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Feenstra L, van der Stel SD, Da Silva Guimaraes M, Dashtbozorg B, Ruers TJM. Point Projection Mapping System for Tracking, Registering, Labeling, and Validating Optical Tissue Measurements. J Imaging 2024; 10:37. [PMID: 38392085 PMCID: PMC10890146 DOI: 10.3390/jimaging10020037] [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: 11/28/2023] [Revised: 01/23/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024] Open
Abstract
The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques.
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Affiliation(s)
- Lianne Feenstra
- Image-Guided Surgery, Department of Surgical Oncology, 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
| | - Stefan D van der Stel
- Image-Guided Surgery, Department of Surgical Oncology, 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
| | - Behdad Dashtbozorg
- Image-Guided Surgery, Department of Surgical Oncology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands
| | - Theo J M Ruers
- Image-Guided Surgery, Department of Surgical Oncology, 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
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Debacker JM, Maris L, Cordier F, Creytens D, Deron P, Descamps B, D'Asseler Y, De Man K, Keereman V, Libbrecht S, Schelfhout V, Van de Vijver K, Vanhove C, Huvenne W. Direct co-registration of [ 18F]FDG uptake and histopathology in surgically excised malignancies of the head and neck: a feasibility study. Eur J Nucl Med Mol Imaging 2023; 50:2127-2139. [PMID: 36854863 DOI: 10.1007/s00259-023-06153-z] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 02/13/2023] [Indexed: 03/02/2023]
Abstract
PURPOSE Recent technical advancements in PET imaging have improved sensitivity and spatial resolution. Consequently, clinical nuclear medicine will be confronted with PET images on a previously unfamiliar resolution. To better understand [18F]FDG distribution at submillimetric scale, a direct correlation of radionuclide-imaging and histopathology is required. METHODS A total of five patients diagnosed with a malignancy of the head and neck were injected with a clinical activity of [18F]FDG before undergoing surgical resection. The resected specimen was imaged using a preclinical high-resolution PET/CT, followed by slicing of the specimen. Multiple slices were rescanned using a micro-PET/CT device, and one of the slices was snap-frozen for frozen sections. Frozen sections were placed on an autoradiographic film, followed by haematoxylin and eosin staining to prepare them for histopathological assessment. The results from both autoradiography and histopathology were co-registered using an iterative co-registration algorithm, and regions of interest were identified to study radiotracer uptake. RESULTS The co-registration between the autoradiographs and their corresponding histopathology was successful in all specimens. The use of this novel methodology allowed direct comparison of autoradiography and histopathology and enabled the visualisation of uncharted heterogeneity in [18F]FDG uptake in both benign and malignant tissue. CONCLUSION We here describe a novel methodology enabling the direct co-registration of [18F]FDG autoradiography with the gold standard of histopathology in human malignant tissue. The future use of the current methodology could further increase our understanding of the distribution of radionuclides in surgically excised malignancies and hence, improve the integration of pathology and molecular imaging in a multiscale perspective. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT05068687.
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Affiliation(s)
- Jens M Debacker
- Department of Head and Skin, Ghent University, Ghent, Belgium.
- Department of Head and Neck Surgery, Ghent University Hospital, Ghent, Belgium.
- Department of Nuclear Medicine, UZ Brussel, Brussels, Belgium.
- In vivo Cellular and Molecular Imaging Laboratory (ICMI), Vrije Universiteit Brussel, Brussels, Belgium.
- Cancer Research Institute Ghent, Ghent, Belgium.
| | - Luna Maris
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
- XEOS Medical, Ghent, Belgium
| | - Fleur Cordier
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
| | - David Creytens
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Philippe Deron
- Department of Head and Skin, Ghent University, Ghent, Belgium
- Department of Head and Neck Surgery, Ghent University Hospital, Ghent, Belgium
| | - Benedicte Descamps
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
- INFINITY Lab, Ghent University, Ghent, Belgium
| | - Yves D'Asseler
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
- Department of Medical Imaging, Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
| | - Kathia De Man
- Department of Medical Imaging, Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
| | - Vincent Keereman
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
- XEOS Medical, Ghent, Belgium
| | - Sasha Libbrecht
- Department of Pathology, Antwerp University Hospital, Edegem, Belgium
| | - Vanessa Schelfhout
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
- Department of Medical Imaging, Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
| | - Koen Van de Vijver
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
- Department of Diagnostic Sciences, Ghent University, Ghent, Belgium
| | - Christian Vanhove
- Cancer Research Institute Ghent, Ghent, Belgium
- Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
- INFINITY Lab, Ghent University, Ghent, Belgium
| | - Wouter Huvenne
- Department of Head and Skin, Ghent University, Ghent, Belgium
- Department of Head and Neck Surgery, Ghent University Hospital, Ghent, Belgium
- Cancer Research Institute Ghent, Ghent, Belgium
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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.
