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Thenault R, Gasmi A, Khene ZE, Bensalah K, Mathieu R. Radiogenomics in prostate cancer evaluation. Curr Opin Urol 2021; 31:424-429. [PMID: 34009176 DOI: 10.1097/mou.0000000000000902] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
PURPOSE OF REVIEW Radiogenomics, fusion between radiomics and genomics, represents a new field of research to improve cancer comprehension and evaluation. In this review, we give an overview of radiogenomics and its most recent and relevant applications in prostate cancer management. RECENT FINDINGS Literature about radiogenomics in prostate cancer emerged last 5 years but remains scarce. Radiogenomics in prostate cancer mainly rely on MRI-based features. Several imaging biomarkers, mostly based on the identification of radiomic features from deep learning studies, have been studied for the prediction of genomic profiles, such as PTEN Decipher Oncotype DX or Prolaris expression. However, despite promising results, several limitations still preclude any integration of radiogenomics in daily practice. SUMMARY In the future, the emergence of artificial intelligence in urology, with an increasing use of radiomics and genomics data, may enable radiogenomics to assume a growing role in the evaluation of prostate cancer, with a noninvasive and personal approach in the field of personalized medicine. Further efforts are necessary for integration of this promising approach in prostate cancer decision-making.
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Affiliation(s)
- Ronan Thenault
- Department of Urology, Service d'urologie, Rennes University Hospital, Hôpital Pontchaillou
| | - Anis Gasmi
- Department of Urology, Service d'urologie, Rennes University Hospital, Hôpital Pontchaillou
| | - Zine-Edine Khene
- Department of Urology, Service d'urologie, Rennes University Hospital, Hôpital Pontchaillou
| | - Karim Bensalah
- Department of Urology, Service d'urologie, Rennes University Hospital, Hôpital Pontchaillou
| | - Romain Mathieu
- Department of Urology, Service d'urologie, Rennes University Hospital, Hôpital Pontchaillou
- IRSET, Rennes, France
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152
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Value of Quantitative CTTA in Differentiating Malignant From Benign Bosniak III Renal Lesions on CT Images. J Comput Assist Tomogr 2021; 45:528-536. [PMID: 34176873 DOI: 10.1097/rct.0000000000001181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
OBJECTIVE The aim of this study was to investigate whether computed tomography texture analysis can differentiate malignant from benign Bosniak III renal lesions on computed tomography (CT) images. METHODS This retrospective case-control study included 45 patients/lesions (22 benign and 23 malignant lesions) with Bosniak III renal lesions who underwent CT examination. Axial image slices in the unenhanced phase, corticomedullary phase, and nephrographic phase were selected and delineated manually. Computed tomography texture analysis was performed on each lesion during these 3 phases. Histogram-based, gray-level co-occurrence matrix, and gray-level run-length matrix features were extracted using open-source software and analyzed. In addition, receiver operating characteristic curve was constructed, and the area under the receiver operating characteristic curve (AUC) of each feature was constructed. RESULTS Of the 33 extracted features, 16 features showed significant differences (P < 0.05). Eight features were significantly different between the 2 groups after Holm-Bonferroni correction, including 3 histogram-based, 4 gray-level co-occurrence matrix, and 1 gray-level run-length matrix features (P < 0.01). The texture features resulted in the highest AUC of 0.769 ± 0.074. Renal cell carcinomas were labeled with a higher degree of lesion gray-level disorder and lower lesion homogeneity, and a model incorporating the 3 most discriminative features resulted in an AUC of 0.846 ± 0.058. CONCLUSIONS The results of this study showed that CT texture features were related to malignancy in Bosniak III renal lesions. Computed tomography texture analysis might help in differentiating malignant from benign Bosniak III renal lesions on CT images.
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153
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Fiz F, Costa G, Gennaro N, la Bella L, Boichuk A, Sollini M, Politi LS, Balzarini L, Torzilli G, Chiti A, Viganò L. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment. Diagnostics (Basel) 2021; 11:diagnostics11071162. [PMID: 34202253 PMCID: PMC8305553 DOI: 10.3390/diagnostics11071162] [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: 04/28/2021] [Revised: 06/11/2021] [Accepted: 06/22/2021] [Indexed: 12/29/2022] Open
Abstract
The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.
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Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
| | - Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
| | - Nicolò Gennaro
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Ludovico la Bella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Alexandra Boichuk
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Martina Sollini
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Letterio S. Politi
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Balzarini
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Arturo Chiti
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Viganò
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
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154
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Veres G, Vas NF, Lyngby Lassen M, Béresová M, K. Krizsan A, Forgács A, Berényi E, Balkay L. Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups. PLoS One 2021; 16:e0253419. [PMID: 34143830 PMCID: PMC8213143 DOI: 10.1371/journal.pone.0253419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Accepted: 06/04/2021] [Indexed: 12/12/2022] Open
Abstract
Purpose Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. Methods We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. Results In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Conclusion Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.
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Affiliation(s)
- Gergő Veres
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
- * E-mail:
| | - Norman Félix Vas
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Martin Lyngby Lassen
- Cedars-Sinai Medical Center, AIM Group, Los Angeles, CA, United States of America
- Department of Clinical Physiology, Nuclear Medicine and PET and Cluster for Molecular Imaging, University of Copenhagen, Copenhagen, Denmark
| | - Monika Béresová
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | | | | | - Ervin Berényi
- Division of Radiology and Imaging Science, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - László Balkay
- Division of Nuclear Medicine and Translational Imaging, Department of Medical Imaging, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
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155
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Xu H, Lv W, Zhang H, Ma J, Zhao P, Lu L. Evaluation and optimization of radiomics features stability to respiratory motion in 18 F-FDG 3D PET imaging. Med Phys 2021; 48:5165-5178. [PMID: 34085282 DOI: 10.1002/mp.15022] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 04/18/2021] [Accepted: 05/25/2021] [Indexed: 12/31/2022] Open
Abstract
PURPOSE To evaluate the impact of respiratory motion on radiomics features in 18 F-fluoro-2-deoxy-D-glucose three dimensional positron emission tomography (18 F-FDG 3D PET) imaging and optimize feature stability by combining preprocessing configurations and aggregation strategies. METHODS An in-house developed respiratory motion phantom was imaged in 3D PET scanner under nine respiratory patterns including one reference pattern. In total, 487 radiomics features were extracted for each respiratory pattern. Feature stability to respiratory motion was first evaluated by metrics of coefficient of variation (COV) and relative difference (RD) in a fixed preprocessing configuration. Further, one-way ANOVA and trend analysis were performed to evaluate the impact of preprocessing configuration (voxel size, discretization scheme) and aggregation strategy on feature stability. Finally, an optimization framework was proposed by selected feature-specific configurations with minimum COV value, and the diagnostic performance was validated in stable versus unstable features and fixed versus optimal features by a set of 46 patients with lung disease. RESULTS PET radiomics features were sensitive to respiratory motion, only 79/487 (16%) features were identified to be very stable in the fixed configuration. Preprocessing configuration and aggregation strategy had an impact on feature stability. For different voxel size, bin number, bin size and aggregation strategy, 188/487 (39%), 70/487 (15%), 148/487 (30%), and 38/95 (29%) features appeared significant changes in feature stability. The optimized configuration had the potential to improve feature stability compared to fixed configuration, with the COV decreased from 22% ±24% to 16% ±20%. Regarding the diagnostic performance, the stable and optimal configuration features outperformed the unstable and fixed configuration features, respectively (AUC 0.88, 0.87 vs. 0.83, 0.85). CONCLUSIONS Respiratory motion shows considerable impact on feature stability in 3D PET imaging, while optimizing preprocessing configuration may improve feature stability and diagnostic performance.
