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Bianco V, Valentino M, Pirone D, Miccio L, Memmolo P, Brancato V, Coppola L, Smaldone G, D’Aiuto M, Mossetti G, Salvatore M, Ferraro P. Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy. Comput Struct Biotechnol J 2024; 24:225-236. [PMID: 38572166 PMCID: PMC10990711 DOI: 10.1016/j.csbj.2024.03.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/21/2024] [Accepted: 03/21/2024] [Indexed: 04/05/2024] Open
Abstract
Breast cancer is one of the most spread and monitored pathologies in high-income countries. After breast biopsy, histological tissue is stored in paraffin, sectioned and mounted. Conventional inspection of tissue slides under benchtop light microscopes involves paraffin removal and staining, typically with H&E. Then, expert pathologists are called to judge the stained slides. However, paraffin removal and staining are operator-dependent, time and resources consuming processes that can generate ambiguities due to non-uniform staining. Here we propose a novel method that can work directly on paraffined stain-free slides. We use Fourier Ptychography as a quantitative phase-contrast microscopy method, which allows accessing a very wide field of view (i.e., mm2) in one single image while guaranteeing high lateral resolution (i.e., 0.5 µm). This imaging method is multi-scale, since it enables looking at the big picture, i.e. the complex tissue structure and connections, with the possibility to zoom-in up to the single-cell level. To handle this informative image content, we introduce elements of fractal geometry as multi-scale analysis method. We show the effectiveness of fractal features in describing and classifying fibroadenoma and breast cancer tissue slides from ten patients with very high accuracy. We reach 94.0 ± 4.2% test accuracy in classifying single images. Above all, we show that combining the decisions of the single images, each patient's slide can be classified with no error. Besides, fractal geometry returns a guide map to help pathologist to judge the different tissue portions based on the likelihood these can be associated to a breast cancer or fibroadenoma biomarker. The proposed automatic method could significantly simplify the steps of tissue analysis and make it independent from the sample preparation, the skills of the lab operator and the pathologist.
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Affiliation(s)
- Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Marika Valentino
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
- DIETI, Department of Electrical Engineering and Information Technologies, University of Naples “Federico II”, via Claudio 21, 80125 Napoli, Italy
| | - Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
| | | | - Luigi Coppola
- IRCCS SYNLAB SDN, Via E. Gianturco 113, Napoli 80143, Italy
| | | | | | - Gennaro Mossetti
- Pathological Anatomy Service, Casa di Cura Maria Rosaria, Via Colle San Bartolomeo 50, 80045 Pompei, Napoli, Italy
| | | | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “E. Caianiello”, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
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Lin Y, Wang Z, Zhang D, Cheng KT, Chen H. BoNuS: Boundary Mining for Nuclei Segmentation With Partial Point Labels. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2137-2147. [PMID: 38231818 DOI: 10.1109/tmi.2024.3355068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of the wide existence and large variances in nuclei morphometry in histopathology images. However, manual annotation of tens of thousands of nuclei is tedious and time-consuming, which requires significant amount of human effort and domain-specific expertise. To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei. Specifically, we propose a novel boundary mining framework for nuclei segmentation, named BoNuS, which simultaneously learns nuclei interior and boundary information from the point labels. To achieve this goal, we propose a novel boundary mining loss, which guides the model to learn the boundary information by exploring the pairwise pixel affinity in a multiple-instance learning manner. Then, we consider a more challenging problem, i.e., partial point label, where we propose a nuclei detection module with curriculum learning to detect the missing nuclei with prior morphological knowledge. The proposed method is validated on three public datasets, MoNuSeg, CPM, and CoNIC datasets. Experimental results demonstrate the superior performance of our method to the state-of-the-art weakly-supervised nuclei segmentation methods. Code: https://github.com/hust-linyi/bonus.
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Heikal A, El-Ghamry A, Elmougy S, Rashad MZ. Fine tuning deep learning models for breast tumor classification. Sci Rep 2024; 14:10753. [PMID: 38730248 PMCID: PMC11087494 DOI: 10.1038/s41598-024-60245-w] [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: 11/19/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
This paper proposes an approach to enhance the differentiation task between benign and malignant Breast Tumors (BT) using histopathology images from the BreakHis dataset. The main stages involve preprocessing, which encompasses image resizing, data partitioning (training and testing sets), followed by data augmentation techniques. Both feature extraction and classification tasks are employed by a Custom CNN. The experimental results show that the proposed approach using the Custom CNN model exhibits better performance with an accuracy of 84% than applying the same approach using other pretrained models, including MobileNetV3, EfficientNetB0, Vgg16, and ResNet50V2, that present relatively lower accuracies, ranging from 74 to 82%; these four models are used as both feature extractors and classifiers. To increase the accuracy and other performance metrics, Grey Wolf Optimization (GWO), and Modified Gorilla Troops Optimization (MGTO) metaheuristic optimizers are applied to each model separately for hyperparameter tuning. In this case, the experimental results show that the Custom CNN model, refined with MGTO optimization, reaches an exceptional accuracy of 93.13% in just 10 iterations, outperforming the other state-of-the-art methods, and the other four used pretrained models based on the BreakHis dataset.
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Affiliation(s)
- Abeer Heikal
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
- Department of Computer Science, Misr Higher Institute for Commerce and Computers, Mansoura, 35511, Egypt.
| | - Amir El-Ghamry
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
| | - Samir Elmougy
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
| | - M Z Rashad
- Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt
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4
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Nakhli R, Rich K, Zhang A, Darbandsari A, Shenasa E, Hadjifaradji A, Thiessen S, Milne K, Jones SJM, McAlpine JN, Nelson BH, Gilks CB, Farahani H, Bashashati A. VOLTA: an enVironment-aware cOntrastive ceLl represenTation leArning for histopathology. Nat Commun 2024; 15:3942. [PMID: 38729933 PMCID: PMC11087497 DOI: 10.1038/s41467-024-48062-1] [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: 03/29/2023] [Accepted: 04/19/2024] [Indexed: 05/12/2024] Open
Abstract
In clinical oncology, many diagnostic tasks rely on the identification of cells in histopathology images. While supervised machine learning techniques necessitate the need for labels, providing manual cell annotations is time-consuming. In this paper, we propose a self-supervised framework (enVironment-aware cOntrastive cell represenTation learning: VOLTA) for cell representation learning in histopathology images using a technique that accounts for the cell's mutual relationship with its environment. We subject our model to extensive experiments on data collected from multiple institutions comprising over 800,000 cells and six cancer types. To showcase the potential of our proposed framework, we apply VOLTA to ovarian and endometrial cancers and demonstrate that our cell representations can be utilized to identify the known histotypes of ovarian cancer and provide insights that link histopathology and molecular subtypes of endometrial cancer. Unlike supervised models, we provide a framework that can empower discoveries without any annotation data, even in situations where sample sizes are limited.
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Affiliation(s)
- Ramin Nakhli
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Katherine Rich
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, Canada
| | - Allen Zhang
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Amirali Darbandsari
- Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Elahe Shenasa
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Amir Hadjifaradji
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Sidney Thiessen
- Deeley Research Centre, BC Cancer Agency, Victoria, BC, Canada
| | - Katy Milne
- Deeley Research Centre, BC Cancer Agency, Victoria, BC, Canada
| | - Steven J M Jones
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, Vancouver, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, Canada
| | - Jessica N McAlpine
- Department of Obstetrics and Gynecology, University of British Columbia, Vancouver, BC, Canada
| | - Brad H Nelson
- Deeley Research Centre, BC Cancer Agency, Victoria, BC, Canada
| | - C Blake Gilks
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Hossein Farahani
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Ali Bashashati
- School of Biomedical Engineering, University of British Columbia, Vancouver, BC, Canada.
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada.
- Canada's Michael Smith Genome Sciences Centre, BC Cancer Research Institute, Vancouver, Canada.
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Huang K, Liao J, He J, Lai S, Peng Y, Deng Q, Wang H, Liu Y, Peng L, Bai Z, Yu N, Li Y, Jiang Z, Su J, Li J, Tang Y, Chen M, Lu L, Chen X, Yao J, Zhao S. A Real-time augmented reality robot integrated with artificial intelligence for skin tumor surgery - experimental study and case series. Int J Surg 2024; 110:01279778-990000000-01257. [PMID: 38549223 PMCID: PMC11175769 DOI: 10.1097/js9.0000000000001371] [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: 10/11/2023] [Accepted: 03/11/2024] [Indexed: 06/15/2024]
Abstract
BACKGROUND Skin tumors affect many people worldwide, and surgery is the first treatment choice. Achieving precise preoperative planning and navigation of intraoperative sampling remains a problem and is excessively reliant on the experience of surgeons, especially for Mohs surgery for malignant tumors. MATERIALS AND METHODS To achieve precise preoperative planning and navigation of intraoperative sampling, we developed a real-time augmented reality (AR) surgical system integrated with artificial intelligence (AI) to enhance three functions: AI-assisted tumor boundary segmentation, surgical margin design, and navigation in intraoperative tissue sampling. Non-randomized controlled trials were conducted on manikin, tumor-simulated rabbits, and human volunteers in xxx Laboratory to evaluate the surgical system. RESULTS The results showed that the accuracy of the benign and malignant tumor segmentation were 0.9556 and 0.9548, respectively, and the average AR navigation mapping error was 0.644 mm. The proposed surgical system was applied in 106 skin tumor surgeries, including intraoperative navigation of sampling in 16 Mohs surgery cases. Surgeons who have used this system highly recognize it. CONCLUSIONS The surgical system highlighted the potential to achieve accurate treatment of skin tumors and to fill the gap in global research on skin tumor surgery systems.
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Affiliation(s)
- Kai Huang
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
- Tencent AI Lab, Shenzhen, People’s Republic of China
| | - Jun Liao
- Tencent AI Lab, Shenzhen, People’s Republic of China
| | - Jishuai He
- Tencent AI Lab, Shenzhen, People’s Republic of China
| | - Sicen Lai
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Yihao Peng
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Qian Deng
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Han Wang
- Tencent AI Lab, Shenzhen, People’s Republic of China
| | - Yuancheng Liu
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Lanyuan Peng
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Ziqi Bai
- Tencent AI Lab, Shenzhen, People’s Republic of China
| | - Nianzhou Yu
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Yixin Li
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Zixi Jiang
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Juan Su
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Jinmao Li
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Yan Tang
- Department of Dermatology
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Mingliang Chen
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Lixia Lu
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Xiang Chen
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
| | - Jianhua Yao
- Tencent AI Lab, Shenzhen, People’s Republic of China
| | - Shuang Zhao
- Department of Dermatology
- Hunan Key Laboratory of Skin Cancer and Psoriasis
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Hunan Engineering Research Center of Skin Health and Disease, Central South University
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Hunan
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Sharkas M, Attallah O. Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform. Sci Rep 2024; 14:6914. [PMID: 38519513 PMCID: PMC10959971 DOI: 10.1038/s41598-024-56820-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/11/2024] [Indexed: 03/25/2024] Open
Abstract
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called "Color-CADx" for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training-testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.
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Affiliation(s)
- Maha Sharkas
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt
| | - Omneya Attallah
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt.
- Wearables, Biosensing, and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 21937, Egypt.
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Rydzewski NR, Shi Y, Li C, Chrostek MR, Bakhtiar H, Helzer KT, Bootsma ML, Berg TJ, Harari PM, Floberg JM, Blitzer GC, Kosoff D, Taylor AK, Sharifi MN, Yu M, Lang JM, Patel KR, Citrin DE, Sundling KE, Zhao SG. A platform-independent AI tumor lineage and site (ATLAS) classifier. Commun Biol 2024; 7:314. [PMID: 38480799 PMCID: PMC10937974 DOI: 10.1038/s42003-024-05981-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 02/27/2024] [Indexed: 03/17/2024] Open
Abstract
Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98-99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.
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Affiliation(s)
- Nicholas R Rydzewski
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Yue Shi
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Chenxuan Li
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | | | - Hamza Bakhtiar
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Kyle T Helzer
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Matthew L Bootsma
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Tracy J Berg
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
| | - Paul M Harari
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - John M Floberg
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - Grace C Blitzer
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
| | - David Kosoff
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Amy K Taylor
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Marina N Sharifi
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Menggang Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA
| | - Joshua M Lang
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA
- Department of Medicine, University of Wisconsin, Madison, WI, USA
| | - Krishnan R Patel
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Deborah E Citrin
- Radiation Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Kaitlin E Sundling
- Department of Pathology and Laboratory Medicine, University of Wisconsin, Madison, WI, USA
- Wisconsin State Laboratory of Hygiene, University of Wisconsin, Madison, WI, USA
| | - Shuang G Zhao
- Department of Human Oncology, University of Wisconsin, Madison, WI, USA.
- Carbone Cancer Center, University of Wisconsin, Madison, WI, USA.
