, accuracy of 0.87, Matthew's correlation coefficient of 0.71, and area under the curve of 0.92. (4) Conclusion: Using this hyperspectral-unmixing-based approach, we demonstrated that the measured locations with hyperspectral imaging on the resection surface of lumpectomy specimens could be classified with excellent performance.
Collapse
Affiliation(s)
- Lynn-Jade S Jong
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Anouk L Post
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Dinusha Veluponnar
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Henricus J C M Sterenborg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Theo J M Ruers
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| |
Collapse
|
4
|
Veluponnar D, de Boer LL, Geldof F, Jong LJS, Da Silva Guimaraes M, Vrancken Peeters MJTFD, van Duijnhoven F, Ruers T, Dashtbozorg B. Toward Intraoperative Margin Assessment Using a Deep Learning-Based Approach for Automatic Tumor Segmentation in Breast Lumpectomy Ultrasound Images. Cancers (Basel) 2023; 15:cancers15061652. [PMID: 36980539 PMCID: PMC10046373 DOI: 10.3390/cancers15061652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/01/2023] [Accepted: 03/06/2023] [Indexed: 03/30/2023] Open
Abstract
There is an unmet clinical need for an accurate, rapid and reliable tool for margin assessment during breast-conserving surgeries. Ultrasound offers the potential for a rapid, reproducible, and non-invasive method to assess margins. However, it is challenged by certain drawbacks, including a low signal-to-noise ratio, artifacts, and the need for experience with the acquirement and interpretation of images. A possible solution might be computer-aided ultrasound evaluation. In this study, we have developed new ensemble approaches for automated breast tumor segmentation. The ensemble approaches to predict positive and close margins (distance from tumor to margin ≤ 2.0 mm) in the ultrasound images were based on 8 pre-trained deep neural networks. The best optimum ensemble approach for segmentation attained a median Dice score of 0.88 on our data set. Furthermore, utilizing the segmentation results we were able to achieve a sensitivity of 96% and a specificity of 76% for predicting a close margin when compared to histology results. The promising results demonstrate the capability of AI-based ultrasound imaging as an intraoperative surgical margin assessment tool during breast-conserving surgery.
Collapse
Affiliation(s)
- Dinusha Veluponnar
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Lisanne L de Boer
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Lynn-Jade S Jong
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Marcos Da Silva Guimaraes
- Department of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | | | - Frederieke van Duijnhoven
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo Ruers
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Nanobiophysics, Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| |
Collapse
|
5
|
Hoogteijling N, Veluponnar D, de Boer L, Dashtbozorg B, Vrancken Peeters MJ, van Duijnhoven F, Ruers T. Toward automatic surgical margin assessment using ultrasound imaging during breast cancer surgery. European Journal of Surgical Oncology 2023. [DOI: 10.1016/j.ejso.2022.11.323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
|
6
|
Jong LJS, de Kruif N, Geldof F, Veluponnar D, Sanders J, Vrancken Peeters MJTFD, van Duijnhoven F, Sterenborg HJCM, Dashtbozorg B, Ruers TJM. Discriminating healthy from tumor tissue in breast lumpectomy specimens using deep learning-based hyperspectral imaging. Biomed Opt Express 2022; 13:2581-2604. [PMID: 35774331 PMCID: PMC9203093 DOI: 10.1364/boe.455208] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/09/2022] [Accepted: 03/10/2022] [Indexed: 06/15/2023]
Abstract
Achieving an adequate resection margin during breast-conserving surgery remains challenging due to the lack of intraoperative feedback. Here, we evaluated the use of hyperspectral imaging to discriminate healthy tissue from tumor tissue in lumpectomy specimens. We first used a dataset obtained on tissue slices to develop and evaluate three convolutional neural networks. Second, we fine-tuned the networks with lumpectomy data to predict the tissue percentages of the lumpectomy resection surface. A MCC of 0.92 was achieved on the tissue slices and an RMSE of 9% on the lumpectomy resection surface. This shows the potential of hyperspectral imaging to classify the resection margins of lumpectomy specimens.