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Affiliation(s)
- Hui Xu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Wenbing Lv
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Hongyan Zhang
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Jianhua Ma
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Peng Zhao
- National Innovation Center for Advanced Medical Devices, Shenzheng, China
| | - Lijun Lu
- School of Biomedical Engineering and Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.,Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
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156
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A Novel Nodule Edge Sharpness Radiomic Biomarker Improves Performance of Lung-RADS for Distinguishing Adenocarcinomas from Granulomas on Non-Contrast CT Scans. Cancers (Basel) 2021; 13:cancers13112781. [PMID: 34205005 PMCID: PMC8199879 DOI: 10.3390/cancers13112781] [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] [Received: 05/18/2021] [Accepted: 05/31/2021] [Indexed: 11/18/2022] Open
Abstract
Simple Summary The great majority of pulmonary nodules on screening CT scans are benign (95%). Due to inaccurate diagnoses of granulomas from adenocarcinomas on CT scans, many patients with benign nodules are subjected to unnecessary surgical procedures. The aim of this retrospective study is to evaluate the discriminability of a new radiomic feature, nodule edge/interface sharpness (NIS), for distinguishing lung adenocarcinomas from benign granulomas on non-contrast CT scans. Moreover, we aim to evaluate whether NIS can improve the performance of Lung-RADS, by reclassifying benign nodules that were initially assessed as suspicious. In a cohort of 352 patients with diagnostic non-contrast CT scans, NIS radiomics was able to classify nodules with an area under the receiver operating characteristic curve (ROC AUC) of 0.77, and when combined with intra-tumoral textural and shape features, classification performance increased to AUC of 0.84. Additionally, the NIS classifier correctly reclassified 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS. Combining NIS with Lung-RADS has the potential to alter patient management by significantly decreasing unnecessary biopsies/follow up imaging. Abstract The aim of this study is to evaluate whether NIS radiomics can distinguish lung adenocarcinomas from granulomas on non-contrast CT scans, and also to improve the performance of Lung-RADS by reclassifying benign nodules that were initially assessed as suspicious. The screening or standard diagnostic non-contrast CT scans of 362 patients was divided into training (St, N = 145), validation (Sv, N = 145), and independent validation (Siv, N = 62) sets from different institutions. Nodules were identified and manually segmented on CT images by a radiologist. A series of 264 features relating to the edge sharpness transition from the inside to the outside of the nodule were extracted. The top 10 features were used to train a linear discriminant analysis (LDA) machine learning classifier on St. In conjunction with the LDA classifier, NIS radiomics classified nodules with an AUC of 0.82 ± 0.04, 0.77, and 0.71 respectively on St, Sv, and Siv. We evaluated the ability of the NIS classifier to determine the proportion of the patients in Sv that were identified initially as suspicious by Lung-RADS but were reclassified as benign by applying the NIS scores. The NIS classifier was able to correctly reclassify 46% of those lesions that were actually benign but deemed suspicious by Lung-RADS alone on Sv.
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157
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Ye K, Chen M, Zhu Q, Lu Y, Yuan H. Effect of adaptive statistical iterative reconstruction-V (ASiR-V) levels on ultra-low-dose CT radiomics quantification in pulmonary nodules. Quant Imaging Med Surg 2021; 11:2344-2353. [PMID: 34079706 DOI: 10.21037/qims-20-932] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Background The weightings of iterative reconstruction algorithm can affect CT radiomic quantification. But, the effect of ASiR-V levels on the reproducibility of CT radiomic features between ultra-low-dose computed tomography (ULDCT) and low-dose computed tomography (LDCT) is still unknown. The purpose of study is to investigate whether adaptive statistical iterative reconstruction-V (ASiR-V) levels affect radiomic feature quantification using ULDCT and to assess the reproducibility of radiomic features between ULDCT and LDCT. Methods Sixty-three patients with pulmonary nodules underwent LDCT (0.70±0.16 mSv) and ULDCT (0.15±0.02 mSv). LDCT was reconstructed with ASiR-V 50%, and ULDCT with ASiR-V 50%, 70%, and 90%. Radiomics analysis was applied, and 107 features were extracted. The concordance correlation coefficient (CCC) was calculated to describe agreement among ULDCTs and between ULDCT and LDCT for each feature. The proportion of features with CCC >0.9 among ULDCTs and between ULDCT and LDCT, and the mean CCC for all features between ULDCT and LDCT were also compared. Results Sixty-three solid nodules (SNs) and 48 pure ground-glass nodules (pGGNs) were analyzed. There was no difference for the proportion of features in SNs among ULDCTs and between ULDCT and LDCT (P>0.05). The proportion of features in pGGNs were highest for ULDCT70% vs. 90% (78.5%) and ULDCT90% vs. LDCT50% (50.5%). In SNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.67±0.26, not different with that for ULDCT50% vs. LDCT50% (0.68±0.24) and ULDCT70% vs. LDCT50% (0.64±0.21) (P>0.05). In pGGNs, the mean CCC for ULDCT90% vs. LDCT50% was 0.79±0.19, higher than that for ULDCT50% vs. LDCT50% (0.61±0.28) and ULDCT70% vs. LDCT50% (0.76±0.24) (P<0.05). Conclusions ASiR-V levels significantly affected ULDCT radiomic feature quantification in pulmonary nodules, with stronger effects in pGGNs than in SNs. The reproducibility of radiomic features was highest between ULDCT90% and LDCT50%.
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Affiliation(s)
- Kai Ye
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Min Chen
- Department of Radiology, Ghent University Hospital, Corneel Heymanslaan 10,9000, Ghent, Belgium
| | - Qiao Zhu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Yuliu Lu
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
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158
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van Dijk LV, Fuller CD. Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges. Am Soc Clin Oncol Educ Book 2021; 41:1-11. [PMID: 33929877 DOI: 10.1200/edbk_320951] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. This article aims to convey the basic conceptual premises of radiomics and artificial intelligence using head and neck cancer as a use case. This educational overview focuses on approaches for head and neck oncology imaging, detailing current research efforts and challenges to implementation.
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Affiliation(s)
- Lisanne V van Dijk
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX.,Department of Radiation Oncology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Clifton D Fuller
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX
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159
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Gao Y, Song F, Zhang P, Liu J, Cui J, Ma Y, Zhang G, Luo J. Improving the Subtype Classification of Non-small Cell Lung Cancer by Elastic Deformation Based Machine Learning. J Digit Imaging 2021; 34:605-617. [PMID: 33963422 PMCID: PMC8329138 DOI: 10.1007/s10278-021-00455-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2019] [Revised: 01/06/2021] [Accepted: 04/27/2021] [Indexed: 01/02/2023] Open
Abstract
Non-invasive image-based machine learning models have been used to classify subtypes of non-small cell lung cancer (NSCLC). However, the classification performance is limited by the dataset size, because insufficient data cannot fully represent the characteristics of the tumor lesions. In this work, a data augmentation method named elastic deformation is proposed to artificially enlarge the image dataset of NSCLC patients with two subtypes (squamous cell carcinoma and large cell carcinoma) of 3158 images. Elastic deformation effectively expanded the dataset by generating new images, in which tumor lesions go through elastic shape transformation. To evaluate the proposed method, two classification models were trained on the original and augmented dataset, respectively. Using augmented dataset for training significantly increased classification metrics including area under the curve (AUC) values of receiver operating characteristics (ROC) curves, accuracy, sensitivity, specificity, and f1-score, thus improved the NSCLC subtype classification performance. These results suggest that elastic deformation could be an effective data augmentation method for NSCLC tumor lesion images, and building classification models with the help of elastic deformation has the potential to serve for clinical lung cancer diagnosis and treatment design.
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Affiliation(s)
- Yang Gao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Fan Song
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Peng Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Jian Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Jingjing Cui
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China
| | - Yingying Ma
- Medical Engineering Management Office, Shandong Provincial Hospital Affiliated To Shandong University, Jinan, 250021, China
| | - Guanglei Zhang
- Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China.
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100083, China.
| | - Jianwen Luo
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
- Center for Biomedical Imaging Research, Tsinghua University, Beijing, China.
