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Claeson M, Tan SX, Lambie D, Brown S, Walsh MD, Baade PD, Pandeya N, Whitehead KJ, Soyer HP, Smithers BM, Whiteman DC, Khosrotehrani K. The association between BRAF-V600E mutations and death from thin (≤1.00 mm) melanomas: A nested case-case study from Queensland, Australia. J Eur Acad Dermatol Venereol 2023; 37:e1168-e1172. [PMID: 37147869 DOI: 10.1111/jdv.19173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Accepted: 04/26/2023] [Indexed: 05/07/2023]
Affiliation(s)
- M Claeson
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Dermatology Research Centre, Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
- Department of Dermatology and Venereology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - S X Tan
- Dermatology Research Centre, Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
| | - D Lambie
- Anatomical Pathology, Princess Alexandra Hospital, Pathology Queensland, Brisbane, Queensland, Australia
- University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
| | - S Brown
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
- Dermatology Research Centre, Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
| | - M D Walsh
- Histopathology Department, Sullivan Nicolaides Pathology, Brisbane, Queensland, Australia
| | - P D Baade
- Cancer Council Queensland, Queensland, Australia
- Menzies Health Institute Queensland, Griffith University, Gold Coast, Queensland, Australia
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
| | - N Pandeya
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K J Whitehead
- Histopathology Department, Sullivan Nicolaides Pathology, Brisbane, Queensland, Australia
| | - H P Soyer
- Dermatology Research Centre, Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
- Department of Dermatology, Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - B M Smithers
- Queensland Melanoma Project, University of Queensland, Princess Alexandra Hospital, Brisbane, Queensland, Australia
| | - D C Whiteman
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - K Khosrotehrani
- Dermatology Research Centre, Experimental Dermatology Group, University of Queensland Diamantina Institute, Brisbane, Queensland, Australia
- Department of Dermatology, Princess Alexandra Hospital, Brisbane, Queensland, Australia
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Krishnan RP, Ramani P, Pandiar D. Plausible mechanisms in the pathobiology of acantholytic squamous cell carcinoma: An evidence based hypothesis. Med Hypotheses 2022. [DOI: 10.1016/j.mehy.2022.110946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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DeepRePath: Identifying the Prognostic Features of Early-Stage Lung Adenocarcinoma Using Multi-Scale Pathology Images and Deep Convolutional Neural Networks. Cancers (Basel) 2021; 13:cancers13133308. [PMID: 34282757 PMCID: PMC8268823 DOI: 10.3390/cancers13133308] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Revised: 06/10/2021] [Accepted: 06/28/2021] [Indexed: 12/12/2022] Open
Abstract
Simple Summary Pathology images are vital for understanding solid cancers. In this study, we created DeepRePath using multi-scale pathology images with two-channel deep learning to predict the prognosis of patients with early-stage lung adenocarcinoma (LUAD). DeepRePath demonstrated that it could predict the recurrence of early-stage LUAD with average area under the curve scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Pathological features found to be associated with a high probability of recurrence included tumor necrosis, discohesive tumor cells, and atypical nuclei. In conclusion, DeepRePath can improve the treatment modality for patients with early-stage LUAD through recurrence prediction. Abstract The prognosis of patients with lung adenocarcinoma (LUAD), especially early-stage LUAD, is dependent on clinicopathological features. However, its predictive utility is limited. In this study, we developed and trained a DeepRePath model based on a deep convolutional neural network (CNN) using multi-scale pathology images to predict the prognosis of patients with early-stage LUAD. DeepRePath was pre-trained with 1067 hematoxylin and eosin-stained whole-slide images of LUAD from the Cancer Genome Atlas. DeepRePath was further trained and validated using two separate CNNs and multi-scale pathology images of 393 resected lung cancer specimens from patients with stage I and II LUAD. Of the 393 patients, 95 patients developed recurrence after surgical resection. The DeepRePath model showed average area under the curve (AUC) scores of 0.77 and 0.76 in cohort I and cohort II (external validation set), respectively. Owing to low performance, DeepRePath cannot be used as an automated tool in a clinical setting. When gradient-weighted class activation mapping was used, DeepRePath indicated the association between atypical nuclei, discohesive tumor cells, and tumor necrosis in pathology images showing recurrence. Despite the limitations associated with a relatively small number of patients, the DeepRePath model based on CNNs with transfer learning could predict recurrence after the curative resection of early-stage LUAD using multi-scale pathology images.
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