- William S. Middleton Veterans Hospital, Madison, WI, USA.
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8
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Pedersen MA, Munk OL, Dias AH, Steffensen JH, Møller AL, Johnsson AL, Hansen KV, Bender D, Jakobsen S, Busk M, Gormsen LC, Tramm T, Borgquist S, Vendelbo MH. Dynamic whole-body [ 18F]FES PET/CT increases lesion visibility in patients with metastatic breast cancer. EJNMMI Res 2024; 14:24. [PMID: 38436824 PMCID: PMC10912074 DOI: 10.1186/s13550-024-01080-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 02/15/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND Correct classification of estrogen receptor (ER) status is essential for prognosis and treatment planning in patients with breast cancer (BC). Therefore, it is recommended to sample tumor tissue from an accessible metastasis. However, ER expression can show intra- and intertumoral heterogeneity. 16α-[18F]fluoroestradiol ([18F]FES) Positron Emission Tomography/Computed Tomography (PET/CT) allows noninvasive whole-body (WB) identification of ER distribution and is usually performed as a single static image 60 min after radiotracer injection. Using dynamic whole-body (D-WB) PET imaging, we examine [18F]FES kinetics and explore whether Patlak parametric images ( K i ) are quantitative and improve lesion visibility. RESULTS This prospective study included eight patients with metastatic ER-positive BC scanned using a D-WB PET acquisition protocol. The kinetics of [18F]FES were best characterized by the irreversible two-tissue compartment model in tumor lesions and in the majority of organ tissues. K i values from Patlak parametric images correlated with K i values from the full kinetic analysis, r2 = 0.77, and with the semiquantitative mean standardized uptake value (SUVmean), r2 = 0.91. Furthermore, parametric K i images had the highest target-to-background ratio (TBR) in 162/164 metastatic lesions and the highest contrast-to-noise ratio (CNR) in 99/164 lesions compared to conventional SUV images. TBR was 2.45 (95% confidence interval (CI): 2.25-2.68) and CNR 1.17 (95% CI: 1.08-1.26) times higher in K i images compared to SUV images. These quantitative differences were seen as reduced background activity in the K i images. CONCLUSION [18F]FES uptake is best described by an irreversible two-tissue compartment model. D-WB [18F]FES PET/CT scans can be used for direct reconstruction of parametric K i images, with superior lesion visibility and K i values comparable to K i values found from full kinetic analyses. This may aid correct ER classification and treatment decisions. Trial registration ClinicalTrials.gov: NCT04150731, https://clinicaltrials.gov/study/NCT04150731.
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Affiliation(s)
- Mette A Pedersen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Ole L Munk
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - André H Dias
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
| | | | - Anders L Møller
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | | | - Kim Vang Hansen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
| | - Dirk Bender
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Steen Jakobsen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
| | - Morten Busk
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Danish Centre for Particle Therapy, Aarhus University Hospital, Aarhus, Denmark
| | - Lars C Gormsen
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Trine Tramm
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Pathology, Aarhus University Hospital, Aarhus, Denmark
| | - Signe Borgquist
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Mikkel H Vendelbo
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Boulevard 165, 8200, Aarhus, Denmark.
- Department of Biomedicine, Aarhus University, Aarhus, Denmark.
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark.
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9
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Fernandez-Martín C, Silva-Rodriguez J, Kiraz U, Morales S, Janssen EAM, Naranjo V. Uninformed Teacher-Student for hard-samples distillation in weakly supervised mitosis localization. Comput Med Imaging Graph 2024; 112:102328. [PMID: 38244279 DOI: 10.1016/j.compmedimag.2024.102328] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 11/02/2023] [Accepted: 12/12/2023] [Indexed: 01/22/2024]
Abstract
BACKGROUND AND OBJECTIVE Mitotic activity is a crucial biomarker for diagnosing and predicting outcomes for different types of cancers, particularly breast cancer. However, manual mitosis counting is challenging and time-consuming for pathologists, with moderate reproducibility due to biopsy slide size, low mitotic cell density, and pattern heterogeneity. In recent years, deep learning methods based on convolutional neural networks (CNNs) have been proposed to address these limitations. Nonetheless, these methods have been hampered by the available data labels, which usually consist only of the centroids of mitosis, and by the incoming noise from annotated hard negatives. As a result, complex algorithms with multiple stages are often required to refine the labels at the pixel level and reduce the number of false positives. METHODS This article presents a novel weakly supervised approach for mitosis detection that utilizes only image-level labels on histological hematoxylin and eosin (H&E) images, avoiding the need for complex labeling scenarios. Also, an Uninformed Teacher-Student (UTS) pipeline is introduced to detect and distill hard samples by comparing weakly supervised localizations and the annotated centroids, using strong augmentations to enhance uncertainty. Additionally, an automatic proliferation score is proposed that mimicks the pathologist-annotated mitotic activity index (MAI). The proposed approach is evaluated on three publicly available datasets for mitosis detection on breast histology samples, and two datasets for mitotic activity counting in whole-slide images. RESULTS The proposed framework achieves competitive performance with relevant prior literature in all the datasets used for evaluation without explicitly using the mitosis location information during training. This approach challenges previous methods that rely on strong mitosis location information and multiple stages to refine false positives. Furthermore, the proposed pipeline for hard-sample distillation demonstrates promising dataset-specific improvements. Concretely, when the annotation has not been thoroughly refined by multiple pathologists, the UTS model offers improvements of up to ∼4% in mitosis localization, thanks to the detection and distillation of uncertain cases. Concerning the mitosis counting task, the proposed automatic proliferation score shows a moderate positive correlation with the MAI annotated by pathologists at the biopsy level on two external datasets. CONCLUSIONS The proposed Uninformed Teacher-Student pipeline leverages strong augmentations to distill uncertain samples and measure dissimilarities between predicted and annotated mitosis. Results demonstrate the feasibility of the weakly supervised approach and highlight its potential as an objective evaluation tool for tumor proliferation.
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Affiliation(s)
- Claudio Fernandez-Martín
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain.
| | | | - Umay Kiraz
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway; Department of Pathology, Stavanger University Hospital, Stavanger, Norway
| | - Sandra Morales
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain
| | - Emiel A M Janssen
- Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway; Department of Pathology, Stavanger University Hospital, Stavanger, Norway
| | - Valery Naranjo
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, HUMAN-tech, Universitat Politècnica de València, Valencia, Spain
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10
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Harper EM, Henderson-Jackson E, Rosa M. Pathology Residents' Perceptions and Attitudes Toward Breast Pathology: A National Survey. Arch Pathol Lab Med 2024; 148:371-376. [PMID: 37270800 DOI: 10.5858/arpa.2022-0323-ep] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2023] [Indexed: 06/06/2023]
Abstract
CONTEXT.— Breast pathology (BP) is considered to be subject to interobserver variability among pathologists, emphasizing the need for adequate training. However, specifics of BP residency training have not been elucidated. OBJECTIVE.— To assess the characteristics of BP residency training in the United States. DESIGN.— A Qualtrics-managed online survey was emailed to program directors of all US pathology residency programs, requesting them to forward the survey link to their pathology residents. RESULTS.— One hundred seventeen residents' survey responses were evaluable. Most responses (92; 79%) came from residents in university hospital-based programs. Thirty-five respondents (30%) had a dedicated BP rotation in their program. Most respondents believed that BP was an important part of training (96 of 100; 96%) and pathology practice (95 of 100; 95%). Seventy-one respondents believed that their BP training was adequate overall (71 of 100; 71%). Forty-one percent of respondents indicated that they would not like BP to be a significant part of their future practice. The main reasons given were that they had a different preferred area of interest, that they lacked interest in BP, or that breast cases were time-consuming to sign out. CONCLUSIONS.— Our results show that in the United States, most programs do not offer a dedicated BP rotation, but breast cases are signed out by subspecialized or experienced breast pathologists. In addition, most respondents believed that they received adequate training and would be competent to independently sign out BP in the future. Additional studies addressing new-in-practice pathologists' proficiency in BP will further help elucidate the quality of BP training in the United States.
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Affiliation(s)
- Erika M Harper
- From the Department of Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Evita Henderson-Jackson
- From the Department of Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida
| | - Marilin Rosa
- From the Department of Pathology, Moffitt Cancer Center and Research Institute, Tampa, Florida
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11
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Silva AB, Martins AS, Tosta TAA, Loyola AM, Cardoso SV, Neves LA, de Faria PR, do Nascimento MZ. OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01041-w. [PMID: 38409608 DOI: 10.1007/s10278-024-01041-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/28/2024]
Abstract
Early diagnosis of potentially malignant disorders, such as oral epithelial dysplasia, is the most reliable way to prevent oral cancer. Computational algorithms have been used as an auxiliary tool to aid specialists in this process. Usually, experiments are performed on private data, making it difficult to reproduce the results. There are several public datasets of histological images, but studies focused on oral dysplasia images use inaccessible datasets. This prevents the improvement of algorithms aimed at this lesion. This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain normalization methods. Then, the segmented and non-segmented images were classified with CNN architectures and machine learning algorithms. The data obtained through these processes is available in the dataset. The segmentation stage showed the F1-score value of 0.83, obtained with the U-Net model using the ResNet-50 as a backbone. At the classification stage, the most expressive result was achieved with the Random Forest method, with an accuracy value of 94.22%. The results show that the segmentation contributed to the classification results, but studies are needed for the improvement of these stages of automated diagnosis. The original, gold standard, normalized, and segmented images are publicly available and may be used for the improvement of clinical applications of CAD methods on oral epithelial dysplasia tissue images.
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Affiliation(s)
- Adriano Barbosa Silva
- Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB, 38400-902, Uberlândia, MG, Brazil.
| | - Alessandro Santana Martins
- Federal Institute of Triângulo Mineiro (IFTM), R. Belarmino Vilela Junqueira, S/N, 38305-200, Ituiutaba, MG, Brazil
| | - Thaína Aparecida Azevedo Tosta
- Science and Technology Institute, Federal University of São Paulo (UNIFESP), Av. Cesare Mansueto Giulio Lattes, 1201, 12247-014, São José dos Campos, SP, Brazil
| | - Adriano Mota Loyola
- School of Dentistry, Federal University of Uberlândia (UFU), Av. Pará - 1720, 38405-320, Uberlândia, MG, Brazil
| | - Sérgio Vitorino Cardoso
- School of Dentistry, Federal University of Uberlândia (UFU), Av. Pará - 1720, 38405-320, Uberlândia, MG, Brazil
| | - Leandro Alves Neves
- Department of Computer Science and Statistics (DCCE), São Paulo State University (UNESP), R. Cristóvão Colombo, 2265, 38305-200, São José do Rio Preto, SP, Brazil
| | - Paulo Rogério de Faria
- Department of Histology and Morphology, Institute of Biomedical Science, Federal University of Uberlândia (UFU), Av. Amazonas, S/N, 38405-320, Uberlândia, MG, Brazil
| | - Marcelo Zanchetta do Nascimento
- Faculty of Computer Science (FACOM) - Federal University of Uberlândia (UFU), Av. João Naves de Ávila 2121, BLB, 38400-902, Uberlândia, MG, Brazil
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12
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Zhang C, Gao X, Fan B, Guo S, Lyu X, Shi J, Fu Y, Zhang Q, Liu P, Guo H. Highly accurate and effective deep neural networks in pathological diagnosis of prostate cancer. World J Urol 2024; 42:93. [PMID: 38386116 DOI: 10.1007/s00345-024-04775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/08/2024] [Indexed: 02/23/2024] Open
Abstract
PURPOSE To established an AI system to make the pathological diagnosis of prostate cancer. METHODS Prostate histopathological whole mount (WM) sections from patients underwent robot-assisted laparoscopic prostatectomy were prepared. All the prostate WM pathological sections were converted to digital image data and marked with different colors on the basis of the ISUP Gleason grade group. The image was then fed into a segmentation algorithm. We chose modified U-Net as our fundamental network architecture. RESULTS 172 patients were involved in this study. 896 pieces of prostate WM pathological sections from 160 patients, in which 826 pieces of WM sections from 148 patients were assigned to the training set randomly. After image segmentation there were totally 2,138,895 patches, of which 1,646,535 patches were valid for training. The other WM section was arranged for testing. Based on the whole image testing, AI and pathologists presented the same answers among 21 of 22 pieces of sections. To evaluate the diagnostic results at the pixel level, we anticipated correct cancer or non-cancer diagnose from this AI system. The area under the ROC curve as 96.8%. The value of pixel accuracy of three methods (binary analysis, clinically oriented analysis and analysis for different ISUP Gleason grade) were 96.93%, 95.43% and 93.88%, respectively. The value of frequency weighted IoU were 94.32%, 92.13% and 90.21%, respectively. CONCLUSIONS This AI system is able to assist pathologists to make a final diagnosis, indicating the great potential and a wide-range of applications of AI in the medical field.
