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Mota SM, Priester A, Shubert J, Bong J, Sayre J, Berry-Pusey B, Brisbane WG, Natarajan S. Artificial Intelligence Improves the Ability of Physicians to Identify Prostate Cancer Extent. J Urol 2024; 212:52-62. [PMID: 38860576 PMCID: PMC11178250 DOI: 10.1097/ju.0000000000003960] [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: 09/08/2023] [Accepted: 03/28/2024] [Indexed: 06/12/2024]
Abstract
PURPOSE Defining prostate cancer contours is a complex task, undermining the efficacy of interventions such as focal therapy. A multireader multicase study compared physicians' performance using artificial intelligence (AI) vs standard-of-care methods for tumor delineation. MATERIALS AND METHODS Cases were interpreted by 7 urologists and 3 radiologists from 5 institutions with 2 to 23 years of experience. Each reader evaluated 50 prostatectomy cases retrospectively eligible for focal therapy. Each case included a T2-weighted MRI, contours of the prostate and region(s) of interest suspicious for cancer, and a biopsy report. First, readers defined cancer contours cognitively, manually delineating tumor boundaries to encapsulate all clinically significant disease. Then, after ≥ 4 weeks, readers contoured the same cases using AI software. Using tumor boundaries on whole-mount histopathology slides as ground truth, AI-assisted, cognitively-defined, and hemigland cancer contours were evaluated. Primary outcome measures were the accuracy and negative margin rate of cancer contours. All statistical analyses were performed using generalized estimating equations. RESULTS The balanced accuracy (mean of voxel-wise sensitivity and specificity) of AI-assisted cancer contours (84.7%) was superior to cognitively-defined (67.2%) and hemigland contours (75.9%; P < .0001). Cognitively-defined cancer contours systematically underestimated cancer extent, with a negative margin rate of 1.6% compared to 72.8% for AI-assisted cancer contours (P < .0001). CONCLUSIONS AI-assisted cancer contours reduce underestimation of prostate cancer extent, significantly improving contouring accuracy and negative margin rate achieved by physicians. This technology can potentially improve outcomes, as accurate contouring informs patient management strategy and underpins the oncologic efficacy of treatment.
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Affiliation(s)
| | - Alan Priester
- Avenda Health, Inc
- Department of Urology, David Geffen School of Medicine, Los Angeles, California
| | | | | | - James Sayre
- Department of Radiological Sciences and Biostatistics, University of California, Los Angeles, California
| | | | - Wayne G Brisbane
- Department of Urology, David Geffen School of Medicine, Los Angeles, California
| | - Shyam Natarajan
- Avenda Health, Inc
- Department of Urology, David Geffen School of Medicine, Los Angeles, California
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Ma X, Cai S, Lu J, Rao S, Zhou J, Zeng M, Pan X. The Added Value of ADC-based Nomogram in Assessing the Depth of Myometrial Invasion of Endometrial Endometrioid Adenocarcinoma. Acad Radiol 2024; 31:2324-2333. [PMID: 38016822 DOI: 10.1016/j.acra.2023.11.016] [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: 08/24/2023] [Revised: 10/28/2023] [Accepted: 11/08/2023] [Indexed: 11/30/2023]
Abstract
RATIONALE AND OBJECTIVES To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA). MATERIALS AND METHODS Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared. RESULTS Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist_1, 73.3% vs 86.2% (p = 0.001); radiologist_2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist_1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist_2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001). CONCLUSION The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions.
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Affiliation(s)
- Xiaoliang Ma
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Songqi Cai
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Jingjing Lu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Shengxiang Rao
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Jianjun Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Mengsu Zeng
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, People's Republic of China (X.M., S.C., J.L., S.R., J.Z., MZ.)
| | - Xiaoping Pan
- Department of Radiology, Lishui People's Hospital, Dazhong Road, Zhejiang, People's Republic of China (X.P.).