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160
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Hong D, Zhang L, Xu K, Wan X, Guo Y. Prognostic Value of Pre-Treatment CT Radiomics and Clinical Factors for the Overall Survival of Advanced (IIIB-IV) Lung Adenocarcinoma Patients. Front Oncol 2021; 11:628982. [PMID: 34123786 PMCID: PMC8193844 DOI: 10.3389/fonc.2021.628982] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 05/04/2021] [Indexed: 11/27/2022] Open
Abstract
Purpose The purpose of this study was to investigate the prognostic value of pre-treatment CT radiomics and clinical factors for the overall survival (OS) of advanced (IIIB–IV) lung adenocarcinoma patients. Methods This study involved 165 patients with advanced lung adenocarcinoma. The Lasso–Cox regression model was used for feature selection and radiomics signature building. Then a clinical model was built based on clinical factors; a combined model in the form of nomogram was constructed with both clinical factors and the radiomics signature. Harrell’s concordance index (C-Index) and Receiver operating characteristic (ROC) curves at cut-off time points of 1-, 2-, and 3- year were used to estimate and compare the predictive ability of all three models. Finally, the discriminatory ability and calibration of the nomogram were analyzed. Results Thirteen significant features were selected to build the radiomics signature whose C-indexes were 0.746 (95% CI, 0.699 to 0.792) in the training cohort and 0.677 (95% CI, 0.597 to 0.766) in the validation cohort. The C-indexes of combined model achieved 0.799 (95% CI, 0.757 to 0.84) in the training cohort and 0.733 (95% CI, 0.656 to 0.81) in the validation cohort, which outperformed the clinical model and radiomics signature. Moreover, the areas under the curve (AUCs) of the radiomic signature for 2-year prediction was superior to that of the clinical model. The combined model had the best AUCs for 2- and 3-year predictions. Conclusions Radiomic signatures and clinical factors have prognostic value for OS in advanced (IIIB–IV) lung adenocarcinoma patients. The optimal model should be selected according to different cut-off time points in clinical application.
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Affiliation(s)
- Duo Hong
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Lina Zhang
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Ke Xu
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Xiaoting Wan
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China
| | - Yan Guo
- GE Healthcare, Beijing, China
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Novelties in Imaging of Thoracic Sarcoidosis. J Clin Med 2021; 10:jcm10112222. [PMID: 34063811 PMCID: PMC8196662 DOI: 10.3390/jcm10112222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 05/14/2021] [Accepted: 05/19/2021] [Indexed: 01/14/2023] Open
Abstract
Sarcoidosis is a systemic granulomatous disease affecting various organs, and the lungs are the most commonly involved. According to guidelines, diagnosis relies on a consistent clinical picture, histological demonstration of non-caseating granulomas, and exclusion of other diseases with similar histological or clinical picture. Nevertheless, chest imaging plays an important role in both diagnostic assessment, allowing to avoid biopsy in some situations, and prognostic evaluation. Despite the demonstrated lower sensitivity of chest X-ray (CXR) in the evaluation of chest findings compared to high-resolution computed tomography (HRCT), CXR still retains a pivotal role in both diagnostic and prognostic assessment in sarcoidosis. Moreover, despite the huge progress made in the field of radiation dose reduction, chest magnetic resonance (MR), and quantitative imaging, very little research has focused on their application in sarcoidosis. In this review, we aim to describe the latest novelties in diagnostic and prognostic assessment of thoracic sarcoidosis and to identify the fields of research that require investigation.
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162
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M2 macrophage-derived exosomal long non-coding RNA AGAP2-AS1 enhances radiotherapy immunity in lung cancer by reducing microRNA-296 and elevating NOTCH2. Cell Death Dis 2021; 12:467. [PMID: 33972506 PMCID: PMC8110970 DOI: 10.1038/s41419-021-03700-0] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 01/09/2023]
Abstract
Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) play vital roles in human diseases. We aimed to identify the effect of the lncRNA AGAP2 antisense RNA 1 (AGAP2-AS1)/miR-296/notch homolog protein 2 (NOTCH2) axis on the progression and radioresistance of lung cancer. Expression of AGAP2-AS1, miR-296, and NOTCH2 in lung cancer cells and tissues from radiosensitive and radioresistant patients was determined, and the predictive role of AGAP2-AS1 in the prognosis of patients was identified. THP-1 cells were induced and exosomes were extracted, and the lung cancer cells were respectively treated with silenced AGAP2-AS1, exosomes, and exosomes upregulating AGAP2-AS1 or downregulating miR-296. The cells were radiated under different doses, and the biological processes of cells were assessed. Moreover, the natural killing cell-mediated cytotoxicity on lung cancer cells was determined. The relationships between AGAP2-AS1 and miR-296, and between miR-296 and NOTCH2 were verified. AGAP2-AS1 and NOTCH2 increased while miR-296 decreased in radioresistant patients and lung cancer cells. The malignant behaviors of radioresistant cells were promoted compared with the parent cells. Inhibited AGAP2-AS1, macrophage-derived exosomes, and exosomes overexpressing AGAP2-AS1 or inhibiting miR-296 facilitated the malignant phenotypes of radioresistant lung cancer cells. Furthermore, AGAP2-AS1 negatively regulated miR-296, and NOTCH2 was targeted by miR-296. M2 macrophage-derived exosomal AGAP2-AS1 enhances radiotherapy immunity in lung cancer by reducing miR-296 and elevating NOTCH2. This study may be helpful for the investigation of radiotherapy of lung cancer.
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Cucchiara F, Petrini I, Romei C, Crucitta S, Lucchesi M, Valleggi S, Scavone C, Capuano A, De Liperi A, Chella A, Danesi R, Del Re M. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res 2021; 169:105643. [PMID: 33940185 DOI: 10.1016/j.phrs.2021.105643] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/11/2022]
Abstract
Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient's follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.
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Affiliation(s)
- Federico Cucchiara
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Iacopo Petrini
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Chiara Romei
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Stefania Crucitta
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
| | - Maurizio Lucchesi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Simona Valleggi
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Cristina Scavone
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa Capuano
- Department of Experimental Medicine, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Annalisa De Liperi
- Unit II of Radio-diagnostics, Department of Diagnostic and Imaging, University Hospital of Pisa, Pisa, Italy
| | - Antonio Chella
- Unit of Pneumology, Department of Translational Research and New Technologies in Medicine, University Hospital of Pisa, Pisa, Italy
| | - Romano Danesi
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy.
| | - Marzia Del Re
- Unit of Clinical Pharmacology and Pharmacogenetics, Department of Clinical and Experimental Medicine, University Hospital of Pisa, Pisa, Italy
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164
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Li N, Wang L, Hu Y, Han W, Zheng F, Song W, Jiang J. Global evolution of research on pulmonary nodules: a bibliometric analysis. Future Oncol 2021; 17:2631-2645. [PMID: 33880950 DOI: 10.2217/fon-2020-0987] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Aim: To provide a historical and global picture of research concerning lung nodules, compare the contributions of major countries and explore research trends over the past 10 years. Methods: A bibliometric analysis of publications from Scopus (1970-2020) and Web of Science (2011-2020). Results: Publications about pulmonary nodules showed an enormous growth trend from 1970 to 2020. There is a high level of collaboration among the 20 most productive countries and regions, with the USA located at the center of the collaboration network. The keywords 'deep learning', 'artificial intelligence' and 'machine learning' are current hotspots. Conclusions: Abundant research has focused on pulmonary nodules. Deep learning is emerging as a promising tool for lung cancer diagnosis and management.
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Affiliation(s)
- Ning Li
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Lei Wang
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yaoda Hu
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Wei Han
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Fuling Zheng
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Wei Song
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jingmei Jiang
- Department of Epidemiology & Biostatistics, Institute of Basic Medicine Sciences, Chinese Academy of Medical Sciences/School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
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165
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Tabatabaei M, Razaei A, Sarrami AH, Saadatpour Z, Singhal A, Sotoudeh H. Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review. Oncology 2021; 99:433-443. [PMID: 33849021 DOI: 10.1159/000515597] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 02/28/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges. MATERIAL AND METHODS Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers. RESULTS By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score). CONCLUSION The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.