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Affiliation(s)
- Chengwei Zhang
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Xiubin Gao
- Nanjing Innovative Data Technologies, Inc., Nanjing, 210014, Jiangsu, China
| | - Bo Fan
- Department of Urology, The First People's Hospital of Changshu, The Changshu Hospital Affiliated to Soochow University, Changshu, 215500, China
| | - Suhan Guo
- College of Global Public Health, New York University, NY, 10012, USA
| | - Xiaoyu Lyu
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Jiong Shi
- Department of Pathology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Yao Fu
- Department of Pathology, Drum Tower Hospital, Medical School of Nanjing University, Nanjing, 210008, Jiangsu, China
| | - Qing Zhang
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| | - Peng Liu
- Nanjing Innovative Data Technologies, Inc., Nanjing, 210014, Jiangsu, China.
| | - Hongqian Guo
- Department of Urology, Drum Tower Hospital, Medical School of Nanjing University, Institute of Urology, Nanjing University, No. 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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13
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Brunyé TT, Booth K, Hendel D, Kerr KF, Shucard H, Weaver DL, Elmore JG. Machine learning classification of diagnostic accuracy in pathologists interpreting breast biopsies. J Am Med Inform Assoc 2024; 31:552-562. [PMID: 38031453 PMCID: PMC10873842 DOI: 10.1093/jamia/ocad232] [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: 08/22/2023] [Revised: 10/19/2023] [Accepted: 11/20/2023] [Indexed: 12/01/2023] Open
Abstract
OBJECTIVE This study explores the feasibility of using machine learning to predict accurate versus inaccurate diagnoses made by pathologists based on their spatiotemporal viewing behavior when evaluating digital breast biopsy images. MATERIALS AND METHODS The study gathered data from 140 pathologists of varying experience levels who each reviewed a set of 14 digital whole slide images of breast biopsy tissue. Pathologists' viewing behavior, including zooming and panning actions, was recorded during image evaluation. A total of 30 features were extracted from the viewing behavior data, and 4 machine learning algorithms were used to build classifiers for predicting diagnostic accuracy. RESULTS The Random Forest classifier demonstrated the best overall performance, achieving a test accuracy of 0.81 and area under the receiver-operator characteristic curve of 0.86. Features related to attention distribution and focus on critical regions of interest were found to be important predictors of diagnostic accuracy. Further including case-level and pathologist-level information incrementally improved classifier performance. DISCUSSION Results suggest that pathologists' viewing behavior during digital image evaluation can be leveraged to predict diagnostic accuracy, affording automated feedback and decision support systems based on viewing behavior to aid in training and, ultimately, clinical practice. They also carry implications for basic research examining the interplay between perception, thought, and action in diagnostic decision-making. CONCLUSION The classifiers developed herein have potential applications in training and clinical settings to provide timely feedback and support to pathologists during diagnostic decision-making. Further research could explore the generalizability of these findings to other medical domains and varied levels of expertise.
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Affiliation(s)
- Tad T Brunyé
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA 02155, United States
- Department of Psychology, Tufts University, Medford, MA 02155, United States
| | - Kelsey Booth
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA 02155, United States
| | - Dalit Hendel
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA 02155, United States
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, Seattle, WA 98105, United States
| | - Hannah Shucard
- Department of Biostatistics, University of Washington, Seattle, WA 98105, United States
| | - Donald L Weaver
- Department of Pathology and Laboratory Medicine, Larner College of Medicine, University of Vermont and Vermont Cancer Center, Burlington, VT 05405, United States
| | - Joann G Elmore
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, United States
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14
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Fernández-Aranzamendi EG, Castillo-Araníbar PR, San Román Castillo EG, Oller BS, Ventura-Zaa L, Eguiluz-Rodriguez G, González-Posadas V, Segovia-Vargas D. Dielectric Characterization of Ex-Vivo Breast Tissues: Differentiation of Tumor Types through Permittivity Measurements. Cancers (Basel) 2024; 16:793. [PMID: 38398184 PMCID: PMC10886458 DOI: 10.3390/cancers16040793] [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: 12/31/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Early analysis and diagnosis of breast tumors is essential for either quickly launching a treatment or for seeing the evolution of patients who, for instance, have already undergone chemotherapy treatment. Once tissues are excised, histological analysis is the most frequent tool used to characterize benign or malignant tumors. Dielectric microwave spectroscopy makes use of an open-ended coaxial probe in the 1-8 GHz frequency range to quickly identify the type of tumor (ductal carcinoma, lobular carcinoma, mucinous carcinoma and fibroadenoma). The experiment was undertaken with data from 70 patients who had already undergone chemotherapy treatment, which helped to electrically map the histological tissues with their electric permittivity. Thus, the variations in the permittivity of different types of tumors reveal distinctive patterns: benign tumors have permittivity values lower than 35, while malignant ones range between 40 and 60. For example, at a frequency of 2 GHz, the measured permittivity was 45.6 for ductal carcinoma, 33.1 for lobular carcinoma, 59.5 for mucinous carcinoma, and 27.6 for benign tumors. This differentiation remains consistent in a frequency range of 1 to 4.5 GHz. These results highlight the effectiveness of these measurements in the classification of breast tumors, providing a valuable tool for quick and accurate diagnosis and effective treatment.
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Affiliation(s)
- Elizabeth G. Fernández-Aranzamendi
- Department of Signal Theory and Communications, University Carlos III of Madrid, 28911 Madrid, Spain; (E.G.S.R.C.); (B.S.O.); (V.G.-P.)
- Department de Ingeniería Eléctrica y Electrónica, Universidad Católica San Pablo, Arequipa 04001, Peru;
| | | | - Ebert G. San Román Castillo
- Department of Signal Theory and Communications, University Carlos III of Madrid, 28911 Madrid, Spain; (E.G.S.R.C.); (B.S.O.); (V.G.-P.)
| | - Belén S. Oller
- Department of Signal Theory and Communications, University Carlos III of Madrid, 28911 Madrid, Spain; (E.G.S.R.C.); (B.S.O.); (V.G.-P.)
| | - Luz Ventura-Zaa
- Department of Oncology Medicine, Regional Institute of Neoplastic Diseases, Arequipa 04002, Peru; (L.V.-Z.); (G.E.-R.)
| | - Gelber Eguiluz-Rodriguez
- Department of Oncology Medicine, Regional Institute of Neoplastic Diseases, Arequipa 04002, Peru; (L.V.-Z.); (G.E.-R.)
| | - Vicente González-Posadas
- Department of Signal Theory and Communications, University Carlos III of Madrid, 28911 Madrid, Spain; (E.G.S.R.C.); (B.S.O.); (V.G.-P.)
| | - Daniel Segovia-Vargas
- Department of Signal Theory and Communications, University Carlos III of Madrid, 28911 Madrid, Spain; (E.G.S.R.C.); (B.S.O.); (V.G.-P.)
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15
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He Y, Duan L, Dong G, Chen F, Li W. Computational pathology-based weakly supervised prediction model for MGMT promoter methylation status in glioblastoma. Front Neurol 2024; 15:1345687. [PMID: 38385046 PMCID: PMC10880091 DOI: 10.3389/fneur.2024.1345687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 01/19/2024] [Indexed: 02/23/2024] Open
Abstract
Introduction The methylation status of oxygen 6-methylguanine-DNA methyltransferase (MGMT) is closely related to the treatment and prognosis of glioblastoma. However, there are currently some challenges in detecting the methylation status of MGMT promoters. The hematoxylin and eosin (H&E)-stained histopathological slides have always been the gold standard for tumor diagnosis. Methods In this study, based on the TCGA database and H&E-stained Whole slide images (WSI) of Beijing Tiantan Hospital, we constructed a weakly supervised prediction model of MGMT promoter methylation status in glioblastoma by using two Transformer structure models. Results The accuracy scores of this model in the TCGA dataset and our independent dataset were 0.79 (AUC = 0.86) and 0.76 (AUC = 0.83), respectively. Conclusion The model demonstrates effective prediction of MGMT promoter methylation status in glioblastoma and exhibits some degree of generalization capability. At the same time, our study also shows that adding Patches automatic screening module to the computational pathology research framework of glioma can significantly improve the model effect.
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Affiliation(s)
- Yongqi He
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ling Duan
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Gehong Dong
- Department of Pathology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Feng Chen
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wenbin Li
- Department of Neuro-Oncology, Cancer Center, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
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16
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Fernandez G, Zeineh J, Prastawa M, Scott R, Madduri AS, Shtabsky A, Jaffer S, Feliz A, Veremis B, Mejias JC, Charytonowicz E, Gladoun N, Koll G, Cruz K, Malinowski D, Donovan MJ. Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer. Clin Breast Cancer 2024; 24:93-102.e6. [PMID: 38114366 DOI: 10.1016/j.clbc.2023.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 10/19/2023] [Accepted: 10/29/2023] [Indexed: 12/21/2023]
Abstract
BACKGROUND PreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis. MATERIALS AND METHODS Using hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day, and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility. RESULTS Analytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations. CONCLUSION In summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients.
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Affiliation(s)
- Gerardo Fernandez
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | | | | | | | | | | | - Brandon Veremis
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | - Nataliya Gladoun
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY
| | | | | | | | - Michael J Donovan
- PreciseDx, New York, NY; Icahn School of Medicine at Mount Sinai, New York, NY; University of Miami, Pathology, Miami, FL.
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17
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Liu Z, Cai Y, Tang Q. Nuclei detection in breast histopathology images with iterative correction. Med Biol Eng Comput 2024; 62:465-478. [PMID: 37914958 DOI: 10.1007/s11517-023-02947-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023]
Abstract
This work presents a deep network architecture to improve nuclei detection performance and achieve the high localization accuracy of nuclei in breast cancer histopathology images. The proposed model consists of two parts, generating nuclear candidate module and refining nuclear localization module. We first design a novel patch learning method to obtain high-quality nuclear candidates, where in addition to categories, location representations are also added to the patch information to implement the multi-task learning process of nuclear classification and localization; meanwhile, the deep supervision mechanism is introduced to obtain the coherent contributions from each scale layer. In order to refine nuclear localization, we propose an iterative correction strategy to make the prediction progressively closer to the ground truth, which significantly improves the accuracy of nuclear localization and facilitates neighbor size selection in the nonmaximum suppression step. Experimental results demonstrate the superior performance of our method for nuclei detection on the H&E stained histopathological image dataset as compared to previous state-of-the-art methods, especially in multiple cluttered nuclei detection, can achieve better results than existing techniques.
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Affiliation(s)
- Ziyi Liu
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, People's Republic of China
- Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, 264001, People's Republic of China
| | - Yu Cai
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, People's Republic of China
| | - Qiling Tang
- School of Biomedical Engineering, South Central Minzu University, Wuhan, 430074, People's Republic of China.
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18
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Dalton JC, Chiba A, Plichta JK. The Evolving Era of Breast Cancer Risk Assessment in Benign Breast Disease. JAMA Surg 2024; 159:201-202. [PMID: 38091019 DOI: 10.1001/jamasurg.2023.6389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Affiliation(s)
- Juliet C Dalton
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
| | - Akiko Chiba
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Duke University, Durham, North Carolina
| | - Jennifer K Plichta
- Department of Surgery, Duke University Medical Center, Durham, North Carolina
- Duke Cancer Institute, Duke University, Durham, North Carolina
- Department of Population Health Sciences, Duke University Medical Center, Durham, North Carolina
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19
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Farooq H, Saleem S, Aleem I, Iftikhar A, Sheikh UN, Naveed H. Toward interpretable and generalized mitosis detection in digital pathology using deep learning. Digit Health 2024; 10:20552076241255471. [PMID: 38778869 PMCID: PMC11110526 DOI: 10.1177/20552076241255471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 05/01/2024] [Indexed: 05/25/2024] Open
Abstract
Objective The mitotic activity index is an important prognostic factor in the diagnosis of cancer. The task of mitosis detection is difficult as the nuclei are microscopic in size and partially labeled, and there are many more non-mitotic nuclei compared to mitotic ones. In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method to tackle these challenges. Methods Our proposed methodology is inspired from recent research on deep learning and an extensive analysis on the dataset and training pipeline. We first used the MiDoG'22 dataset for training, validation, and testing. We then tested the methodology without fine-tuning on the TUPAC'16 dataset and on a real-time case from Shaukat Khanum Memorial Cancer Hospital and Research Centre. Results Our methodology has shown promising results both quantitatively and qualitatively. Quantitatively, our methodology achieved an F1-score of 0.87 on the MiDoG'22 dataset and an F1-score of 0.83 on the TUPAC dataset. Qualitatively, our methodology is generalizable and interpretable across various datasets and clinical settings. Conclusion In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method that can accurately predict mitotic nuclei. We illustrate the accuracy, generalizability, and interpretability of our approach across various datasets and clinical settings. Our methodology can speed up the adoption of computer-aided digital pathology in clinical settings.