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Chen J, Wang X, Xu Q, Zhang W, Chen H, Gu H, Tang W, Tian Y, Wang Z. Development and external validation of a nomogram for predicting overall survival of patients with non-endometrioid endometrial cancer: A population-based analysis. Heliyon 2024; 10:e28864. [PMID: 38596036 PMCID: PMC11002679 DOI: 10.1016/j.heliyon.2024.e28864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
Objectives The main objective of this study was to identify the key predictors and construct a nomogram that can be used to predict the overall survival of individuals with non-endometrioid endometrial cancer. Methods A total of 2686 non-endometrioid endometrial cancer patients confirmed between 1988 and 2018 were selected from the Surveillance, Epidemiology, and End Results database. They were divided into a training cohort and an internal validation cohort. Independent risk factors were chosen by Cox regression analyses. A predictive nomogram model for overall survival was constructed based on above factors. A Chinese cohort of 41 patients was collected to be an external validation cohort. Results Eight variables were estimated as independent predictors for overall survival. A nomogram was established using these factors. The C-index for predicting the overall survival of patients with non-endometrioid endometrial cancer from the nomogram was 0.734, 0.700, and 0.767 in training, internal, and external validation cohort, respectively. Calibration plots and decision curve analysis showed that the nomogram was valuable for further clinical application. Conclusion We constructed a nomogram which can be used as an effective tool to predict the 3- and 5-year overall survival of Non-endometrioid endometrial cancer patients.
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Affiliation(s)
- Jingya Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | | | - Qinfeng Xu
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Wei Zhang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
| | - Hu Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Hailei Gu
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Wenwei Tang
- Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing, China
| | - Ying Tian
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Zhongqiu Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
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Fischerova D, Smet C, Scovazzi U, Sousa DN, Hundarova K, Haldorsen IS. Staging by imaging in gynecologic cancer and the role of ultrasound: an update of European joint consensus statements. Int J Gynecol Cancer 2024; 34:363-378. [PMID: 38438175 PMCID: PMC10958454 DOI: 10.1136/ijgc-2023-004609] [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: 12/05/2023] [Accepted: 01/05/2024] [Indexed: 03/06/2024] Open
Abstract
In recent years the role of diagnostic imaging by pelvic ultrasound in the diagnosis and staging of gynecological cancers has been growing exponentially. Evidence from recent prospective multicenter studies has demonstrated high accuracy for pre-operative locoregional ultrasound staging in gynecological cancers. Therefore, in many leading gynecologic oncology units, ultrasound is implemented next to pelvic MRI as the first-line imaging modality for gynecological cancer. The work herein is a consensus statement on the role of pre-operative imaging by ultrasound and other imaging modalities in gynecological cancer, following European Society guidelines.
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Affiliation(s)
- Daniela Fischerova
- Gynecologic Oncology Center, Department of Gynecology, Obstetrics and Neonatology, First Faculty of Medicine, Charles University and General University Hospital in Prague, Prague, Czech Republic
| | - Carolina Smet
- Department of Obstetrics and Gynecology, São Francisco de Xavier Hospital in Lisbon, Lisbon, Portugal
| | - Umberto Scovazzi
- Department of Gynecology and Obstetrics, Ospedale Policlinico San Martino and University of Genoa, Genoa, Italy
| | | | - Kristina Hundarova
- Department of Gynecology and Obstetrics A, Hospital and University Centre of Coimbra, Coimbra, Portugal
| | - Ingfrid Salvesen Haldorsen
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology and Department of Clinical Medicine, Haukeland University Hospital and the University of Bergen, Bergen, Norway