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Affiliation(s)
- Mohsen Tabatabaei
- Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran
| | - Ali Razaei
- Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA.,Division of Neuroradiology, Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA
| | | | - Zahra Saadatpour
- Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA.,Division of Neuroradiology, Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA
| | - Aparna Singhal
- Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA.,Division of Neuroradiology, Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA
| | - Houman Sotoudeh
- Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA.,Division of Neuroradiology, Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA
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166
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Liu Y, Zhang FJ, Zhao XX, Yang Y, Liang CY, Feng LL, Wan XB, Ding Y, Zhang YW. Development of a Joint Prediction Model Based on Both the Radiomics and Clinical Factors for Predicting the Tumor Response to Neoadjuvant Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer. Cancer Manag Res 2021; 13:3235-3246. [PMID: 33880066 PMCID: PMC8053518 DOI: 10.2147/cmar.s295317] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 03/18/2021] [Indexed: 12/22/2022] Open
Abstract
Purpose Neoadjuvant chemoradiotherapy (nCRT) has become the standard treatment for locally advanced rectal cancer (LARC). However, the accuracy of traditional clinical indicators in predicting tumor response is poor. Recently, radiomics based on magnetic resonance imaging (MRI) has been regarded as a promising noninvasive assessment method. The present study was conducted to develop a model to predict the pathological response by analyzing the quantitative features of MRI and clinical risk factors, which might predict the therapeutic effects in patients with LARC as accurately as possible before treatment. Patients and Methods A total of 82 patients with LARC were enrolled as the training cohort and internal validation cohort. The pre-CRT MRI after pretreatment was acquired to extract texture features, which was finally selected through the minimum redundancy maximum relevance (mRMR) algorithm. A support vector machine (SVM) was used as a classifier to classify different tumor responses. A joint radiomics model combined with clinical risk factors was then developed and evaluated by receiver operating characteristic (ROC) curves. External validation was performed with 107 patients from another center to evaluate the applicability of the model. Results Twenty top image texture features were extracted from 6192 extracted-radiomic features. The radiomics model based on high-spatial-resolution T2-weighted imaging (HR-T2WI) and contrast-enhanced T1-weighted imaging (T1+C) demonstrated an area under the curve (AUC) of 0.8910 (0.8114–0.9706) and 0.8938 (0.8084–0.9792), respectively. The AUC value rose to 0.9371 (0.8751–0.9997) and 0.9113 (0.8449–0.9776), respectively, when the circumferential resection margin (CRM) and carbohydrate antigen 19-9 (CA19-9) levels were incorporated. Clinical usefulness was confirmed in an external validation cohort as well (AUC, 0.6413 and 0.6818). Conclusion Our study indicated that the joint radiomics prediction model combined with clinical risk factors showed good predictive ability regarding the treatment response of tumors as accurately as possible before treatment.
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Affiliation(s)
- Yang Liu
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Feng-Jiao Zhang
- Shanghai Concord Medical Cancer Center, Shanghai, 200001, People's Republic of China
| | - Xi-Xi Zhao
- Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yuan Yang
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Chun-Yi Liang
- Medical Imaging Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Li-Li Feng
- Department of Radiation Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
| | - Xiang-Bo Wan
- Department of Radiation Oncology, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, 510655, People's Republic of China
| | - Yi Ding
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
| | - Yao-Wei Zhang
- Department of Radiation Oncology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, People's Republic of China
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167
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Zhang T, Xu Z, Liu G, Jiang B, de Bock GH, Groen HJM, Vliegenthart R, Xie X. Simultaneous Identification of EGFR,KRAS,ERBB2, and TP53 Mutations in Patients with Non-Small Cell Lung Cancer by Machine Learning-Derived Three-Dimensional Radiomics. Cancers (Basel) 2021; 13:cancers13081814. [PMID: 33920322 PMCID: PMC8070114 DOI: 10.3390/cancers13081814] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/07/2021] [Accepted: 04/08/2021] [Indexed: 12/25/2022] Open
Abstract
Simple Summary Multiple genetic mutations are associated with the outcomes of patients with non-small cell lung cancer (NSCLC) after using tyrosine kinase inhibitor, but the cost for detecting multiple genetic mutations is high. Few studies have investigated whether multiple genetic mutations can be simultaneously detected based on image features in patients with NSCLC. We developed a machine learning-derived radiomics approach that can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations on CT images in patients with NSCLC. These findings suggest that machine learning-derived radiomics may become a noninvasive and low-cost method to screen for multiple genetic mutations in patients with NSCLC before using next-generation sequencing tests, which can help to improve individualized targeted therapies. Abstract Purpose: To develop a machine learning-derived radiomics approach to simultaneously discriminate epidermal growth factor receptor (EGFR), Kirsten rat sarcoma viral oncogene (KRAS), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and tumor protein 53 (TP53) genetic mutations in patients with non-small cell lung cancer (NSCLC). Methods: This study included consecutive patients from April 2018 to June 2020 who had histologically confirmed NSCLC, and underwent pre-surgical contrast-enhanced CT and post-surgical next-generation sequencing (NGS) tests to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations. A dedicated radiomics analysis package extracted 1672 radiomic features in three dimensions. Discriminative models were established using the least absolute shrinkage and selection operator to determine the presence of EGFR, KRAS, ERBB2, and TP53 mutations, based on radiomic features and relevant clinical factors. Results: In 134 patients (63.6 ± 8.9 years), the 20 most relevant radiomic features (13 for KRAS) to mutations were selected to construct models. The areas under the curve (AUCs) of the combined model (radiomic features and relevant clinical factors) for discriminating EGFR, KRAS, ERBB2, and TP53 mutations were 0.78 (95% CI: 0.70–0.86), 0.81 (0.69–0.93), 0.87 (0.78–0.95), and 0.84 (0.78–0.91), respectively. In particular, the specificity to exclude EGFR mutations was 0.96 (0.87–0.99). The sensitivity to determine KRAS, ERBB2, and TP53 mutations ranged from 0.82 (0.69–90) to 0.92 (0.62–0.99). Conclusions: Machine learning-derived 3D radiomics can simultaneously discriminate the presence of EGFR, KRAS, ERBB2, and TP53 mutations in patients with NSCLC. This noninvasive and low-cost approach may be helpful in screening patients before invasive sampling and NGS testing.
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Affiliation(s)
- Tiening Zhang
- Department of Radiation Oncology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China;
| | - Zhihan Xu
- Siemens Healthineers Ltd., Zhouzhu Rd.278, Shanghai 200120, China;
| | - Guixue Liu
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China; (G.L.); (B.J.)
| | - Beibei Jiang
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China; (G.L.); (B.J.)
| | - Geertruida H. de Bock
- Department of Epidemiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands;
| | - Harry J. M. Groen
- Department of Lung Diseases, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands;
| | - Rozemarijn Vliegenthart
- Department of Radiology, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands;
| | - Xueqian Xie
- Department of Radiology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Haining Rd.100, Shanghai 200080, China; (G.L.); (B.J.)
- Correspondence:
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168
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Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 2021; 48:4002-4015. [PMID: 33835220 DOI: 10.1007/s00259-021-05338-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/24/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. METHODS Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. RESULTS Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7-0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. CONCLUSIONS Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.
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Affiliation(s)
- C Bezzi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
| | - P Mapelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - L Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - I Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - P Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - A Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - V Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - S Partelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - L Gianolli
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - M Falconi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - M Picchio
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy. .,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
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169
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Zheng L, Yin J, Wang S, Jiang H, Hu P, Kang Z, Lv P, Li W, Cai C. Associated factors of co-existent pulmonary tuberculosis and lung cancer: A case-control study. Eur J Clin Invest 2021; 51:e13432. [PMID: 33053200 DOI: 10.1111/eci.13432] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/29/2020] [Accepted: 10/10/2020] [Indexed: 12/24/2022]
Abstract
BACKGROUND Although the incidence of co-existent pulmonary tuberculosis (PTB) and lung cancer in China is increasing, risk factors related to its development are still poorly understood. We aimed to investigate which clinical factors were associated with the odds of co-existent PTB and lung cancer (PTB-lung cancer) cases in a case-control study. METHOD A total of 125 PTB-lung cancer patients were enrolled by Beijing Chest Hospital as the case group between January 2012 and December 2016. Age- and sex-matched PTB-only (N = 125) and lung cancer-only (N = 125) patients were selected as the control groups. Data were collected from the medical records and computed tomography (CT) reports. The case group was further categorized into three sub-groups according to the diagnosis intervals between previous PTB and lung cancer (<1 year, 1-10 years, and > 10 years). RESULT Compared with both controls of PTB-only and lung cancer-only patients, the PTB-lung cancer case group had significantly higher proportions of patients with irritant cough, expectoration, hemoptysis, fever and CT features of irregular mass and pleural thickening. For PTB patients, fibrous calcification (OR, 2.193; 95%CI, 1.168-4.117) was associated with higher odds of lung cancer (P-value < .05). CONCLUSION Distinct clinical symptoms and CT tests may help with the early diagnosis of PTB-lung cancer cases. PTB patients with fibrous calcification may have a higher risk of lung cancer. Further multicenter prospective studies are required to validate our findings.