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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
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Sen A, Troncoso P, Venkatesan A, Pagel MD, Nijkamp JA, He Y, Lesage AC, Woodland M, Brock KK. Correlation of in-vivo imaging with histopathology: A review. Eur J Radiol 2021; 144:109964. [PMID: 34619617 DOI: 10.1016/j.ejrad.2021.109964] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 08/26/2021] [Accepted: 09/17/2021] [Indexed: 11/21/2022]
Abstract
Despite tremendous advancements in in vivo imaging modalities, there remains substantial uncertainty with respect to tumor delineation on in these images. Histopathology remains the gold standard for determining the extent of malignancy, with in vivo imaging to histopathologic correlation enabling spatial comparisons. In this review, the steps necessary for successful imaging to histopathologic correlation are described, including in vivo imaging, resection, fixation, specimen sectioning (sectioning technique, securing technique, orientation matching, slice matching), microtome sectioning and staining, correlation (including image registration) and performance evaluation. The techniques used for each of these steps are also discussed. Hundreds of publications from the past 20 years were surveyed, and 62 selected for detailed analysis. For these 62 publications, each stage of the correlative pathology process (and the sub-steps of specimen sectioning) are listed. A statistical analysis was conducted based on 19 studies that reported target registration error as their performance metric. While some methods promise greater accuracy, they may be expensive. Due to the complexity of the processes involved, correlative pathology studies generally include a small number of subjects, which hinders advanced developments in this field.
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Sen A, Fowlkes NW, Kingsley CV, Kulp AM, Huynh T, Willis BJ, Brewer Savannah KJ, Bordes MCA, Hwang KP, McCulloch MM, Stafford RJ, Contreras A, Reece G, Brock KK. Technical Note: Histological validation of anatomical imaging for breast modeling using a novel cryo-microtome. Med Phys 2021; 48:7323-7332. [PMID: 34559413 DOI: 10.1002/mp.15245] [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: 05/08/2021] [Revised: 08/27/2021] [Accepted: 09/14/2021] [Indexed: 11/05/2022] Open
Abstract
PURPOSE Precise correlation between three-dimensional (3D) imaging and histology can aid biomechanical modeling of the breast. We develop a framework to register ex vivo images to histology using a novel cryo-fluorescence tomography (CFT) device. METHODS A formalin-fixed cadaveric breast specimen, including chest wall, was subjected to high-resolution magnetic resonance (MR) imaging. The specimen was then frozen and embedded in an optimal cutting temperature (OCT) compound. The OCT block was placed in a CFT device with an overhead camera and 50 μm thick slices were successively shaved off the block. After each shaving, the block-face was photographed. At select locations including connective/adipose tissue, muscle, skin, and fibroglandular tissue, 20 μm sections were transferred onto cryogenic tape for manual hematoxylin and eosin staining, histological assessment, and image capture. A 3D white-light image was automatically reconstructed from the photographs by aligning fiducial markers embedded in the OCT block. The 3D MR image, 3D white-light image, and photomicrographs were rigidly registered. Target registration errors (TREs) were computed based on 10 pairs of points marked at fibroglandular intersections. The overall MR-histology registration was used to compare the MR intensities at tissue extraction sites with a one-way analysis of variance. RESULTS The MR image to CFT-captured white-light image registration achieved a mean TRE of 0.73 ± 0.25 mm (less than the 1 mm MR slice resolution). The block-face white-light image and block-face photomicrograph registration showed visually indistinguishable alignment of anatomical structures and tissue boundaries. The MR intensities at the four tissue sites identified from histology differed significantly (p < 0.01). Each tissue pair, except the skin-connective/adipose tissue pair, also had significantly different MR intensities (p < 0.01). CONCLUSIONS Fine sectioning in a highly controlled imaging/sectioning environment enables accurate registration between the MR image and histology. Statistically significant differences in MR signal intensities between histological tissues are indicators for the specificity of correlation between MRI and histology.
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Affiliation(s)
- Anando Sen
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Natalie W Fowlkes
- Department of Veterinary Medicine & Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Charles V Kingsley
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Adam M Kulp
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Thomas Huynh
- Department of Veterinary Medicine & Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Brandy J Willis
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kari J Brewer Savannah
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Mary Catherine A Bordes
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Ken-Pin Hwang
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Molly M McCulloch
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Roger Jason Stafford
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Alejandro Contreras
- Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Gregory Reece
- Department of Plastic Surgery, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Kristy K Brock
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Department of Radiation Physics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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Zhang Y, Yu S, Zhu X, Ning X, Liu W, Wang C, Liu X, Zhao D, Zheng Y, Bao J. Explainable liver tumor delineation in surgical specimens using hyperspectral imaging and deep learning. Biomed Opt Express 2021; 12:4510-4529. [PMID: 34457429 PMCID: PMC8367264 DOI: 10.1364/boe.432654] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 05/08/2023]
Abstract
Surgical removal is the primary treatment for liver cancer, but frequent recurrence caused by residual malignant tissue remains an important challenge, as recurrence leads to high mortality. It is unreliable to distinguish tumors from normal tissues merely under visual inspection. Hyperspectral imaging (HSI) has been proved to be a promising technology for intra-operative use by capturing the spatial and spectral information of tissue in a fast, non-contact and label-free manner. In this work, we investigated the feasibility of HSI for liver tumor delineation on surgical specimens using a multi-task U-Net framework. Measurements are performed on 19 patients and a dataset of 36 specimens was collected with corresponding pathological results serving as the ground truth. The developed framework can achieve an overall sensitivity of 94.48% and a specificity of 87.22%, outperforming the baseline SVM method by a large margin. In particular, we propose to add explanations on the well-trained model from the spatial and spectral dimensions to show the contribution of pixels and spectral channels explicitly. On that basis, a novel saliency-weighted channel selection method is further proposed to select a small subset of 5 spectral channels which provide essentially as much information as using all 224 channels. According to the dominant channels, the absorption difference of hemoglobin and bile content in the normal and malignant tissues seems to be promising markers that could be further exploited.