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Ahmed B, Sheikhzadeh P, Changizi V, Abbasi M, Soleymani Y, Sarhan W, Rahmim A. CT radiomics analysis of primary colon cancer patients with or without liver metastases: a correlative study with [ 18F]FDG PET uptake values. Abdom Radiol (NY) 2023; 48:3297-3309. [PMID: 37453942 DOI: 10.1007/s00261-023-03999-7] [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: 04/16/2023] [Revised: 06/23/2023] [Accepted: 06/26/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE Utilizing [18F]Fluoro-2-deoxy-D-glucose Positron Emission Tomography/Computed Tomography ([18F]FDG PET/CT) scans on primary colon cancer (CC) patients including with liver metastases (LM), we aimed to determine the relationship between structural CT radiomic features and metabolic PET standard uptake value (SUV) in these patients. MATERIAL AND METHOD A retrospective analysis was performed on 60 patients with primary CC, of which 40 had liver metastases that were more than 2 cm in diameter. [18F]FDG PET/CT was used to calculate SUVmax, and 42 CT radiomic characteristics were extracted from non-enhanced CT images. Tumors were manually segmented on fused PET/CT scans by two experienced nuclear medicine physicians. Sixty primary CC and forty LM lesions were segmented accordingly. In the cases of multiple LM lesions, the lesion with the largest diameter was chosen for segmentation. In a univariate analysis approach, we used Spearman correlation with multiple testing correction (Benjamini-Hutchberg false discovery rate (FDR), α = 0.05) to ascertain the relationship between SUVmax and CT radiomic features. RESULT Twenty-two (52.3%) and twenty-six (61.9%) CT radiomic features were found to be significantly correlated with SUVmax values of primary CC (n = 60) and LM (n = 40) lesions, respectively (FDR-corrected p value < 0.05 and 0.6 < |ρ| < 1). GLCM_homogeneity (ρ = 0.839), GLCM_dissimilarity (ρ = - 0.832), GLZLM_ZLNU (ρ = 0.827), and GLCM_contrast (ρ = - 0.815) were the 4 features most correlated with SUVmax in CC. On the other hand, in LM, the 4 features most correlated with SUVmax were GLRLM_LRHGE (ρ = 0.859), GLRLM_LRE (ρ = 0.859), GLRLM_LRLGE (ρ = 0.857), and GLRLM_RP (ρ = - 0.820). CONCLUSION We investigated the relationship between SUVmax of preoperative primary CC lesions and their LM with CT radiomic features. We found some CT radiomic features having relationships with the metabolic characteristics of lesions. This work suggests that non-invasive predictive imaging biomarkers for precision medicine can be derived from CT radiomic.
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Affiliation(s)
- Badr Ahmed
- Department of Radiology Technology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Peyman Sheikhzadeh
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran.
| | - Vahid Changizi
- Department of Radiology Technology and Radiotherapy, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Mehrshad Abbasi
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
| | - Yunus Soleymani
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Wisam Sarhan
- Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
- Department of Nuclear Medicine International Hospital for Cancer and Nuclear Medicine, University of Kufa, Najaf, Iraq
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada
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Chen J, Wang X, Lv H, Zhang W, Tian Y, Song L, Wang Z. Development and external validation of a clinical-radiomics nomogram for preoperative prediction of LVSI status in patients with endometrial carcinoma. J Cancer Res Clin Oncol 2023; 149:13943-13953. [PMID: 37542548 DOI: 10.1007/s00432-023-05044-y] [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: 05/11/2023] [Accepted: 06/28/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE To develop and validate a model that incorporates radiomics based on MRI scans and clinical characteristics to predict lymphovascular invasion (LVSI) in endometrial cancer (EC) patients. METHODS There were 332 patients with EC enrolled retrospectively in this multicenter study. Radiomics score (Radscore) were computed using the valuable radiomics features. The independent predictors of LVSI were identified by univariate logistic analysis. Multivariate logistic regression was used to develop a clinical-radiomics predictive model. Based on the model, a nomogram was developed and validated internally and externally. The nomogram was evaluated with discrimination, calibration, decision curve analysis (DCA), and clinical impact curves (CIC). RESULTS Three predictive models were constructed based on clinicopathological features, radiomic factors and a combination of them, and that the clinic-radiomic model performed best among the three models. Four independent factors comprised the clinical-radiomics model: dynamic contrast enhancement rate of late arterial phase (DCE2), deep myometrium invasion (DMI), lymph node metastasis (LNM), and Radscore. Clinical-radiomics model performance was 0.901 (95% CI 0.84-0.96) in the training cohort, 0.80 (95% CI 0.68-0.92) in the internal validation cohort, and 0.81 (95% CI 0.73-0.9) in the external validation cohort for identifying patients with LVSI, respectively. The model is used to develop a nomogram for clinical use. CONCLUSIONS The MRI-based radiomics nomogram could serve as a noninvasive tool to predict LVSI in EC patients.