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Affiliation(s)
- Lijun Zheng
- National Tuberculosis Clinical Lab of China, Beijing Chest Hospital, Capital Medical University, Beijing, China.,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Jinfeng Yin
- National Tuberculosis Clinical Lab of China, Beijing Chest Hospital, Capital Medical University, Beijing, China.,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Siyi Wang
- School of Statistics, Ren'min University of China, Beijing, China
| | - Hui Jiang
- National Tuberculosis Clinical Lab of China, Beijing Chest Hospital, Capital Medical University, Beijing, China.,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China
| | - Puqi Hu
- Beijing You'an Hospital, Capital Medical University, Beijing, China
| | - Ziqing Kang
- Beijing You'an Hospital, Capital Medical University, Beijing, China
| | - Pingxin Lv
- Department of Radiology, Beijing Chest Hospital, Capital Medical University, Beijing, China
| | - Weimin Li
- National Tuberculosis Clinical Lab of China, Beijing Chest Hospital, Capital Medical University, Beijing, China.,National Tuberculosis Clinical Lab of China, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing, China.,Beijing Municipal Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Chao Cai
- Beijing You'an Hospital, Capital Medical University, Beijing, China
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170
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Li H, Gao C, Sun Y, Li A, Lei W, Yang Y, Guo T, Sun X, Wang K, Liu M, Cui D. Radiomics Analysis to Enhance Precise Identification of Epidermal Growth Factor Receptor Mutation Based on Positron Emission Tomography Images of Lung Cancer Patients. J Biomed Nanotechnol 2021; 17:691-702. [DOI: 10.1166/jbn.2021.3056] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
Abstract
How to recognize precisely epidermal growth factor receptor (EGFR) mutation in lung cancer patients owns great clinical requirement. In this study, 1575 radiomics features were extracted from PET images of 75 lung cancer patients based on contrast agents such as 18F-MPG and
18F-FDG. The Mann-Whitney U test was used for single factor analysis, the Least Absolute Shrinkage and Selection Operator (Lasso) Regression was used for feature screening, then the radiomics classification models were established by using support vector machines and ten-fold cross-validation,
and were used to identify EGFR mutation in primary lung cancers and metastasis lung cancers, accuracy based on 18F-MPG PET images are respectively 90% for primary lung cancers, and 89.66% for metastasis lung cancers, accuracy based on 18F-FDG PET images are respectively
76% for primary lung cancers and 82.75% for metastasis lung cancers. The area under the curves (AUC) based on 18F-MPG PET images are respectively 0.94877 for primary lung cancers, and 0.91775 for metastasis lung cancers, AUC based on 18F-FDG PET images are respectively
0.87374 for primary lung cancers, and 0.82251 for metastasis lung cancers. In conclusion, both 18F-MPG PET images and 18F-FDG PET images combined with established classification models can identify EGFR mutation, but 18F-MPG PET images have more precisely than
18F-FDG PET images, own clinical translational prospects.
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Affiliation(s)
- Hui Li
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Chao Gao
- TOF-PET/CT/MR Center, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, PR China
| | - Yingying Sun
- TOF-PET/CT/MR Center, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, PR China
| | - Aojie Li
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Wang Lei
- Department of Chest Surgery, Tangdu Hospital, Air Force Medical University, Xi’an 710038, PR China National Engineering Research Center for Nanotechnology 28, Jiangchuan Road, Shanghai 200241, PR China
| | - Yuming Yang
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Ting Guo
- Department of Chest Surgery, Tangdu Hospital, Air Force Medical University, Xi’an 710038, PR China National Engineering Research Center for Nanotechnology 28, Jiangchuan Road, Shanghai 200241, PR China
| | - Xilin Sun
- TOF-PET/CT/MR Center, The Fourth Hospital of Harbin Medical University, Harbin, Heilongjiang 150028, PR China
| | - Kan Wang
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Manhua Liu
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Daxiang Cui
- Institute of Nano Biomedicine and Engineering, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China
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He T, Fong JN, Moore LW, Ezeana CF, Victor D, Divatia M, Vasquez M, Ghobrial RM, Wong STC. An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer. Comput Med Imaging Graph 2021; 89:101894. [PMID: 33725579 PMCID: PMC8054468 DOI: 10.1016/j.compmedimag.2021.101894] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/04/2021] [Accepted: 03/08/2021] [Indexed: 01/27/2023]
Abstract
INTRODUCTION Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. Patients with small HCC (<5 cm) are given priority over others for transplantation due to clinical allocation policies based on tumor size. Attempting to shift from the prevalent paradigm that successful transplantation and longer disease-free survival can only be achieved in patients with small HCC to expanding the transplantation option to patients with HCC of the highest tumor burden (>5 cm), we developed a convergent artificial intelligence (AI) model that combines transient clinical data with quantitative histologic and radiomic features for more objective risk assessment of liver transplantation for HCC patients. METHODS Patients who received a LT for HCC between 2008-2019 were eligible for inclusion in the analysis. All patients with post-LT recurrence were included, and those without recurrence were randomly selected for inclusion in the deep learning model. Pre- and post-transplant magnetic resonance imaging (MRI) scans and reports were compressed using CapsNet networks and natural language processing, respectively, as input for a multiple feature radial basis function network. We applied a histological image analysis algorithm to detect pathologic areas of interest from explant tissue of patients who recurred. The multilayer perceptron was designed as a feed-forward, supervised neural network topology, with the final assessment of recurrence risk. We used area under the curve (AUC) and F-1 score to assess the predictability of different network combinations. RESULTS A total of 109 patients were included (87 in the training group, 22 in the testing group), of which 20 were positive for cancer recurrence. Seven models (AUC; F-1 score) were generated, including clinical features only (0.55; 0.52), magnetic resonance imaging (MRI) only (0.64; 0.61), pathological images only (0.64; 0.61), MRI plus pathology (0.68; 0.65), MRI plus clinical (0.78, 0.75), pathology plus clinical (0.77; 0.73), and a combination of clinical, MRI, and pathology features (0.87; 0.84). The final combined model showed 80 % recall and 89 % precision. The total accuracy of the implemented model was 82 %. CONCLUSION We validated that the deep learning model combining clinical features and multi-scale histopathologic and radiomic image features can be used to discover risk factors for recurrence beyond tumor size and biomarker analysis. Such a predictive, convergent AI model has the potential to alter the LT allocation system for HCC patients and expand the transplantation treatment option to patients with HCC of the highest tumor burden.
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Affiliation(s)
- Tiancheng He
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - Joy Nolte Fong
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Linda W Moore
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States; Center for Outcomes Research, Houston Methodist Research Institute, Houston, TX, 77030, United States
| | - Chika F Ezeana
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - David Victor
- JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX, 77030, United States; Department of Medicine, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Mukul Divatia
- Department of Clinical Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, TX, 77030, United States
| | - Matthew Vasquez
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States
| | - R Mark Ghobrial
- Department of Surgery, Houston Methodist Hospital, Houston, TX, 77030, United States; JC Walter Jr Transplant Center, Houston Methodist Hospital, Houston, TX, 77030, United States.
| | - Stephen T C Wong
- Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center and Departments of Radiology and Pathology, Houston Methodist Hospital, Weill Cornell Medicine, Houston, TX, 77030, United States.