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Affiliation(s)
- Hasan Farooq
- Computational Biology Research Lab, National University of Computer & Emerging Sciences, Islamabad, Pakistan
| | - Saira Saleem
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Iffat Aleem
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Ayesha Iftikhar
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Umer Nisar Sheikh
- Shaukat Khanum Memorial Cancer Hospital and Research Centre, Lahore, Pakistan
| | - Hammad Naveed
- Computational Biology Research Lab, National University of Computer & Emerging Sciences, Islamabad, Pakistan
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20
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Honma N, Yoshida M, Kinowaki K, Horii R, Katsurada Y, Murata Y, Shimizu A, Tanabe Y, Yamauchi C, Yamamoto Y, Iwata H, Saji S. The Japanese breast cancer society clinical practice guidelines for pathological diagnosis of breast cancer, 2022 edition. Breast Cancer 2024; 31:8-15. [PMID: 37934318 PMCID: PMC10764572 DOI: 10.1007/s12282-023-01518-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 10/18/2023] [Indexed: 11/08/2023]
Affiliation(s)
- Naoko Honma
- Department of Pathology, Faculty of Medicine, Toho University, 5-21-16 Omori-Nishi, Ota-Ku, Tokyo, 143-8540, Japan.
| | - Masayuki Yoshida
- Department of Diagnostic Pathology, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-Ku, Tokyo, 104-0045, Japan
| | - Keiichi Kinowaki
- Department of Pathology, Toranomon Hospital, 2-2-2 Toranomon, Minato-Ku, Tokyo, 105-8470, Japan
| | - Rie Horii
- Department of Pathology, Saitama Cancer Center, 780 Komuro, Ina, Kita-Adachi-Gun, Saitama, 362-0806, Japan
| | - Yuka Katsurada
- Pathology and Laboratory Medicine, National Defense Medical College, 3-2, Namiki, Tokorozawa, Saitama, 359-8513, Japan
| | - Yuya Murata
- Department of Pathology, NHO Tokyo Medical Center, 2-5-1, Higashigaoka, Meguro-Ku, Tokyo, 152-0021, Japan
| | - Ai Shimizu
- Department of Surgical Pathology, Hokkaido University Hospital, Kita-14, Nishi-5, Kita-Ku, Sapporo, 060-8648, Japan
| | - Yuko Tanabe
- Department of Medical Oncology, Toranomon Hospital, 2-2-2 Toranomon, Minato-Ku, Tokyo, 105-8470, Japan
| | - Chikako Yamauchi
- Department of Radiation Oncology, Shiga General Hospital, 4-1-1 KyomachiShiga Prefecture, Otsu City, 520-8577, Japan
| | - Yutaka Yamamoto
- Department of Breast and Endocrine Surgery, Kumamoto University Hospital, 1-1-1 Honjo, Chuo-Ku, Kumamoto, 860-8556, Japan
| | - Hiroji Iwata
- Department of Breast Oncology, Aichi Cancer Center Hospital, 1-1 Kanokoden, Chikusa-Ku, Nagoya, 464-8681, Japan
| | - Shigehira Saji
- Department of Medical Oncology, Fukushima Medical University, 1 Hikarigaoka, Fukushima City, Fukushima, 960-1295, Japan
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21
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Azad R, Kazerouni A, Heidari M, Aghdam EK, Molaei A, Jia Y, Jose A, Roy R, Merhof D. Advances in medical image analysis with vision Transformers: A comprehensive review. Med Image Anal 2024; 91:103000. [PMID: 37883822 DOI: 10.1016/j.media.2023.103000] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 09/30/2023] [Accepted: 10/11/2023] [Indexed: 10/28/2023]
Abstract
The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in Computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in Computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://github.com/mindflow-institue/Awesome-Transformer.
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Affiliation(s)
- Reza Azad
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Amirhossein Kazerouni
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Moein Heidari
- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
| | | | - Amirali Molaei
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Yiwei Jia
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Abin Jose
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Rijo Roy
- Faculty of Electrical Engineering and Information Technology, RWTH Aachen University, Aachen, Germany
| | - Dorit Merhof
- Faculty of Informatics and Data Science, University of Regensburg, Regensburg, Germany; Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.
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22
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Janesick A, Shelansky R, Gottscho AD, Wagner F, Williams SR, Rouault M, Beliakoff G, Morrison CA, Oliveira MF, Sicherman JT, Kohlway A, Abousoud J, Drennon TY, Mohabbat SH, Taylor SEB. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat Commun 2023; 14:8353. [PMID: 38114474 PMCID: PMC10730913 DOI: 10.1038/s41467-023-43458-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 11/09/2023] [Indexed: 12/21/2023] Open
Abstract
Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial, or targeted in situ gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. This integrative approach allowed us to explore molecular differences that exist between distinct tumor regions and to identify biomarkers involved in the progression towards invasive carcinoma. Further, we study cell neighborhoods and identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. Here, we demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity; however, it is the integration of these technologies that leads to deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.
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23
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Abbas SF, Vuong TTL, Kim K, Song B, Kwak JT. Multi-cell type and multi-level graph aggregation network for cancer grading in pathology images. Med Image Anal 2023; 90:102936. [PMID: 37660482 DOI: 10.1016/j.media.2023.102936] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/30/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023]
Abstract
In pathology, cancer grading is crucial for patient management and treatment. Recent deep learning methods, based upon convolutional neural networks (CNNs), have shown great potential for automated and accurate cancer diagnosis. However, these do not explicitly utilize tissue/cellular composition, and thus difficult to incorporate the existing knowledge of cancer pathology. In this study, we propose a multi-cell type and multi-level graph aggregation network (MMGA-Net) for cancer grading. Given a pathology image, MMGA-Net constructs multiple cell graphs at multiple levels to represent intra- and inter-cell type relationships and to incorporate global and local cell-to-cell interactions. In addition, it extracts tissue contextual information using a CNN. Then, the tissue and cellular information are fused to predict a cancer grade. The experimental results on two types of cancer datasets demonstrate the effectiveness of MMGA-Net, outperforming other competing models. The results also suggest that the information fusion of multiple cell types and multiple levels via graphs is critical for improved pathology image analysis.
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Affiliation(s)
- Syed Farhan Abbas
- School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Trinh Thi Le Vuong
- School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Kyungeun Kim
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Boram Song
- Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Jin Tae Kwak
- School of Electrical Engineering, Korea University, Seoul 02841, Republic of Korea.
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24
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Voon W, Hum YC, Tee YK, Yap WS, Nisar H, Mokayed H, Gupta N, Lai KW. Evaluating the effectiveness of stain normalization techniques in automated grading of invasive ductal carcinoma histopathological images. Sci Rep 2023; 13:20518. [PMID: 37993544 PMCID: PMC10665422 DOI: 10.1038/s41598-023-46619-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 11/02/2023] [Indexed: 11/24/2023] Open
Abstract
Debates persist regarding the impact of Stain Normalization (SN) on recent breast cancer histopathological studies. While some studies propose no influence on classification outcomes, others argue for improvement. This study aims to assess the efficacy of SN in breast cancer histopathological classification, specifically focusing on Invasive Ductal Carcinoma (IDC) grading using Convolutional Neural Networks (CNNs). The null hypothesis asserts that SN has no effect on the accuracy of CNN-based IDC grading, while the alternative hypothesis suggests the contrary. We evaluated six SN techniques, with five templates selected as target images for the conventional SN techniques. We also utilized seven ImageNet pre-trained CNNs for IDC grading. The performance of models trained with and without SN was compared to discern the influence of SN on classification outcomes. The analysis unveiled a p-value of 0.11, indicating no statistically significant difference in Balanced Accuracy Scores between models trained with StainGAN-normalized images, achieving a score of 0.9196 (the best-performing SN technique), and models trained with non-normalized images, which scored 0.9308. As a result, we did not reject the null hypothesis, indicating that we found no evidence to support a significant discrepancy in effectiveness between stain-normalized and non-normalized datasets for IDC grading tasks. This study demonstrates that SN has a limited impact on IDC grading, challenging the assumption of performance enhancement through SN.
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Affiliation(s)
- Wingates Voon
- Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| | - Yan Chai Hum
- Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia.
| | - Yee Kai Tee
- Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| | - Wun-She Yap
- Department of Electrical and Electronic Engineering, Faculty of Engineering and Science, Lee Kong Chian, Universiti Tunku Abdul Rahman, Kampar, Malaysia
| | - Humaira Nisar
- Department of Electronic Engineering, Faculty of Engineering and Green Technology, Universiti Tunku Abdul Rahman, 31900, Kampar, Malaysia
| | - Hamam Mokayed
- Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, Lulea, Sweden
| | - Neha Gupta
- School of Electronics Engineering, Vellore Institute of Technology, Amaravati, AP, India
| | - Khin Wee Lai
- Department of Biomedical Engineering, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
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25
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Abdallah N, Marion JM, Tauber C, Carlier T, Hatt M, Chauvet P. Enhancing histopathological image classification of invasive ductal carcinoma using hybrid harmonization techniques. Sci Rep 2023; 13:20014. [PMID: 37973797 PMCID: PMC10654662 DOI: 10.1038/s41598-023-46239-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 10/30/2023] [Indexed: 11/19/2023] Open
Abstract
This study aims to develop a robust pipeline for classifying invasive ductal carcinomas and benign tumors in histopathological images, addressing variability within and between centers. We specifically tackle the challenge of detecting atypical data and variability between common clusters within the same database. Our feature engineering-based pipeline comprises a feature extraction step, followed by multiple harmonization techniques to rectify intra- and inter-center batch effects resulting from image acquisition variability and diverse patient clinical characteristics. These harmonization steps facilitate the construction of more robust and efficient models. We assess the proposed pipeline's performance on two public breast cancer databases, BreaKHIS and IDCDB, utilizing recall, precision, and accuracy metrics. Our pipeline outperforms recent models, achieving 90-95% accuracy in classifying benign and malignant tumors. We demonstrate the advantage of harmonization for classifying patches from different databases. Our top model scored 94.7% for IDCDB and 95.2% for BreaKHis, surpassing existing feature engineering-based models (92.1% for IDCDB and 87.7% for BreaKHIS) and attaining comparable performance to deep learning models. The proposed feature-engineering-based pipeline effectively classifies malignant and benign tumors while addressing variability within and between centers through the incorporation of various harmonization techniques. Our findings reveal that harmonizing variabilities between patches from different batches directly impacts the learning and testing performance of classification models. This pipeline has the potential to enhance breast cancer diagnosis and treatment and may be applicable to other diseases.
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Affiliation(s)
- Nassib Abdallah
- LaTIM, INSERM, Université de Bretagne-Occidentale, Brest, France.
- LARIS, Université d'Angers, Angers, France.
| | | | | | | | - Mathieu Hatt
- LaTIM, INSERM, Université de Bretagne-Occidentale, Brest, France
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26
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Masood S, Silverstein MJ. Is it Time to Retire the Term of Low-Grade Ductal Carcinoma in Situ and Replace it With Ductal Neoplasia? Adv Anat Pathol 2023; 30:361-367. [PMID: 37746902 DOI: 10.1097/pap.0000000000000418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
As the leading cause of cancer morbidity and the second leading cause of cancer mortality among women, breast cancer continues to remain a major global public health problem. Consequently, significant attention has been directed toward early breast cancer detection and prevention. As a result, the number of image-detected biopsies has increased, and minimally invasive diagnostic procedures have almost replaced open surgical biopsies. Therefore, pathologists are expected to provide more information with less tissue and diagnose increasing numbers of atypical proliferative breast lesions, in situ lesions, and small breast carcinomas. This is a difficult task, as reflected by continuous reports highlighting the challenges associated with morphologic distinction between atypical ductal hyperplasia and low-grade ductal carcinoma in situ. The current interobserver variability among pathologists to accurately define these two entities often leads to silent overdiagnosis and overtreatment. Up to now, there are no reproducible morphologic features and/or any reliable biomarkers that can accurately separate the above-mentioned entities. Despite these reports, patients diagnosed with low-grade ductal carcinoma in situ are subject to cancer therapy regardless of the fact that low-grade ductal carcinoma in situ is known to be an indolent lesion. Studies have shown that low and high-grade ductal carcinoma in situ are genetically different forms of breast cancer precursors; however, the term ductal carcinoma in situ is followed by cancer therapy regardless of the grade and biology of the tumor. In contrast, patients with the diagnoses of atypical ductal hyperplasia do not undergo cancer therapy. In the current article, attempts are made to highlight the continuous dilemma in distinction between atypical ductal hyperplasia and low-grade ductal carcinoma in situ. Going forward, we suggest that low-grade ductal carcinoma in situ be referred to as ductal neoplasia. This alternative terminology allows for different management and follow-up strategies by eliminating the word carcinoma.