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Affiliation(s)
- Yating Zhang
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Si Yu
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Xueyu Zhu
- Department of Mathematics, University of Iowa, Iowa City, IA 52242, USA
| | - Xuefei Ning
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Wei Liu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Chuting Wang
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Xiaohu Liu
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
- Currently with the School of Biomedical Engineering, School of Ophthalmology & Optometry, Wenzhou Medical University. Xueyuan Road 270, Wenzhou 325027, China
| | - Ding Zhao
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Yongchang Zheng
- Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing 100730, China
| | - Jie Bao
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
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de Boer LL, Kho E, Jóźwiak K, Van de Vijver KK, Vrancken Peeters MJTFD, van Duijnhoven F, Hendriks BHW, Sterenborg HJCM, Ruers TJM. Influence of neoadjuvant chemotherapy on diffuse reflectance spectra of tissue in breast surgery specimens. J Biomed Opt 2019; 24:115004. [PMCID: PMC7003145 DOI: 10.1117/1.jbo.24.11.115004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 11/04/2019] [Indexed: 05/28/2023]
Abstract
Diffuse reflectance spectroscopy (DRS) can discriminate different tissue types based on optical characteristics. Since this technology has the ability to detect tumor tissue, several groups have proposed to use DRS for margin assessment during breast-conserving surgery for breast cancer. Nowadays, an increasing number of patients with breast cancer are being treated by neoadjuvant chemotherapy. Limited research has been published on the influence of neoadjuvant chemotherapy on the optical characteristics of the tissue. Hence, it is unclear whether margin assessment based on DRS is feasible in this specific group of patients. We investigate whether there is an effect of neoadjuvant chemotherapy on optical measurements of breast tissue. To this end, DRS measurements were performed on 92 ex-vivo breast specimens from 92 patients, treated with neoadjuvant chemotherapy and without neoadjuvant chemotherapy. Generalized estimating equation (GEE) models were generated, comparing the measurements of patients with and without neoadjuvant chemotherapy in datasets of different tissue types using a significance level of 5%. As input for the GEE models, either the intensity at a specific wavelength or a fit parameter, derived from the spectrum, was used. In the evaluation of the intensity, no influence of neoadjuvant chemotherapy was found, since none of the wavelengths were significantly different between the measurements with and the measurements without neoadjuvant chemotherapy in any of the datasets. These results were confirmed by the analysis of the fit parameters, which showed a significant difference for the amount of collagen in only one dataset. All other fit parameters were not significant for any of the datasets. These findings may indicate that assessment of the resection margin with DRS is also feasible in the growing population of breast cancer patients who receive neoadjuvant chemotherapy. However, it is possible that we did not detect neoadjuvant chemotherapy effect in the some of the datasets due to the small number of measurements in those datasets.
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Affiliation(s)
- Lisanne L. de Boer
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Esther Kho
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
| | - Katarzyna Jóźwiak
- The Netherlands Cancer Institute, Department of Epidemiology and Biostatistics, The Netherlands
- Institute of Biostatistics and Registry Research, Brandenburg Medical School Theodor Fontane, Neuruppin, Germany
| | - Koen K. Van de Vijver
- The Netherlands Cancer Institute, Department of Pathology, Amsterdam, The Netherlands
- Ghent University Hospital, Department of Pathology, Gent, Belgium
| | | | | | - Benno H. W. Hendriks
- Philips Research, Eindhoven, The Netherlands
- Delft University of Technology, Biomechanical Engineering Department, Delft, The Netherlands
| | - Henricus J. C. M. Sterenborg
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
- Amsterdam University Medical Center, Department of Biomedical Engineering and Physics, Amsterdam, The Netherlands
| | - Theo J. M. Ruers
- The Netherlands Cancer Institute, Department of Surgery, Amsterdam, The Netherlands
- University of Twente, TNW, Technical Medical Centre, Enschede, The Netherlands
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