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Ferreira Junior JR, Koenigkam-Santos M, Machado CVB, Faleiros MC, Correia NSC, Cipriano FEG, Fabro AT, de Azevedo-Marques PM. Radiomic analysis of lung cancer for the assessment of patient prognosis and intratumor heterogeneity. Radiol Bras 2021; 54:87-93. [PMID: 33854262 PMCID: PMC8029936 DOI: 10.1590/0100-3984.2019.0135] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Objective To determine whether the radiomic features of lung lesions on computed tomography correlate with overall survival in lung cancer patients. Materials and Methods This was a retrospective study involving 101 consecutive patients with malignant neoplasms confirmed by biopsy or surgery. On computed tomography images, the lesions were submitted to semi-automated segmentation and were characterized on the basis of 2,465 radiomic variables. The prognostic assessment was based on Kaplan-Meier analysis and log-rank tests, according to the median value of the radiomic variables. Results Of the 101 patients evaluated, 28 died (16 dying from lung cancer), and 73 were censored, with a mean overall survival time of 1,819.4 days (95% confidence interval [95% CI]: 1,481.2-2,157.5). One radiomic feature (the mean of the Fourier transform) presented a difference on Kaplan-Meier curves (p < 0.05). A high-risk group of patients was identified on the basis of high values for the mean of the Fourier transform. In that group, the mean survival time was 1,465.4 days (95% CI: 985.2-1,945.6), with a hazard ratio of 2.12 (95% CI: 1.01-4.48). We also identified a low-risk group, in which the mean of the Fourier transform was low (mean survival time of 2,164.8 days; 95% CI: 1,745.4-2,584.1). Conclusion A radiomic signature based on the Fourier transform correlates with overall survival, representing a prognostic biomarker for risk stratification in patients with lung cancer.
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Affiliation(s)
| | - Marcel Koenigkam-Santos
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Camila Vilas Boas Machado
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | - Matheus Calil Faleiros
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
| | | | | | - Alexandre Todorovic Fabro
- Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo (FMRP-USP), Ribeirão Preto, SP, Brazil
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173
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Radiomic Model Predicts Lymph Node Response to Induction Chemotherapy in Locally Advanced Head and Neck Cancer. Diagnostics (Basel) 2021; 11:diagnostics11040588. [PMID: 33806029 PMCID: PMC8064478 DOI: 10.3390/diagnostics11040588] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/22/2021] [Accepted: 03/22/2021] [Indexed: 12/24/2022] Open
Abstract
This study developed a pretreatment CT-based radiomic model of lymph node response to induction chemotherapy in locally advanced head and neck squamous cell carcinoma (HNSCC) patients. This was a single-center retrospective study of patients with locally advanced HPV+ HNSCC. Forty-one enlarged lymph nodes were found from 27 patients on pretreatment CT and were split into 3:1 training and testing cohorts. Ninety-three radiomic features were extracted. A radiomic model and a combined radiomic-clinical model predicting lymph node response to induction chemotherapy were developed using multivariable logistic regression. Median age was 57 years old, and 93% of patients were male. Post-treatment evaluation was 32 days after treatment, with a median reduction in lymph node volume of 66%. A three-feature radiomic model (minimum, skewness, and low gray level run emphasis) and a combined radiomic-clinical model were developed. The combined model performed the best, with AUC = 0.85 on the training cohort and AUC = 0.75 on the testing cohort. A pretreatment CT-based lymph node radiomic signature combined with clinical parameters was able to predict nodal response to induction chemotherapy for patients with locally advanced HNSCC.
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174
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Radiomics MRI for lymph node status prediction in breast cancer patients: the state of art. J Cancer Res Clin Oncol 2021; 147:1587-1597. [PMID: 33758997 DOI: 10.1007/s00432-021-03606-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 03/16/2021] [Indexed: 12/20/2022]
Abstract
OBJECTIVES To create a review of the existing literature on the radiomic approach in predicting the lymph node status of the axilla in breast cancer (BC). MATERIALS AND METHODS Two reviewers conducted the literature search on MEDLINE databases independently. Ten articles on the prediction of sentinel lymph node metastasis in breast cancer with a radiomic approach were selected. The study characteristics and results were reported. The quality of the methodology was evaluated according to the Radiomics Quality Score (RQS). RESULTS All studies were retrospective in design and published between 2017 and 2020. The majority of studies used DCE-MRI sequences and two investigated only pre-contrast images. The sample size was lower than 200 patients for 7 studies. The pre-processing used software, feature extraction and selection methods and classifier development are heterogeneous and a standardization of results is not yet possible. The average RQS score was 11.1 (maximum possible value = 36). The criteria with the lowest scores were the type of study, validation, comparison with a gold standard, potential clinical utility, cost-effective analysis and open science data. CONCLUSION The field of radiomics is a diagnostic approach of relative recent development. The results in predicting axillary lymph node status are encouraging, but there are still weaknesses in the quality of studies that may limit the reproducibility of the results.
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175
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Chang C, Sun X, Wang G, Yu H, Zhao W, Ge Y, Duan S, Qian X, Wang R, Lei B, Wang L, Liu L, Ruan M, Yan H, Liu C, Chen J, Xie W. A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma. Front Oncol 2021; 11:603882. [PMID: 33738250 PMCID: PMC7962599 DOI: 10.3389/fonc.2021.603882] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 01/08/2021] [Indexed: 12/12/2022] Open
Abstract
Objectives Anaplastic lymphoma kinase (ALK) rearrangement status examination has been widely used in clinic for non-small cell lung cancer (NSCLC) patients in order to find patients that can be treated with targeted ALK inhibitors. This study intended to non-invasively predict the ALK rearrangement status in lung adenocarcinomas by developing a machine learning model that combines PET/CT radiomic features and clinical characteristics. Methods Five hundred twenty-six patients of lung adenocarcinoma with PET/CT scan examination were enrolled, including 109 positive and 417 negative patients for ALK rearrangements from February 2016 to March 2019. The Artificial Intelligence Kit software was used to extract radiomic features of PET/CT images. The maximum relevance minimum redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) logistic regression were further employed to select the most distinguishable radiomic features to construct predictive models. The mRMR is a feature selection method, which selects the features with high correlation to the pathological results (maximum correlation), meanwhile retain the features with minimum correlation between them (minimum redundancy). LASSO is a statistical formula whose main purpose is the feature selection and regularization of data model. LASSO method regularizes model parameters by shrinking the regression coefficients, reducing some of them to zero. The feature selection phase occurs after the shrinkage, where every non-zero value is selected to be used in the model. Receiver operating characteristic (ROC) analysis was used to evaluate the performance of the models, and the performance of different models was compared by the DeLong test. Results A total of 22 radiomic features were extracted from PET/CT images for constructing the PET/CT radiomic model, and majority of these features used were based on CT features (20 out of 22), only 2 PET features were included (PET percentile 10 and PET difference entropy). Moreover, three clinical features associated with ALK mutation (age, burr and pleural effusion) were also employed to construct a combined model of PET/CT and clinical model. We found that this combined model PET/CT-clinical model has a significant advantage to predict the ALK mutation status in the training group (AUC = 0.87) and the testing group (AUC = 0.88) compared with the clinical model alone in the training group (AUC = 0.76) and the testing group (AUC = 0.74) respectively. However, there is no significant difference between the combined model and PET/CT radiomic model. Conclusions This study demonstrated that PET/CT radiomics-based machine learning model has potential to be used as a non-invasive diagnostic method to help diagnose ALK mutation status for lung adenocarcinoma patients in the clinic.