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Affiliation(s)
- Shahla Masood
- Department of Pathology and Laboratory Medicine. University of Florida College of Medicine, Jacksonville
- UF Health Jacksonville Breast Center
- UF Health Jacksonville Laboratories
- UF Health Jacksonville Cancer Program, Jacksonville, FL
| | - Melvin J Silverstein
- Hoag Breast Program, Hoag Memorial Hospital Presbyterian, Newport Beach
- Keck School of Medicine, University of Southern California, Los Angeles, CA
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27
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Hanna MG, Brogi E. Future Practices of Breast Pathology Using Digital and Computational Pathology. Adv Anat Pathol 2023; 30:421-433. [PMID: 37737690 DOI: 10.1097/pap.0000000000000414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/23/2023]
Abstract
Pathology clinical practice has evolved by adopting technological advancements initially regarded as potentially disruptive, such as electron microscopy, immunohistochemistry, and genomic sequencing. Breast pathology has a critical role as a medical domain, where the patient's pathology diagnosis has significant implications for prognostication and treatment of diseases. The advent of digital and computational pathology has brought about significant advancements in the field, offering new possibilities for enhancing diagnostic accuracy and improving patient care. Digital slide scanning enables to conversion of glass slides into high-fidelity digital images, supporting the review of cases in a digital workflow. Digitization offers the capability to render specimen diagnoses, digital archival of patient specimens, collaboration, and telepathology. Integration of image analysis and machine learning-based systems layered atop the high-resolution digital images offers novel workflows to assist breast pathologists in their clinical, educational, and research endeavors. Decision support tools may improve the detection and classification of breast lesions and the quantification of immunohistochemical studies. Computational biomarkers may help to contribute to patient management or outcomes. Furthermore, using digital and computational pathology may increase standardization and quality assurance, especially in areas with high interobserver variability. This review explores the current landscape and possible future applications of digital and computational techniques in the field of breast pathology.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
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28
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Schulmeyer CE, Beckmann MW, Fasching PA, Häberle L, Golcher H, Kunath F, Wullich B, Emons J. Improving the Quality of Care for Cancer Patients through Oncological Second Opinions in a Comprehensive Cancer Center: Feasibility of Patient-Initiated Second Opinions through a Health-Insurance Service Point. Diagnostics (Basel) 2023; 13:3300. [PMID: 37958196 PMCID: PMC10647700 DOI: 10.3390/diagnostics13213300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 10/17/2023] [Accepted: 10/19/2023] [Indexed: 11/15/2023] Open
Abstract
BACKGROUND To improve the quality and cost-effectiveness of care, cancer patients can obtain a second medical opinion on their treatment. Validation of the diagnostic procedure (e.g., imaging), diagnosis, and treatment recommendation allows oncological therapy to be applied in a more targeted way, optimizing interdisciplinary care. This study describes patients who received second opinions at the Comprehensive Cancer Center for Erlangen-Nuremberg metropolitan area in Germany over a 6-year period, as well as the amount of time spent on second-opinion counseling. METHODS This prospective, descriptive, single-center observational study included 584 male and female cancer patients undergoing gynecological, urologic, or general surgery who sought a second medical opinion. The extent to which the first opinion complied with standard guidelines was assessed solely descriptively. RESULTS The first opinion was in accordance with the guidelines and complete in 54.5% of the patients, and guideline compliant but incomplete in 13.2%. The median time taken to form a second opinion was 225 min, and the cancer information service was contacted by patients an average of eight times. CONCLUSIONS The initial opinion was guideline compliant and complete in every second case. Without a second opinion, the remaining patients would have been denied a guideline-compliant treatment recommendation. Obtaining a second opinion gives patients an opportunity to receive a guideline-compliant treatment recommendation and enables them to benefit from newer, individualized therapeutic approaches in clinical trials. Establishing patient-initiated second opinions via central contact points appears to be a feasible option for improving guideline compliance.
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Affiliation(s)
- Carla E. Schulmeyer
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
| | - Matthias W. Beckmann
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
| | - Lothar Häberle
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
- Biostatistics Unit, Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
| | - Henriette Golcher
- Department of Surgery, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
| | - Frank Kunath
- Department of Urology, Klinikum Bayreuth GmbH, 95445 Bayreuth, Germany
| | - Bernd Wullich
- Department of Urology and Pediatric Urology, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany;
| | - Julius Emons
- Department of Gynecology and Obstetrics, Erlangen University Hospital, Comprehensive Cancer Center Erlangen-EMN, Friedrich Alexander University of Erlangen–Nuremberg, 91054 Erlangen, Germany
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29
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Gudhe NR, Kosma VM, Behravan H, Mannermaa A. Nuclei instance segmentation from histopathology images using Bayesian dropout based deep learning. BMC Med Imaging 2023; 23:162. [PMID: 37858043 PMCID: PMC10585914 DOI: 10.1186/s12880-023-01121-3] [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: 07/04/2023] [Accepted: 10/05/2023] [Indexed: 10/21/2023] Open
Abstract
BACKGROUND The deterministic deep learning models have achieved state-of-the-art performance in various medical image analysis tasks, including nuclei segmentation from histopathology images. The deterministic models focus on improving the model prediction accuracy without assessing the confidence in the predictions. METHODS We propose a semantic segmentation model using Bayesian representation to segment nuclei from the histopathology images and to further quantify the epistemic uncertainty. We employ Bayesian approximation with Monte-Carlo (MC) dropout during the inference time to estimate the model's prediction uncertainty. RESULTS We evaluate the performance of the proposed approach on the PanNuke dataset, which consists of 312 visual fields from 19 organ types. We compare the nuclei segmentation accuracy of our approach with that of a fully convolutional neural network, U-Net, SegNet, and the state-of-the-art Hover-net. We use F1-score and intersection over union (IoU) as the evaluation metrics. The proposed approach achieves a mean F1-score of 0.893 ± 0.008 and an IoU value of 0.868 ± 0.003 on the test set of the PanNuke dataset. These results outperform the Hover-net, which has a mean F1-score of 0.871 ± 0.010 and an IoU value of 0.840 ± 0.032. CONCLUSIONS The proposed approach, which incorporates Bayesian representation and Monte-Carlo dropout, demonstrates superior performance in segmenting nuclei from histopathology images compared to existing models such as U-Net, SegNet, and Hover-net. By considering the epistemic uncertainty, our model provides a more reliable estimation of the prediction confidence. These findings highlight the potential of Bayesian deep learning for improving medical image analysis tasks and can contribute to the development of more accurate and reliable computer-aided diagnostic systems.
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Affiliation(s)
- Naga Raju Gudhe
- Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer research community RC Cancer, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland.
| | - Veli-Matti Kosma
- Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer research community RC Cancer, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Hamid Behravan
- Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer research community RC Cancer, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland
| | - Arto Mannermaa
- Institute of Clinical Medicine, Pathology and Forensic Medicine, Multidisciplinary Cancer research community RC Cancer, University of Eastern Finland, P.O. Box 1627, Kuopio, 70211, Finland
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
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30
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Suciu V, El Chamieh C, Soufan R, Mathieu MC, Balleyguier C, Delaloge S, Balogh Z, Scoazec JY, Chevret S, Vielh P. Real-World Diagnostic Accuracy of the On-Site Cytopathology Advance Report (OSCAR) Procedure Performed in a Multidisciplinary One-Stop Breast Clinic. Cancers (Basel) 2023; 15:4967. [PMID: 37894334 PMCID: PMC10605571 DOI: 10.3390/cancers15204967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 08/19/2023] [Accepted: 09/24/2023] [Indexed: 10/29/2023] Open
Abstract
Fine-needle aspiration (FNA) cytology has been widely used for the diagnosis of breast cancer lesions with the objective of differentiating benign from malignant masses. However, the occurrence of unsatisfactory samples and false-negative rates remains a matter of concern. Major improvements have been made thanks to the implementation of rapid on-site evaluation (ROSE) in multidisciplinary and integrated medical settings such as one-stop clinics (OSCs). In these settings, clinical and radiological examinations are combined with a morphological study performed by interventional pathologists. The aim of our study was to assess the diagnostic accuracy of the on-site cytopathology advance report (OSCAR) procedure on breast FNA cytologic samples in our breast OSC during the first three years (April 2004 till March 2007) of its implementation. To this goal, we retrospectively analyzed a series of 1820 breast masses (1740 patients) radiologically classified according to the American College of Radiology (ACR) BI-RADS lexicon (67.6% being either BI-RADS 4 or 5), sampled by FNA and immediately diagnosed by cytomorphology. The clinicoradiological, cytomorphological, and histological characteristics of all consecutive patients were retrieved from the hospital computerized medical records prospectively registered in the central information system. Histopathological analysis and ultrasound (US) follow-up (FU) were the reference diagnostic tests of the study design. In brief, we carried out either a histopathological verification or an 18-month US evaluation when a benign cytology was concordant with the components of the triple test. Overall, histology was available for 1138 masses, whereas 491 masses were analyzed at the 18-month US-FU. FNA specimens were morphologically nondiagnostic in 3.1%, false negatives were observed in 1.5%, and there was only one false positive (0.06%). The breast cancer prevalence was 62%. Diagnostic accuracy measures of the OSCAR procedure with their 95% confidence intervals (95% CI) were the following: sensitivity (Se) = 97.4% (96.19-98.31); specificity (Sp) = 94.98% (92.94-96.56); positive predictive value (PPV) = 96.80% (95.48-97.81); negative predictive value (NPV) = 95.91% (94.02-97.33); positive likelihood ratio (LR+) = 19.39 (13.75-27.32); negative predictive ratio (LR-) = 0.03 (0.02-0.04), and; accuracy = 96.45% (95.42-97.31). The respective positive likelihood ratio (LR+) for each of the four categories of cytopathological diagnoses (with their 95% CI) which are malignant, suspicious, benign, and nondiagnostic were 540 (76-3827); 2.69 (1.8-3.96); 0.03 (0.02-0.04); and 0.37 (0.2-0.66), respectively. In conclusion, our study demonstrates that the OSCAR procedure is a highly reliable diagnostic approach and a perfect test to select patients requiring core-needle biopsy (CNB) when performed by interventional cytopathologists in a multidisciplinary and integrated OSC setting. Besides drastically limiting the rate of nondiagnostic specimens and diagnostic turn-around time, OSCAR is an efficient and powerful first-line diagnostic approach for patient-centered care.
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Affiliation(s)
- Voichita Suciu
- Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
| | - Carolla El Chamieh
- Department of Biostatistics and Medical Information, INSERM UMR1153 ECSTRRA Team, Hôpital Saint Louis, AP-HP, 75010 Paris, France
| | - Ranya Soufan
- Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
| | | | | | - Suzette Delaloge
- Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
| | - Zsofia Balogh
- Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
| | | | - Sylvie Chevret
- Department of Biostatistics and Medical Information, INSERM UMR1153 ECSTRRA Team, Hôpital Saint Louis, AP-HP, 75010 Paris, France
| | - Philippe Vielh
- Gustave Roussy, Université Paris-Saclay, 94805 Villejuif, France
- Medipath and American Hospital of Paris, 92200 Paris, France
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31
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Ahn JS, Shin S, Yang SA, Park EK, Kim KH, Cho SI, Ock CY, Kim S. Artificial Intelligence in Breast Cancer Diagnosis and Personalized Medicine. J Breast Cancer 2023; 26:405-435. [PMID: 37926067 PMCID: PMC10625863 DOI: 10.4048/jbc.2023.26.e45] [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: 09/05/2023] [Revised: 09/25/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023] Open
Abstract
Breast cancer is a significant cause of cancer-related mortality in women worldwide. Early and precise diagnosis is crucial, and clinical outcomes can be markedly enhanced. The rise of artificial intelligence (AI) has ushered in a new era, notably in image analysis, paving the way for major advancements in breast cancer diagnosis and individualized treatment regimens. In the diagnostic workflow for patients with breast cancer, the role of AI encompasses screening, diagnosis, staging, biomarker evaluation, prognostication, and therapeutic response prediction. Although its potential is immense, its complete integration into clinical practice is challenging. Particularly, these challenges include the imperatives for extensive clinical validation, model generalizability, navigating the "black-box" conundrum, and pragmatic considerations of embedding AI into everyday clinical environments. In this review, we comprehensively explored the diverse applications of AI in breast cancer care, underlining its transformative promise and existing impediments. In radiology, we specifically address AI in mammography, tomosynthesis, risk prediction models, and supplementary imaging methods, including magnetic resonance imaging and ultrasound. In pathology, our focus is on AI applications for pathologic diagnosis, evaluation of biomarkers, and predictions related to genetic alterations, treatment response, and prognosis in the context of breast cancer diagnosis and treatment. Our discussion underscores the transformative potential of AI in breast cancer management and emphasizes the importance of focused research to realize the full spectrum of benefits of AI in patient care.