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Affiliation(s)
- Jingya Chen
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | | | - Haoyi Lv
- University of Science and Technology of China, Hefei, Anhui, China
| | - Wei Zhang
- Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing, Jiangsu Province, China
| | - Ying Tian
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | - Lina Song
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China
| | - Zhongqiu Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210009, Jiangsu, China.
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Aerts J, Hendrickx S, Berquin C, Lumen N, Verbeke S, Villeirs G, Van Praet C, De Visschere P. Clinical Application of the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation Score for Reporting Magnetic Resonance Imaging in Men on Active Surveillance for Prostate Cancer. EUR UROL SUPPL 2023; 56:39-46. [PMID: 37822515 PMCID: PMC10562144 DOI: 10.1016/j.euros.2023.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/16/2023] [Indexed: 10/13/2023] Open
Abstract
Background The Prostate Cancer Radiological Estimation of Change in Sequential Evaluation (PRECISE) score has been developed to standardise prostate magnetic resonance imaging (MRI) reporting in men on active surveillance (AS) for prostate cancer (PCa). Objective To evaluate the feasibility of PRECISE scoring and assess its diagnostic accuracy. Design setting and participants All PCa patients on AS with a baseline MRI and at least one follow-up MRI scan between January 2008 and September 2022 at a single tertiary referral centre were included in a database. The follow-up protocol of the Prostate Cancer International Active Surveillance (PRIAS) study was used. All scans were retrospectively re-reported by a dedicated uroradiologist and appointed a Prostate Imaging Reporting and Data System (version 2.1) and PRECISE score. Outcome measurements and statistical analysis Clinically significant progression was defined by histopathological upgrading (on biopsy or radical prostatectomy) to grade group ≥3 and/or evolution to T3 stage. A survival analysis was performed to assess differential progression-free survival (PFS) according to the PRECISE score. Results and limitations A total of 188 patients were included for an analysis with a total of 358 repeat MRI scans and 144 repeat biopsies. The median follow-up was 46 mo (interquartile range 21-74). Radiological progression (PRECISE 4-5) had sensitivity, specificity, negative predictive value, and positive predictive value of, respectively, 78%, 70%, 90%, and 49% for clinically significant progression. Four-year PFS was 91% for PRECISE 1-3 versus 66% for PRECISE 4-5 (p < 0.001). In total, 137 patients underwent a confirmation MRI scan within 18 mo after diagnosis. Four-year PFS in this group was 81% for PRECISE 1-3 versus 43% for PRECISE 4-5 (p < 0.001). Limitations include retrospective design and no strict adherence to AS protocol. Conclusions Implementation of PRECISE scoring for PCa patients on AS is feasible and offers a prognostic value. Patients with PRECISE score 4-5 on confirmation MRI within 18 mo after diagnosis have a three-fold higher risk of clinically significant progression after 4 yr. Patient summary Patients with low-risk prostate cancer can be followed up carefully. In this study, we evaluate the standardised reporting of repeat magnetic resonance imaging scans (using the Prostate Cancer Radiological Estimation of Change in Sequential Evaluation [PRECISE] recommendations). PRECISE scoring is feasible and helps identify patients in need of further treatment.