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Affiliation(s)
- Cheng Chang
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xiaoyan Sun
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Gang Wang
- Statistical Center, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hong Yu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenlu Zhao
- Department of Radiology, Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yaqiong Ge
- Pharmaceutical Diagnostic Department, GE Healthcare China, Shanghai, China
| | - Shaofeng Duan
- Pharmaceutical Diagnostic Department, GE Healthcare China, Shanghai, China
| | - Xiaohua Qian
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Rui Wang
- Department of Thoracic Surgery, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Bei Lei
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Lihua Wang
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Liu Liu
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Maomei Ruan
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Hui Yan
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Ciyi Liu
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Chen
- Department of Ultrasound, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
| | - Wenhui Xie
- Department of Nuclear Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.,Clinical and Translational Center in Shanghai Chest Hospital, Shanghai Key Laboratory for Molecular Imaging, Shanghai University of Medicine and Health Sciences, Shanghai, China
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176
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Choi W, Nadeem S, Alam SR, Deasy JO, Tannenbaum A, Lu W. Reproducible and Interpretable Spiculation Quantification for Lung Cancer Screening. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105839. [PMID: 33221055 PMCID: PMC7920914 DOI: 10.1016/j.cmpb.2020.105839] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 11/08/2020] [Indexed: 05/28/2023]
Abstract
Spiculations are important predictors of lung cancer malignancy, which are spikes on the surface of the pulmonary nodules. In this study, we proposed an interpretable and parameter-free technique to quantify the spiculation using area distortion metric obtained by the conformal (angle-preserving) spherical parameterization. We exploit the insight that for an angle-preserved spherical mapping of a given nodule, the corresponding negative area distortion precisely characterizes the spiculations on that nodule. We introduced novel spiculation scores based on the area distortion metric and spiculation measures. We also semi-automatically segment lung nodule (for reproducibility) as well as vessel and wall attachment to differentiate the real spiculations from lobulation and attachment. A simple pathological malignancy prediction model is also introduced. We used the publicly-available LIDC-IDRI dataset pathologists (strong-label) and radiologists (weak-label) ratings to train and test radiomics models containing this feature, and then externally validate the models. We achieved AUC = 0.80 and 0.76, respectively, with the models trained on the 811 weakly-labeled LIDC datasets and tested on the 72 strongly-labeled LIDC and 73 LUNGx datasets; the previous best model for LUNGx had AUC = 0.68. The number-of-spiculations feature was found to be highly correlated (Spearman's rank correlation coefficient ρ=0.44) with the radiologists' spiculation score. We developed a reproducible and interpretable, parameter-free technique for quantifying spiculations on nodules. The spiculation quantification measures was then applied to the radiomics framework for pathological malignancy prediction with reproducible semi-automatic segmentation of nodule. Using our interpretable features (size, attachment, spiculation, lobulation), we were able to achieve higher performance than previous models. In the future, we will exhaustively test our model for lung cancer screening in the clinic.
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Affiliation(s)
- Wookjin Choi
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA; Department of Engineering and Computer Science, Virginia State University, 1 Hayden St, Petersburg, VA 23806, USA
| | - Saad Nadeem
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA.
| | - Sadegh R Alam
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY 11790, USA
| | - Wei Lu
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY 10065, USA
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177
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Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021; 5:203-218. [PMID: 33589781 PMCID: PMC8118147 DOI: 10.1038/s41551-020-00681-x] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 12/17/2020] [Indexed: 02/08/2023]
Abstract
High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.
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Affiliation(s)
- Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Nicholas P Reder
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Kevin W Eliceiri
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
- Morgridge Institute for Research, Madison, WI, USA.
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.
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178
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Astaraki M, Zakko Y, Toma Dasu I, Smedby Ö, Wang C. Benign-malignant pulmonary nodule classification in low-dose CT with convolutional features. Phys Med 2021; 83:146-153. [DOI: 10.1016/j.ejmp.2021.03.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 03/01/2021] [Accepted: 03/05/2021] [Indexed: 12/24/2022] Open
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179
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Carles M, Fechter T, Radicioni G, Schimek-Jasch T, Adebahr S, Zamboglou C, Nicolay NH, Martí-Bonmatí L, Nestle U, Grosu AL, Baltas D, Mix M, Gkika E. FDG-PET Radiomics for Response Monitoring in Non-Small-Cell Lung Cancer Treated with Radiation Therapy. Cancers (Basel) 2021; 13:cancers13040814. [PMID: 33672052 PMCID: PMC7919471 DOI: 10.3390/cancers13040814] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary In this study, we strive to identify clinically relevant image feature (IF) changes during chemoradiation in patients with non-small-cell lung cancer (NSCLC) to be able to predict tumor responses in an early stage of treatment. All patients underwent static (3D) and respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability such as noise, resolution and movement through breathing. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Abstract The aim of this study is to identify clinically relevant image feature (IF) changes during chemoradiation and evaluate their efficacy in predicting treatment response. Patients with non-small-cell lung cancer (NSCLC) were enrolled in two prospective trials (STRIPE, PET-Plan). We evaluated 48 patients who underwent static (3D) and retrospectively-respiratory-gated 4D PET/CT scans before treatment and a 3D scan during or after treatment. Our proposed method rejects IF changes due to intrinsic variability. The IF variability observed across 4D PET is employed as a patient individualized normalization factor to emphasize statistically relevant IF changes during treatment. Predictions of overall survival (OS), local recurrence (LR) and distant metastasis (DM) were evaluated. From 135 IFs, only 17 satisfied the required criteria of being normally distributed across 4D PET and robust between 3D and 4D images. Changes during treatment in the area-under-the-curve of the cumulative standard-uptake-value histogram (δAUCCSH) within primary tumor discriminated (AUC = 0.87, Specificity = 0.78) patients with and without LR. The resulted prognostic model was validated with a different segmentation method (AUC = 0.83) and in a different patient cohort (AUC = 0.63). The quantification of tumor FDG heterogeneity by δAUCCSH during chemoradiation correlated with the incidence of local recurrence and might be recommended for monitoring treatment response in patients with NSCLC.
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Affiliation(s)
- Montserrat Carles
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (T.F.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
- Correspondence:
| | - Tobias Fechter
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (T.F.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
| | - Gianluca Radicioni
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Tanja Schimek-Jasch
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Sonja Adebahr
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Constantinos Zamboglou
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Nils H. Nicolay
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Luis Martí-Bonmatí
- La Fe Health Research Institute, Biomedical Imaging Research Group (GIBI230-PREBI) and Imaging La Fe Node at Distributed Network for Biomedical Imaging (ReDIB) Unique Scientific and Technical Infrastructures (ICTS), 46026 Valencia, Spain;
| | - Ursula Nestle
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
- Department of Radiation Oncology, Kliniken Maria Hilf, GmbH Moenchengladbach, 41063 Moechengladbach, Germany
| | - Anca L. Grosu
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
| | - Dimos Baltas
- Department of Radiation Oncology, Division of Medical Physics, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (T.F.); (D.B.)
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
| | - Michael Mix
- Department of Nuclear Medicine, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany;
| | - Eleni Gkika
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Partner Site Freiburg of the German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (S.A.); (C.Z.); (N.H.N.); (U.N.); (A.L.G.); (E.G.)
- Department of Radiation Oncology, University Medical Center Freiburg, Faculty of Medicine, 79106 Freiburg, Germany; (G.R.); (T.S.-J.)
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Cutaia G, La Tona G, Comelli A, Vernuccio F, Agnello F, Gagliardo C, Salvaggio L, Quartuccio N, Sturiale L, Stefano A, Calamia M, Arnone G, Midiri M, Salvaggio G. Radiomics and Prostate MRI: Current Role and Future Applications. J Imaging 2021; 7:jimaging7020034. [PMID: 34460633 PMCID: PMC8321264 DOI: 10.3390/jimaging7020034] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Revised: 02/07/2021] [Accepted: 02/08/2021] [Indexed: 02/07/2023] Open
Abstract
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.
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Affiliation(s)
- Giuseppe Cutaia
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Giuseppe La Tona
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Albert Comelli
- Ri.Med Foundation, Via Bandiera 11, 90133 Palermo, Italy;
| | - Federica Vernuccio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Francesco Agnello
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Cesare Gagliardo
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Leonardo Salvaggio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
- Correspondence:
| | - Natale Quartuccio
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Letterio Sturiale
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy;
| | - Mauro Calamia
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Gaspare Arnone
- Nuclear Medicine Unit, ARNAS Ospedali Civico, Di Cristina e Benfratelli, 90133 Palermo, Italy; (N.Q.); (L.S.); (G.A.)
| | - Massimo Midiri
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
| | - Giuseppe Salvaggio
- Section of Radiology, BiND, University Hospital “Paolo Giaccone”, University of Palermo, Via del Vespro 129, 90127 Palermo, Italy; (G.C.); (G.L.T.); (F.V.); (F.A.); (C.G.); (M.C.); (M.M.); (G.S.)