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Affiliation(s)
| | | | | | | | | | | | | | - Seokhwi Kim
- Department of Pathology, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
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32
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Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Nuklearmedizin 2023; 62:306-313. [PMID: 37802058 DOI: 10.1055/a-2157-6670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
BACKGROUND Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..
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Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Tobias Hepp
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Ferdinand Seith
- Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
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33
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Schlam I, Saad Menezes MC, Corti C, Tan A, Abuali I, Tolaney SM. Artificial intelligence as an adjunct tool for breast oncologists - are we there yet? ESMO Open 2023; 8:101643. [PMID: 37703594 PMCID: PMC10502370 DOI: 10.1016/j.esmoop.2023.101643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 08/18/2023] [Indexed: 09/15/2023] Open
Affiliation(s)
- I Schlam
- Department of Hematology and Oncology, Tufts Medical Center, Boston; Harvard T.H. Chan School of Public Health, Boston.
| | - M C Saad Menezes
- Harvard T.H. Chan School of Public Health, Boston; Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - C Corti
- Division of New Drugs and Early Drug Development for Innovative Therapies, European Institute of Oncology, IRCCS, Milan; Department of Oncology and Hemato-Oncology (DIPO), University of Milan, Milan, Italy
| | - A Tan
- Department of Biomedical Informatics, Harvard Medical School, Boston, USA
| | - I Abuali
- Department of Hematology and Oncology, Massachusetts General Hospital, Boston
| | - S M Tolaney
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston; Breast Oncology Program, Dana-Farber Brigham Cancer Center, Boston; Harvard Medical School, Boston, USA
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34
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Popat R, Ive J. Embracing the uncertainty in human-machine collaboration to support clinical decision-making for mental health conditions. Front Digit Health 2023; 5:1188338. [PMID: 37731823 PMCID: PMC10508184 DOI: 10.3389/fdgth.2023.1188338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 08/18/2023] [Indexed: 09/22/2023] Open
Abstract
Two significant obstacles exist preventing the widespread usage of Deep Learning (DL) models for predicting healthcare outcomes in general and mental health conditions in particular. Firstly, DL models do not quantify the uncertainty in their predictions, so clinicians are unsure of which predictions they can trust. Secondly, DL models do not triage, i.e., separate which cases could be best handled by the human or the model. This paper attempts to address these obstacles using Bayesian Deep Learning (BDL), which extends DL probabilistically and allows us to quantify the model's uncertainty, which we use to improve human-model collaboration. We implement a range of state-of-the-art DL models for Natural Language Processing and apply a range of BDL methods to these models. Taking a step closer to the real-life scenarios of human-AI collaboration, we propose a Referral Learning methodology for the models that make predictions for certain instances while referring the rest of the instances to a human expert for further assessment. The study demonstrates that models can significantly enhance their performance by seeking human assistance in cases where the model exhibits high uncertainty, which is closely linked to misclassifications. Referral Learning offers two options: (1) supporting humans in cases where the model predicts with certainty, and (2) triaging cases where the model evaluated when it had a better chance of being right than the human by evaluating human disagreement. The latter method combines model uncertainty from BDL and human disagreement from multiple annotations, resulting in improved triaging capabilities.
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Affiliation(s)
- Ram Popat
- Department of Computing, Imperial College London, London, UK
| | - Julia Ive
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK
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35
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Rakha EA, Adebayo LA, Abbas A, Hodi Z, Lee AHS, Ellis IO. Second opinion (external specialist referral) practice of breast pathology: the Nottingham experience. Histopathology 2023; 83:394-405. [PMID: 37356966 DOI: 10.1111/his.14993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 05/23/2023] [Accepted: 06/01/2023] [Indexed: 06/27/2023]
Abstract
AIMS Breast pathology is a challenging field, and discrepancies in diagnoses exist and can affect patient management. This study aims to review a breast referral practice and assess the pattern and frequency of breast lesions sent for an external expert review and evaluate potential impacts on patients' care. METHODS AND RESULTS Seven hundred and forty cases that were referred to Nottingham City Hospital for a second opinion between 2019 and 2022 which have slides and reports were retrieved and reviewed. Reasons for referral, initial diagnosis, proffered specialist opinion and any discrepancy or potential impacts of management were assessed. The most frequent entities were papillary lesions (19%), fibroepithelial lesions (17%), invasive carcinomas that were sent for confirmation of the invasive diagnosis or subtyping of the invasive tumour (17%), intraductal epithelial proliferation with atypia (9%) and spindle cell lesions (8%). Other entities included biphasic tumours such as adenomyoepithelioma, as well as vascular and nipple lesions. Few cases were sent for prognostic classification or comments on the management, and in occasional cases no initial diagnosis was offered. After reviewing the cases by the expert pathologists, the initial diagnosis was confirmed or one of the suggested diagnoses was preferred in 79% of cases, including 129 cases (17%) in which the opinion resulted minor changes in the management. Significant changes in the classification of lesions were made in 132 cases (18%) which resulted in significant change in the patient management recommendation. In 14 cases (2%) a final classification was not possible, and further specialist opinion was obtained. Comments on the differential diagnosis and advice on further patient management were provided in most cases. CONCLUSIONS This study demonstrates the value of external referral of challenging, rare and difficult to classify breast lesions. It also highlights the most common breast lesions that are likely to be challenging, and specialist opinion can refine their classification to improve patient care.
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Affiliation(s)
- Emad A Rakha
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
- Academic Unit for Translational Medical Sciences, School of Medicine, The University of Nottingham, Nottingham, UK
- Pathology Department, Hamad General Hospital, Hamad Medical Corporation, Doha, Qatar
| | - Luqman Adedotun Adebayo
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
| | - Areeg Abbas
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
| | - Zsolt Hodi
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
| | - Andrew H S Lee
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
| | - Ian O Ellis
- Department of Histopathology, Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham, UK
- Academic Unit for Translational Medical Sciences, School of Medicine, The University of Nottingham, Nottingham, UK
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36
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Zhang F, Xu J, Zhang C, Li Y, Gao J, Qu L, Zhang S, Zhu S, Zhang J, Yang B. Three-Dimensional Histological Electrophoresis for High-Throughput Cancer Margin Detection in Multiple Types of Tumor Specimens. NANO LETTERS 2023; 23:7607-7614. [PMID: 37527513 PMCID: PMC10450807 DOI: 10.1021/acs.nanolett.3c02206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/24/2023] [Indexed: 08/03/2023]
Abstract
Accurate identification of tumor margins during cancer surgeries relies on a rapid detection technique that can perform high-throughput detection of multiple suspected tumor lesions at the same time. Unfortunately, the conventional histopathological analysis of frozen tissue sections, which is considered the gold standard, often demonstrates considerable variability, especially in many regions without adequate access to trained pathologists. Therefore, there is a clinical need for a multitumor-suitable complementary tool that can accurately and high-throughput assess tumor margins in every direction within the surgically resected tissue. We herein describe a high-throughput three-dimensional (3D) histological electrophoresis device that uses tumor-specific proteins to identify and contour tumor margins intraoperatively. Testing on seven cell-line xenograft models and human cervical cancer models (representing five types of tissues) demonstrated the high-throughput detection utility of this approach. We anticipate that the 3D histological electrophoresis device will improve the accuracy and efficiency of diagnosing a wide range of cancers.
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Affiliation(s)
- Feiran Zhang
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Jiajun Xu
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Chengbin Zhang
- Department
of Pathology, The First Hospital of Jilin
University, Changchun 130021, P. R. China
| | - Yin Li
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Jiawei Gao
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Limei Qu
- Department
of Pathology, The First Hospital of Jilin
University, Changchun 130021, P. R. China
| | - Songling Zhang
- Department
of Obstetrics and Gynecology, The First
Hospital of Jilin University, Changchun 130021, P. R. China
| | - Shoujun Zhu
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Junhu Zhang
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
| | - Bai Yang
- Joint
Laboratory of Opto-Functional Theranostics in Medicine and Chemistry, The First Hospital of Jilin University, Changchun 130021, P. R. China
- State
Key Laboratory of Supramolecular Structure and Materials, Center for
Supramolecular Chemical Biology, College of Chemistry, Jilin University, Changchun 130012, P. R. China
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37
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Chlorogiannis DD, Verras GI, Tzelepi V, Chlorogiannis A, Apostolos A, Kotis K, Anagnostopoulos CN, Antzoulas A, Davakis S, Vailas M, Schizas D, Mulita F. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation? PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:353-367. [PMID: 38572457 PMCID: PMC10985751 DOI: 10.5114/pg.2023.130337] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/20/2023] [Indexed: 04/05/2024]
Abstract
Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.
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Affiliation(s)
| | | | - Vasiliki Tzelepi
- Department of Pathology, School of Medicine, University of Patras, Patras, Greece
| | | | - Anastasios Apostolos
- First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
| | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | - Spyridon Davakis
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Michail Vailas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Dimitrios Schizas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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Miceli R, Mercado CL, Hernandez O, Chhor C. Active Surveillance for Atypical Ductal Hyperplasia and Ductal Carcinoma In Situ. JOURNAL OF BREAST IMAGING 2023; 5:396-415. [PMID: 38416903 DOI: 10.1093/jbi/wbad026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Indexed: 03/01/2024]
Abstract
Atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS) are relatively common breast lesions on the same spectrum of disease. Atypical ductal hyperblasia is a nonmalignant, high-risk lesion, and DCIS is a noninvasive malignancy. While a benefit of screening mammography is early cancer detection, it also leads to increased biopsy diagnosis of noninvasive lesions. Previously, treatment guidelines for both entities included surgical excision because of the risk of upgrade to invasive cancer after surgery and risk of progression to invasive cancer for DCIS. However, this universal management approach is not optimal for all patients because most lesions are not upgraded after surgery. Furthermore, some DCIS lesions do not progress to clinically significant invasive cancer. Overtreatment of high-risk lesions and DCIS is considered a burden on patients and clinicians and is a strain on the health care system. Extensive research has identified many potential histologic, clinical, and imaging factors that may predict ADH and DCIS upgrade and thereby help clinicians select which patients should undergo surgery and which may be appropriate for active surveillance (AS) with imaging. Additionally, multiple clinical trials are currently underway to evaluate whether AS for DCIS is feasible for a select group of patients. Recent advances in MRI, artificial intelligence, and molecular markers may also have an important role to play in stratifying patients and delineating best management guidelines. This review article discusses the available evidence regarding the feasibility and limitations of AS for ADH and DCIS, as well as recent advances in patient risk stratification.
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Affiliation(s)
- Rachel Miceli
- NYU Langone Health, Department of Radiology, New York, NY, USA
| | | | | | - Chloe Chhor
- NYU Langone Health, Department of Radiology, New York, NY, USA
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Distante A, Marandino L, Bertolo R, Ingels A, Pavan N, Pecoraro A, Marchioni M, Carbonara U, Erdem S, Amparore D, Campi R, Roussel E, Caliò A, Wu Z, Palumbo C, Borregales LD, Mulders P, Muselaers CHJ. Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives. Diagnostics (Basel) 2023; 13:2294. [PMID: 37443687 DOI: 10.3390/diagnostics13132294] [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: 05/31/2023] [Revised: 07/01/2023] [Accepted: 07/04/2023] [Indexed: 07/15/2023] Open
Abstract
Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.