Collapse
Affiliation(s)
- Lynn-Jade S. Jong
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
- Equal contributors
| | - Naomi de Kruif
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
- Equal contributors
| | - Freija Geldof
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Dinusha Veluponnar
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - Joyce Sanders
- Department of Pathology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | | | - Frederieke van Duijnhoven
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Henricus J. C. M. Sterenborg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands
| | - Behdad Dashtbozorg
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands
| | - Theo J. M. Ruers
- Department of Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
- Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| |
Collapse
|
7
|
Dolmans LS, Lebedeva ER, Veluponnar D, van Dijk EJ, Nederkoorn PJ, Hoes AW, Rutten FH, Olesen J, Kappelle LJ. Diagnostic Accuracy of the Explicit Diagnostic Criteria for Transient Ischemic Attack: A Validation Study. Stroke 2019; 50:2080-2085. [PMID: 31693449 PMCID: PMC6661246 DOI: 10.1161/strokeaha.119.025626] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Supplemental Digital Content is available in the text. Background and Purpose— The clinical diagnosis of a transient ischemic attack (TIA) can be difficult. Evidence-based criteria hardly exist. We evaluated if the recently proposed Explicit Diagnostic Criteria for TIA (EDCT), an easy to perform clinical tool focusing on type, duration, and mode of onset of clinical features, would facilitate the clinical diagnosis of TIA. Methods— We used data from patients suspected of a TIA by a general practitioner and referred to a TIA service in the region of Utrecht, the Netherlands, who participated in the MIND-TIA (Markers in the Diagnosis of TIA) study. Information about the clinical features was collected with a standardized questionnaire within 72 hours after onset. A panel of 3 experienced neurologists ultimately determined the definite diagnosis based on all available diagnostic information including a 6-month follow-up period. Two researchers scored the EDCT. Sensitivity, specificity, and predictive values of the EDCT were assessed using the panel diagnosis as reference. A secondary analysis was performed with modified subcriteria of the EDCT. Results— Of the 206 patients, 126 (61%) had a TIA (n=104) or minor stroke (n=22), and 80 (39%) an alternative diagnosis. Most common alternative diagnoses were migraine with aura (n=24; 30.0%), stress related or somatoform symptoms (n=16; 20.0%), and syncope (n=9; 11.3%). The original EDCT had a sensitivity of 98.4% (95% CI, 94.4–99.8) and a specificity of 61.3% (49.7–71.9). Negative and positive predictive values were 96.1% (86.0–99.0) and 80.0% (75.2–84.1), respectively. The modified EDCT showed a higher specificity of 73.8% (62.7–83.0) with the same sensitivity and a similar negative predictive value of 96.7%, but a higher positive predictive value of 85.5% (80.3–89.5). Conclusions— The EDCT has excellent sensitivity and negative predictive value and could be a valuable diagnostic tool for the diagnosis of TIA.
Collapse
Affiliation(s)
- L Servaas Dolmans
- From the Julius Center for Health Sciences and Primary Care (L.S.D., D.V., A.W.H., F.H.R.), University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Elena R Lebedeva
- Department of Neurology, Ural State Medical University, Yekaterinburg, Russia (E.R.L.).,International Headache Center 'Europe-Asia', Yekaterinburg, Russia (E.R.L., J.O.)
| | - Dinusha Veluponnar
- From the Julius Center for Health Sciences and Primary Care (L.S.D., D.V., A.W.H., F.H.R.), University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Ewoud J van Dijk
- Department of Neurology, Radboud University Medical Center, Nijmegen, the Netherlands (E.J.v.D.)
| | - Paul J Nederkoorn
- Department of Neurology, Amsterdam UMC, University of Amsterdam, the Netherlands (P.J.N.)
| | - Arno W Hoes
- From the Julius Center for Health Sciences and Primary Care (L.S.D., D.V., A.W.H., F.H.R.), University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Frans H Rutten
- From the Julius Center for Health Sciences and Primary Care (L.S.D., D.V., A.W.H., F.H.R.), University Medical Center Utrecht, Utrecht University, the Netherlands
| | - Jes Olesen
- International Headache Center 'Europe-Asia', Yekaterinburg, Russia (E.R.L., J.O.).,Department of Neurology, Danish Headache Center, Rigshospitalet Glostrup, University of Copenhagen, Denmark (J.O.)
| | - L Jaap Kappelle
- Department of Neurology (L.J.K.), University Medical Center Utrecht, Utrecht University, the Netherlands
| | | |
Collapse
|