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Affiliation(s)
- Jan Aerts
- Department of Urology, Ghent University Hospital, Ghent, Belgium
| | - Sigi Hendrickx
- Department of Radiology and Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
| | - Camille Berquin
- Department of Urology, Ghent University Hospital, Ghent, Belgium
| | - Nicolaas Lumen
- Department of Urology, Ghent University Hospital, Ghent, Belgium
| | - Sofie Verbeke
- Department of Pathology, Ghent University Hospital, Ghent, Belgium
| | - Geert Villeirs
- Department of Radiology and Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
| | | | - Pieter De Visschere
- Department of Radiology and Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
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Ali H, Mohsen F, Shah Z. Improving diagnosis and prognosis of lung cancer using vision transformers: a scoping review. BMC Med Imaging 2023; 23:129. [PMID: 37715137 PMCID: PMC10503208 DOI: 10.1186/s12880-023-01098-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/05/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. OBJECTIVE This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. METHODS In this review, we searched Pubmed, Scopus, IEEEXplore, and Google Scholar online databases. The search terms included intervention terms (vision transformers) and the task (i.e., lung cancer, adenocarcinoma, etc.). Two reviewers independently screened the title and abstract to select relevant studies and performed the data extraction. A third reviewer was consulted to validate the inclusion and exclusion. Finally, the narrative approach was used to synthesize the data. RESULTS Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. Researchers have used the publicly available lung cancer datasets of the lung imaging database consortium and the cancer genome atlas. One study used a cluster of 48 GPUs, while other studies used one, two, or four GPUs. CONCLUSION It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/ .
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
| | - Farida Mohsen
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar.
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Ni L, Lin WK, Kasputis A, Postiff D, Siddiqui J, Allaway MJ, Davenport MS, Wei JT, Guo JL, Morgan TM, Udager AM, Wang X, Xu G. Assessment of prostate cancer progression using a translational needle photoacoustic sensing probe: Preliminary study with intact human prostates ex-vivo. PHOTOACOUSTICS 2022; 28:100418. [PMID: 36386297 PMCID: PMC9650056 DOI: 10.1016/j.pacs.2022.100418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 10/17/2022] [Accepted: 10/23/2022] [Indexed: 06/14/2023]
Abstract
In our previous studies, we demonstrated the ability of an interstitial all-optical needle photoacoustic (PA) sensing probe and PA spectral analysis (PASA) to assess the aggressiveness of prostate cancer. In this clinical translation investigation, we integrated the optical components of the needle PA sensing probe into a 18G steel needle. The translational needle PA sensing probe was evaluated using intact human prostates in a simulated ultrasound-guided transperineal prostate biopsy. PA signals were acquired at 1220 nm, 1370 nm, 800 nm and 266 nm at each interstitial measurement location and quantified by PASA within the frequency range of 8-28 MHz. The measurement locations were stained for establishing spatial correlations between the quantitative measurements and the histological diagnosing. Most of the quantitative PA assessments reveal statistically significant differences between the benign and cancerous regions. Multivariate analysis combining the PASA quantifications shows an accuracy close to 90% in differentiating the benign and cancerous regions in the prostates.
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Affiliation(s)
- Linyu Ni
- Department of Biomedical Engineering, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Wei-kuan Lin
- Department of Electrical Engineering and Computer Sciences, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Amy Kasputis
- Department of Urology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Deborah Postiff
- Department of Pathology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Javed Siddiqui
- Department of Pathology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | | | - Matthew S. Davenport
- Department of Urology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
- Department of Radiology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - John T. Wei
- Department of Urology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Jay L. Guo
- Department of Electrical Engineering and Computer Sciences, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Todd M. Morgan
- Department of Urology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Aaron M. Udager
- Michigan Center for Translational Pathology, Rogel Cancer Center, Department of Pathology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Xueding Wang
- Department of Biomedical Engineering, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
- Department of Radiology, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
| | - Guan Xu
- Department of Biomedical Engineering, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
- Department of Ophthalmology and Visual Sciences, University of Michigan, 500 S. State St., Ann Arbor, 48109, MI, USA
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