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Affiliation(s)
- Alfredo Distante
- Department of Urology, Catholic University of the Sacred Heart, 00168 Roma, Italy
- Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
| | - Laura Marandino
- Department of Medical Oncology, IRCCS Ospedale San Raffaele, 20132 Milan, Italy
| | - Riccardo Bertolo
- Department of Urology, San Carlo Di Nancy Hospital, 00165 Rome, Italy
| | - Alexandre Ingels
- Department of Urology, University Hospital Henri Mondor, APHP (Assistance Publique-Hôpitaux de Paris), 94000 Créteil, France
| | - Nicola Pavan
- Department of Surgical, Oncological and Oral Sciences, Section of Urology, University of Palermo, 90133 Palermo, Italy
| | - Angela Pecoraro
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, 10043 Turin, Italy
| | - Michele Marchioni
- Department of Medical, Oral and Biotechnological Sciences, G. d'Annunzio University of Chieti, 66100 Chieti, Italy
| | - Umberto Carbonara
- Andrology and Kidney Transplantation Unit, Department of Emergency and Organ Transplantation-Urology, University of Bari, 70121 Bari, Italy
| | - Selcuk Erdem
- Division of Urologic Oncology, Department of Urology, Istanbul University Istanbul Faculty of Medicine, Istanbul 34093, Turkey
| | - Daniele Amparore
- Department of Urology, San Luigi Gonzaga Hospital, University of Turin, Orbassano, 10043 Turin, Italy
| | - Riccardo Campi
- Urological Robotic Surgery and Renal Transplantation Unit, Careggi Hospital, University of Florence, 50121 Firenze, Italy
| | - Eduard Roussel
- Department of Urology, University Hospitals Leuven, 3000 Leuven, Belgium
| | - Anna Caliò
- Section of Pathology, Department of Diagnostic and Public Health, University of Verona, 37134 Verona, Italy
| | - Zhenjie Wu
- Department of Urology, Changhai Hospital, Naval Medical University, Shanghai 200433, China
| | - Carlotta Palumbo
- Division of Urology, Maggiore della Carità Hospital of Novara, Department of Translational Medicine, University of Eastern Piedmont, 13100 Novara, Italy
| | - Leonardo D Borregales
- Department of Urology, Well Cornell Medicine, New York-Presbyterian Hospital, New York, NY 10032, USA
| | - Peter Mulders
- Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
| | - Constantijn H J Muselaers
- Department of Urology, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA Nijmegen, The Netherlands
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40
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Grabenstetter A, Brennan SB, Sevilimedu V, Kuba MG, Giri DD, Wen HY, Morrow M, Brogi E. Is Surgical Excision of Focal Atypical Ductal Hyperplasia Warranted? Experience at a Tertiary Care Center. Ann Surg Oncol 2023; 30:4087-4094. [PMID: 36905438 PMCID: PMC10542905 DOI: 10.1245/s10434-023-13319-4] [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: 01/13/2023] [Accepted: 02/12/2023] [Indexed: 03/12/2023]
Abstract
BACKGROUND The core-needle biopsy (CNB) diagnosis of atypical ductal hyperplasia (ADH) generally mandates follow-up excision, but controversy exists on whether small foci of ADH require surgical management. This study evaluated the upgrade rate at excision of focal ADH (fADH), defined as 1 focus spanning ≤ 2 mm. METHODS We retrospectively identified in-house CNBs with ADH as the highest-risk lesion obtained between January 2013 and December 2017. A radiologist assessed radiologic-pathologic concordance. All CNB slides were reviewed by two breast pathologists, and ADH was classified as fADH and nonfocal ADH based on extent. Only cases with follow-up excision were included. The slides of excision specimens with upgrade were reviewed. RESULTS The final study cohort consisted of 208 radiologic-pathologic concordant CNBs, including 98 fADH and 110 nonfocal ADH. The imaging targets were calcifications (n = 157), a mass (n = 15), nonmass enhancement (n = 27), and mass enhancement (n = 9). Excision of fADH yielded seven (7%) upgrades (5 ductal carcinoma in situ (DCIS), 2 invasive carcinoma) versus 24 (22%) upgrades (16 DCIS, 8 invasive carcinoma) at excision of nonfocal ADH (p = 0.01). Both invasive carcinomas found at excision of fADH were subcentimeter tubular carcinomas away from the biopsy site and deemed incidental. CONCLUSIONS Our data show a significantly lower upgrade rate at excision of focal ADH than nonfocal ADH. This information can be valuable if nonsurgical management of patients with radiologic-pathologic concordant CNB diagnosis of focal ADH is being considered.
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Affiliation(s)
- Anne Grabenstetter
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Sandra B Brennan
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Varadan Sevilimedu
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - M Gabriela Kuba
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dilip D Giri
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hannah Yong Wen
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Monica Morrow
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Edi Brogi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Ma T, Semsarian CR, Barratt A, Parker L, Pathmanathan N, Nickel B, Bell KJL. Should low-risk DCIS lose the cancer label? An evidence review. Breast Cancer Res Treat 2023; 199:415-433. [PMID: 37074481 PMCID: PMC10175360 DOI: 10.1007/s10549-023-06934-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/30/2023] [Indexed: 04/20/2023]
Abstract
BACKGROUND Population mammographic screening for breast cancer has led to large increases in the diagnosis and treatment of ductal carcinoma in situ (DCIS). Active surveillance has been proposed as a management strategy for low-risk DCIS to mitigate against potential overdiagnosis and overtreatment. However, clinicians and patients remain reluctant to choose active surveillance, even within a trial setting. Re-calibration of the diagnostic threshold for low-risk DCIS and/or use of a label that does not include the word 'cancer' might encourage the uptake of active surveillance and other conservative treatment options. We aimed to identify and collate relevant epidemiological evidence to inform further discussion on these ideas. METHODS We searched PubMed and EMBASE databases for low-risk DCIS studies in four categories: (1) natural history; (2) subclinical cancer found at autopsy; (3) diagnostic reproducibility (two or more pathologist interpretations at a single time point); and (4) diagnostic drift (two or more pathologist interpretations at different time points). Where we identified a pre-existing systematic review, the search was restricted to studies published after the inclusion period of the review. Two authors screened records, extracted data, and performed risk of bias assessment. We undertook a narrative synthesis of the included evidence within each category. RESULTS Natural History (n = 11): one systematic review and nine primary studies were included, but only five provided evidence on the prognosis of women with low-risk DCIS. These studies reported that women with low-risk DCIS had comparable outcomes whether or not they had surgery. The risk of invasive breast cancer in patients with low-risk DCIS ranged from 6.5% (7.5 years) to 10.8% (10 years). The risk of dying from breast cancer in patients with low-risk DCIS ranged from 1.2 to 2.2% (10 years). Subclinical cancer at autopsy (n = 1): one systematic review of 13 studies estimated the mean prevalence of subclinical in situ breast cancer to be 8.9%. Diagnostic reproducibility (n = 13): two systematic reviews and 11 primary studies found at most moderate agreement in differentiating low-grade DCIS from other diagnoses. Diagnostic drift: no studies found. CONCLUSION Epidemiological evidence supports consideration of relabelling and/or recalibrating diagnostic thresholds for low-risk DCIS. Such diagnostic changes would need agreement on the definition of low-risk DCIS and improved diagnostic reproducibility.
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Affiliation(s)
- Tara Ma
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Caitlin R Semsarian
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Alexandra Barratt
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
- Wiser Healthcare, Sydney, Australia
| | - Lisa Parker
- Sydney School of Pharmacy, Charles Perkins Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia
- Department of Radiation Oncology, Royal North Shore Hospital, Sydney, Australia
| | - Nirmala Pathmanathan
- Western Sydney Local Health District, Sydney, Australia
- Westmead Breast Cancer Institute, Westmead Hospital, Sydney, Australia
| | - Brooke Nickel
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Katy J L Bell
- School of Public Health, The University of Sydney, Sydney, NSW, 2006, Australia.
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Wang J, Qin L, Chen D, Wang J, Han BW, Zhu Z, Qiao G. An improved Hover-net for nuclear segmentation and classification in histopathology images. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08394-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
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Brunyé TT, Balla A, Drew T, Elmore JG, Kerr KF, Shucard H, Weaver DL. From Image to Diagnosis: Characterizing Sources of Error in Histopathologic Interpretation. Mod Pathol 2023; 36:100162. [PMID: 36948400 DOI: 10.1016/j.modpat.2023.100162] [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: 11/04/2022] [Revised: 02/11/2023] [Accepted: 03/07/2023] [Indexed: 03/24/2023]
Abstract
An accurate histopathologic diagnosis on surgical biopsy material is necessary for the clinical management of patients and has important implications for research, clinical trial design/enrollment, and public health education. This study used a mixed methods approach to isolate sources of diagnostic error while residents and attending pathologists interpreted digitized breast biopsy slides. Ninety participants including pathology residents and attendings at major United States medical centers reviewed a set of 14 digitized whole slide images of breast biopsies. Each case had a consensus-defined diagnosis and critical region of interest (cROI) representing the most significant pathology on the slide. Participants were asked to view unmarked digitized slides, draw their own participant region of interest (pROI), describe its features, and render a diagnosis. Participants' review behavior was tracked using case viewer software and an eye-tracking device. Diagnostic accuracy was calculated in comparison to the consensus diagnosis. We measured the frequency of errors emerging during four interpretive phases: 1) detecting the cROI, 2) recognizing its relevance, 3) using the correct terminology to describe findings in the pROI, and 4) making a diagnostic decision. According to eye tracking data, both trainees and attending pathologists were very likely (about 94% of the time) to find the cROI when inspecting a slide. However, trainees were less likely to consider the cROI relevant to their diagnosis. Pathology trainees were more likely (41% of cases) to use incorrect terminology to describe pROI features than attending pathologists (21% of cases). Failure to accurately describe features was the only factor strongly associated with an incorrect diagnosis. Identifying where errors emerge in the interpretive and/or descriptive process and working on building organ-specific feature recognition and verbal fluency in describing those features are critical steps for achieving competency in diagnostic decision making.
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Affiliation(s)
- Tad T Brunyé
- Center for Applied Brain and Cognitive Sciences, Tufts University, 177 College Ave., Suite 090, Medford, MA 02155; Department of Psychology, Tufts University, 490 Boston Ave., Medford, MA 02155.
| | - Agnes Balla
- Department of Pathology, University of Vermont and Vermont Cancer Center, 89 Beaumont Ave., Burlington, VT 05405
| | - Trafton Drew
- Department of Psychology, University of Utah, 380 S 1530 E Beh S 502, Salt Lake City, UT 84112
| | - Joann G Elmore
- David Geffen School of Medicine, Department of Medicine, University of California, Los Angeles, 885 Tiverton Drive, Los Angeles, CA 90095
| | - Kathleen F Kerr
- Department of Biostatistics, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195
| | - Hannah Shucard
- Department of Biostatistics, University of Washington, 1705 NE Pacific Street, Seattle, WA 98195
| | - Donald L Weaver
- Department of Pathology, University of Vermont and Vermont Cancer Center, 89 Beaumont Ave., Burlington, VT 05405
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Brunyé TT, Drew T, Kerr KF, Shucard H, Powell K, Weaver DL, Elmore JG. Zoom behavior during visual search modulates pupil diameter and reflects adaptive control states. PLoS One 2023; 18:e0282616. [PMID: 36893083 PMCID: PMC9997932 DOI: 10.1371/journal.pone.0282616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 02/19/2023] [Indexed: 03/10/2023] Open
Abstract
Adaptive gain theory proposes that the dynamic shifts between exploration and exploitation control states are modulated by the locus coeruleus-norepinephrine system and reflected in tonic and phasic pupil diameter. This study tested predictions of this theory in the context of a societally important visual search task: the review and interpretation of digital whole slide images of breast biopsies by physicians (pathologists). As these medical images are searched, pathologists encounter difficult visual features and intermittently zoom in to examine features of interest. We propose that tonic and phasic pupil diameter changes during image review may correspond to perceived difficulty and dynamic shifts between exploration and exploitation control states. To examine this possibility, we monitored visual search behavior and tonic and phasic pupil diameter while pathologists (N = 89) interpreted 14 digital images of breast biopsy tissue (1,246 total images reviewed). After viewing the images, pathologists provided a diagnosis and rated the level of difficulty of the image. Analyses of tonic pupil diameter examined whether pupil dilation was associated with pathologists' difficulty ratings, diagnostic accuracy, and experience level. To examine phasic pupil diameter, we parsed continuous visual search data into discrete zoom-in and zoom-out events, including shifts from low to high magnification (e.g., 1× to 10×) and the reverse. Analyses examined whether zoom-in and zoom-out events were associated with phasic pupil diameter change. Results demonstrated that tonic pupil diameter was associated with image difficulty ratings and zoom level, and phasic pupil diameter showed constriction upon zoom-in events, and dilation immediately preceding a zoom-out event. Results are interpreted in the context of adaptive gain theory, information gain theory, and the monitoring and assessment of physicians' diagnostic interpretive processes.
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Affiliation(s)
- Tad T. Brunyé
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA, United States of America
| | - Trafton Drew
- Department of Psychology, University of Utah, Salt Lake City, UT, United States of America
| | - Kathleen F. Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Hannah Shucard
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Kate Powell
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA, United States of America
| | - Donald L. Weaver
- Department of Pathology, University of Vermont and Vermont Cancer Center, Burlington, VT, United States of America
| | - Joann G. Elmore
- David Geffen School of Medicine, Department of Medicine, University of California, Los Angeles, CA, United States of America
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Basu A, Senapati P, Deb M, Rai R, Dhal KG. A survey on recent trends in deep learning for nucleus segmentation from histopathology images. EVOLVING SYSTEMS 2023; 15:1-46. [PMID: 38625364 PMCID: PMC9987406 DOI: 10.1007/s12530-023-09491-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Accepted: 02/13/2023] [Indexed: 03/08/2023]
Abstract
Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017-2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.
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Affiliation(s)
- Anusua Basu
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, Midnapore, West Bengal India
| | - Pradip Senapati
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, Midnapore, West Bengal India
| | - Mainak Deb
- Wipro Technologies, Pune, Maharashtra India
| | - Rebika Rai
- Department of Computer Applications, Sikkim University, Sikkim, India
| | - Krishna Gopal Dhal
- Department of Computer Science and Application, Midnapore College (Autonomous), Paschim Medinipur, Midnapore, West Bengal India
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Drew T, Konold CE, Lavelle M, Brunyé TT, Kerr KF, Shucard H, Weaver DL, Elmore JG. Pathologist pupil dilation reflects experience level and difficulty in diagnosing medical images. J Med Imaging (Bellingham) 2023; 10:025503. [PMID: 37096053 PMCID: PMC10122150 DOI: 10.1117/1.jmi.10.2.025503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 03/26/2023] [Accepted: 04/10/2023] [Indexed: 04/26/2023] Open
Abstract
Purpose: Digital whole slide imaging allows pathologists to view slides on a computer screen instead of under a microscope. Digital viewing allows for real-time monitoring of pathologists' search behavior and neurophysiological responses during the diagnostic process. One particular neurophysiological measure, pupil diameter, could provide a basis for evaluating clinical competence during training or developing tools that support the diagnostic process. Prior research shows that pupil diameter is sensitive to cognitive load and arousal, and it switches between exploration and exploitation of a visual image. Different categories of lesions in pathology pose different levels of challenge, as indicated by diagnostic disagreement among pathologists. If pupil diameter is sensitive to the perceived difficulty in diagnosing biopsies, eye-tracking could potentially be used to identify biopsies that may benefit from a second opinion. Approach: We measured case onset baseline-corrected (phasic) and uncorrected (tonic) pupil diameter in 90 pathologists who each viewed and diagnosed 14 digital breast biopsy cases that cover the diagnostic spectrum from benign to invasive breast cancer. Pupil data were extracted from the beginning of viewing and interpreting of each individual case. After removing 122 trials ( < 10 % ) with poor eye-tracking quality, 1138 trials remained. We used multiple linear regression with robust standard error estimates to account for dependent observations within pathologists. Results: We found a positive association between the magnitude of phasic dilation and subject-centered difficulty ratings and between the magnitude of tonic dilation and untransformed difficulty ratings. When controlling for case diagnostic category, only the tonic-difficulty relationship persisted. Conclusions: Results suggest that tonic pupil dilation may indicate overall arousal differences between pathologists as they interpret biopsy cases and could signal a need for additional training, experience, or automated decision aids. Phasic dilation is sensitive to characteristics of biopsies that tend to elicit higher difficulty ratings and could indicate a need for a second opinion.
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Affiliation(s)
- Trafton Drew
- University of Utah, Department of Psychology, Salt Lake City, Utah, United States
| | - Catherine E. Konold
- University of Utah, Department of Psychology, Salt Lake City, Utah, United States
| | - Mark Lavelle
- University of New Mexico, Department of Psychology, Albuquerque, New Mexico, United States
| | - Tad T. Brunyé
- Tufts University, Center for Applied Brain and Cognitive Sciences, Medford, Massachusetts, United States
| | - Kathleen F. Kerr
- University of Washington, Department of Biostatistics, Seattle, Washington, United States
| | - Hannah Shucard
- University of Washington, Department of Biostatistics, Seattle, Washington, United States
| | - Donald L. Weaver
- University of Vermont, Department of Pathology & Laboratory Medicine, Burlington, Vermont, United States
| | - Joann G. Elmore
- David Geffen School of Medicine UCLA, Department of Medicine, Los Angeles, California, United States
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47
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Translational Potential of Fluorescence Polarization for Breast Cancer Cytopathology. Cancers (Basel) 2023; 15:cancers15051501. [PMID: 36900291 PMCID: PMC10000687 DOI: 10.3390/cancers15051501] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 02/24/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
Breast cancer is the most common malignancy in women. The standard of care for diagnosis involves invasive core needle biopsy followed by time-consuming histopathological evaluation. A rapid, accurate, and minimally invasive method to diagnose breast cancer would be invaluable. Therefore, this clinical study investigated the fluorescence polarization (Fpol) of the cytological stain methylene blue (MB) for the quantitative detection of breast cancer in fine needle aspiration (FNA) specimens. Cancerous, benign, and normal cells were aspirated from excess breast tissues immediately following surgery. The cells were stained in aqueous MB solution (0.05 mg/mL) and imaged using multimodal confocal microscopy. The system provided MB Fpol and fluorescence emission images of the cells. Results from optical imaging were compared to clinical histopathology. In total, we imaged and analyzed 3808 cells from 44 breast FNAs. Fpol images displayed quantitative contrast between cancerous and noncancerous cells, whereas fluorescence emission images showed the morphological features comparable to cytology. Statistical analysis demonstrated that MB Fpol is significantly higher (p < 0.0001) in malignant vs. benign/normal cells. It also revealed a correlation between MB Fpol values and tumor grade. The results indicate that MB Fpol could provide a reliable, quantitative diagnostic marker for breast cancer at the cellular level.
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Scalco R, Hamsafar Y, White CL, Schneider JA, Reichard RR, Prokop S, Perrin RJ, Nelson PT, Mooney S, Lieberman AP, Kukull WA, Kofler J, Keene CD, Kapasi A, Irwin DJ, Gutman DA, Flanagan ME, Crary JF, Chan KC, Murray ME, Dugger BN. The status of digital pathology and associated infrastructure within Alzheimer's Disease Centers. J Neuropathol Exp Neurol 2023; 82:202-211. [PMID: 36692179 PMCID: PMC9941826 DOI: 10.1093/jnen/nlac127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Digital pathology (DP) has transformative potential, especially for Alzheimer disease and related disorders. However, infrastructure barriers may limit adoption. To provide benchmarks and insights into implementation barriers, a survey was conducted in 2019 within National Institutes of Health's Alzheimer's Disease Centers (ADCs). Questions covered infrastructure, funding sources, and data management related to digital pathology. Of the 35 ADCs to which the survey was sent, 33 responded. Most respondents (81%) stated that their ADC had digital slide scanner access, with the most frequent brand being Aperio/Leica (62.9%). Approximately a third of respondents stated there were fees to utilize the scanner. For DP and machine learning (ML) resources, 41% of respondents stated none was supported by their ADC. For scanner purchasing and operations, 50% of respondents stated they received institutional support. Some were unsure of the file size of scanned digital images (37%) and total amount of storage space files occupied (50%). Most (76%) were aware of other departments at their institution working with ML; a similar (76%) percentage were unaware of multiuniversity or industry partnerships. These results demonstrate many ADCs have access to a digital slide scanner; additional investigations are needed to further understand hurdles to implement DP and ML workflows.
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Affiliation(s)
- Rebeca Scalco
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Yamah Hamsafar
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
| | - Charles L White
- Department of Pathology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | | | | | - Stefan Prokop
- Department of Pathology, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Richard J Perrin
- Department of Pathology and Immunology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, USA
- Knight Alzheimer’s Disease Research Center, Washington University School of Medicine, Saint Louis, Missouri, USA
| | | | - Sean Mooney
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Andrew P Lieberman
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, USA
| | - Walter A Kukull
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Julia Kofler
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Christopher Dirk Keene
- Department Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA
| | | | - David J Irwin
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - David A Gutman
- Departments of Neurology, Psychiatry, and Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Margaret E Flanagan
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - John F Crary
- Department of Pathology, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Neuroscience, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Artificial Intelligence & Human Health, Ronald M. Loeb Center for Alzheimer’s Disease, Friedman Brain Institute, Neuropathology Brain Bank & Research CoRE, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kwun C Chan
- Institute for Medical Data Science and Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Melissa E Murray
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
| | - Brittany N Dugger
- Department of Pathology and Laboratory Medicine, University of California-Davis, Sacramento, California, USA
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Calle C, Zhong E, Hanna MG, Ventura K, Friedlander MA, Morrow M, Cody H, Brogi E. Changes in the Diagnoses of Breast Core Needle Biopsies on Second Review at a Tertiary Care Center: Implications for Surgical Management. Am J Surg Pathol 2023; 47:172-182. [PMID: 36638314 PMCID: PMC10464622 DOI: 10.1097/pas.0000000000002002] [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] [Indexed: 01/15/2023]
Abstract
Core needle biopsy (CNB) of breast lesions is routine for diagnosis and treatment planning. Despite refinement of diagnostic criteria, the diagnosis of breast lesions on CNB can be challenging. At many centers, including ours, confirmation of diagnoses rendered in other laboratories is required before treatment planning. We identified CNBs first diagnosed elsewhere that were reviewed in our department over the course of 1 year because the patients sought care at our center and in which a change in diagnosis had been recorded. The outside and in-house CNB diagnoses were then classified based on Breast WHO Fifth Edition diagnostic categories. The impact of the change in diagnosis was estimated based on the subsequent surgical management. Findings in follow-up surgical excisions (EXCs) were used for validation. In 2018, 4950 outside cases with CNB were reviewed at our center. A total of 403 CNBs diagnoses were discrepant. Of these, 147 had a change in the WHO diagnostic category: 80 (54%) CNBs had a more severe diagnosis and 44 (30%) a less severe diagnosis. In 23 (16%) CNBs, the change of diagnostic category had no impact on management. Intraductal proliferations (n=54), microinvasive carcinoma (n=18), and papillary lesions (n=35) were the most disputed diagnoses. The in-house CNB diagnosis was confirmed in most cases with available excisions. Following CNB reclassification, 22/147 (15%) lesions were not excised. A change affecting the surgical management at our center occurred in 2.5% of all CNBs. Our results support routine review of outside breast CNB as a clinically significant practice before definitive treatment.
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Affiliation(s)
- Catarina Calle
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York, 10065 USA
- Faculdade de Ciencias da Saude da Universidade da Beira Interior, Covilha, Portugal
| | - Elaine Zhong
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York, 10065 USA
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York, 10065 USA
| | - Katia Ventura
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York, 10065 USA
| | - Maria A. Friedlander
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York, 10065 USA
| | - Monica Morrow
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Hiram Cody
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York City, New York, USA
| | - Edi Brogi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York City, New York, 10065 USA
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Clayton DA, Eguchi MM, Kerr KF, Miyoshi K, Brunyé TT, Drew T, Weaver DL, Elmore JG. Are Pathologists Self-Aware of Their Diagnostic Accuracy? Metacognition and the Diagnostic Process in Pathology. Med Decis Making 2023; 43:164-174. [PMID: 36124966 PMCID: PMC9825636 DOI: 10.1177/0272989x221126528] [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] [Indexed: 02/03/2023]
Abstract
BACKGROUND Metacognition is a cognitive process that involves self-awareness of thinking, understanding, and performance. This study assesses pathologists' metacognition by examining the association between their diagnostic accuracy and self-reported confidence levels while interpreting skin and breast biopsies. DESIGN We studied 187 pathologists from the Melanoma Pathology Study (M-Path) and 115 pathologists from the Breast Pathology Study (B-Path). We measured pathologists' metacognitive ability by examining the area under the curve (AUC), the area under each pathologist's receiver operating characteristic (ROC) curve summarizing the association between confidence and diagnostic accuracy. We investigated possible relationships between this AUC measure, referred to as metacognitive sensitivity, and pathologist attributes. We also assessed whether higher metacognitive sensitivity affected the association between diagnostic accuracy and a secondary diagnostic action such as requesting a second opinion. RESULTS We found no significant associations between pathologist clinical attributes and metacognitive AUC. However, we found that pathologists with higher AUC showed a stronger trend to request secondary diagnostic action for inaccurate diagnoses and not for accurate diagnoses compared with pathologists with lower AUC. LIMITATIONS Pathologists reported confidence in specific diagnostic terms, rather than the broader classes into which the diagnostic terms were later grouped to determine accuracy. In addition, while there is no gold standard for the correct diagnosis to determine the accuracy of pathologists' interpretations, our studies achieved a high-quality reference diagnosis by using the consensus diagnosis of 3 experienced pathologists. CONCLUSIONS Metacognition can affect clinical decisions. If pathologists have self-awareness that their diagnosis may be inaccurate, they can request additional tests or second opinions, providing the opportunity to correct inaccurate diagnoses. HIGHLIGHTS Metacognitive sensitivity varied across pathologists, with most showing higher sensitivity than expected by chance.None of the demographic or clinical characteristics we examined was significantly associated with metacognitive sensitivity.Pathologists with higher metacognitive sensitivity were more likely to request additional tests or second opinions for their inaccurate diagnoses.
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Affiliation(s)
- Dayna A. Clayton
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America
| | - Megan M. Eguchi
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America
| | - Kathleen F. Kerr
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Kiyofumi Miyoshi
- Department of Psychology, University of California, Los Angeles, CA, United States of America
| | - Tad T. Brunyé
- Center for Applied Brain and Cognitive Sciences, Tufts University, Medford, MA, United States of America
| | - Trafton Drew
- Department of Psychology, University of Utah, Salt Lake City, UT, United States of America
| | - Donald L. Weaver
- Department of Pathology & Laboratory Medicine, University of Vermont Larner College of Medicine, Burlington, VT
| | - Joann G. Elmore
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America
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