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181
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Denzler S, Vuong D, Bogowicz M, Pavic M, Frauenfelder T, Thierstein S, Eboulet EI, Maurer B, Schniering J, Gabryś HS, Schmitt-Opitz I, Pless M, Foerster R, Guckenberger M, Tanadini-Lang S. Impact of CT convolution kernel on robustness of radiomic features for different lung diseases and tissue types. Br J Radiol 2021; 94:20200947. [PMID: 33544646 DOI: 10.1259/bjr.20200947] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
OBJECTIVES In this study, we aimed to assess the impact of different CT reconstruction kernels on the stability of radiomic features and the transferability between different diseases and tissue types. Three lung diseases were evaluated, i.e. non-small cell lung cancer (NSCLC), malignant pleural mesothelioma (MPM) and interstitial lung disease related to systemic sclerosis (SSc-ILD) as well as four different tissue types, i.e. primary tumor, largest involved lymph node ipsilateral and contralateral lung. METHODS Pre-treatment non-contrast enhanced CT scans from 23 NSCLC, 10 MPM and 12 SSc-ILD patients were collected retrospectively. For each patient, CT scans were reconstructed using smooth and sharp kernel in filtered back projection. The regions of interest (ROIs) were contoured on the smooth kernel-based CT and transferred to the sharp kernel-based CT. The voxels were resized to the largest voxel dimension of each cohort. In total, 1386 features were analyzed. Feature stability was assessed using the intraclass correlation coefficient. Features above the stability threshold >0.9 were considered stable. RESULTS We observed a strong impact of the reconstruction method on stability of the features (at maximum 26% of the 1386 features were stable). Intensity features were the most stable followed by texture and wavelet features. The wavelet features showed a positive correlation between percentage of stable features and size of the ROI (R2 = 0.79, p = 0.005). Lymph node radiomics showed poorest stability (<10%) and lung radiomics the largest stability (26%). Robustness analysis done on the contralateral lung could to a large extent be transferred to the ipsilateral lung, and the overlap of stable lung features between different lung diseases was more than 50%. However, results of robustness studies cannot be transferred between tissue types, which was investigated in NSCLC and MPM patients; the overlap of stable features for lymph node and lung, as well as for primary tumor and lymph node was very small in both disease types. CONCLUSION The robustness of radiomic features is strongly affected by different reconstruction kernels. The effect is largely influenced by the tissue type and less by the disease type. ADVANCES IN KNOWLEDGE The study presents to our knowledge the most complete analysis on the impact of convolution kernel on the robustness of CT-based radiomics for four relevant tissue types in three different lung diseases. .
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Affiliation(s)
- Sarah Denzler
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matea Pavic
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Thomas Frauenfelder
- Institute of Diagnostic and Interventional Radiology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | | | | | - Britta Maurer
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Janine Schniering
- Department of Rheumatology, Center of Experimental Rheumatology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Hubert Szymon Gabryś
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Isabelle Schmitt-Opitz
- Department of Thoracic Surgery, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Robert Foerster
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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182
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Lei J, Chen P, Zhang F, Zhang N, Zhu J, Wang X, Jiang T. M2 macrophages-derived exosomal microRNA-501-3p promotes the progression of lung cancer via targeting WD repeat domain 82. Cancer Cell Int 2021; 21:91. [PMID: 33546686 PMCID: PMC7866732 DOI: 10.1186/s12935-021-01783-5] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 01/20/2021] [Indexed: 12/12/2022] Open
Abstract
Background Exosomes are known to transmit microRNAs (miRNAs) to affect cancer progression, while the role of M2 macrophages-derived exosomes (M2 exosomes) conveying miR-501-3p in lung cancer (LC) remains unknown. We aim to explore the role of exosomal miR-501-3p in LC development via targeting WD repeat domain 82 (WDR82). Methods Lung cancer tissue and normal tissue specimens were collected, in which tumor-associated macrophages (TAM) were measured by immunohistochemistry. M2 macrophages were induced and treated with altered miR-501-3p, and then the exosomes were extracted and identified. MiR-501-3p and WDR82 expression in LC tissues and cell liens was determined. The predictive role of miR-501-3p in prognosis of LC patients was assessed, and the proliferation, colony formation ability, invasion, migration and apoptosis of the LC cells were determined. Targeting relationship between miR-501-3p and WDR82 was confirmed. Results TAM level was elevated in lung cancer tissues. MiR-501-3p was upregulated while WDR82 was downregulated in LC tissues and cell lines, and the M2 exosomes further upregulated miR-501-3p. M2 exosomes and exosomal miR-501-3p promoted LC cell growth. MiR-501-3p inhibition reversed the effect of M2 exosomes on LC cells. WDR82 was confirmed as a target gene of miR-501-3p. Conclusion M2 macrophages-derived exosomal miR-501-3p promotes the progression of LC via downregulating WDR82.
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Affiliation(s)
- Jie Lei
- Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, 569 Xin Si Road, Xi'an, 710038, Shanxi, China
| | - Peng Chen
- Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, 569 Xin Si Road, Xi'an, 710038, Shanxi, China
| | - Feng Zhang
- Department of Oncology, The Second Affiliated Hospital of Air Force Medical University, Xi'an, 710038, Shanxi, China
| | - Na Zhang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, 569 Xin Si Road, Xi'an, 710038, Shanxi, China
| | - Jianfei Zhu
- Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, 569 Xin Si Road, Xi'an, 710038, Shanxi, China
| | - Xiaoping Wang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, 569 Xin Si Road, Xi'an, 710038, Shanxi, China.
| | - Tao Jiang
- Department of Thoracic Surgery, The Second Affiliated Hospital of Air Force Medical University, 569 Xin Si Road, Xi'an, 710038, Shanxi, China.
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183
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Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review. Cancers (Basel) 2021; 13:cancers13030552. [PMID: 33535569 PMCID: PMC7867056 DOI: 10.3390/cancers13030552] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 01/18/2021] [Accepted: 01/27/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary The increasing interest in implementing artificial intelligence in radiomic models has occurred alongside advancement in the tools used for computer-aided diagnosis. Such tools typically apply both statistical and machine learning methodologies to assess the various modalities used in medical image analysis. Specific to prostate cancer, the radiomics pipeline has multiple facets that are amenable to improvement. This review discusses the steps of a magnetic resonance imaging based radiomics pipeline. Present successes, existing opportunities for refinement, and the most pertinent pending steps leading to clinical validation are highlighted. Abstract The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.
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184
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Rodríguez M, Ajona D, Seijo LM, Sanz J, Valencia K, Corral J, Mesa-Guzmán M, Pío R, Calvo A, Lozano MD, Zulueta JJ, Montuenga LM. Molecular biomarkers in early stage lung cancer. Transl Lung Cancer Res 2021; 10:1165-1185. [PMID: 33718054 PMCID: PMC7947407 DOI: 10.21037/tlcr-20-750] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.
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Affiliation(s)
- María Rodríguez
- Department of Thoracic Surgery, Clínica Universidad de Navarra, Madrid, Spain
| | - Daniel Ajona
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Luis M Seijo
- Department of Pulmonology, Clínica Universidad de Navarra, Madrid, Spain.,Centro de Investigación Biomédica en Red Enfermedades Respiratorias (CIBERES), Madrid, Spain
| | - Julián Sanz
- Department of Pathology, Clínica Universidad de Navarra, Madrid, Spain
| | - Karmele Valencia
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Jesús Corral
- Department of Oncology, Clínica Universidad de Navarra, Madrid, Spain
| | - Miguel Mesa-Guzmán
- Department of Thoracic Surgery, Clínica Universidad de Navarra, Pamplona, Spain
| | - Rubén Pío
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Biochemistry and Genetics, School of Sciences, University of Navarra, Pamplona, Spain
| | - Alfonso Calvo
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain
| | - María D Lozano
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain.,Department of Pathology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Javier J Zulueta
- Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pulmonology, Clínica Universidad de Navarra, Pamplona, Spain
| | - Luis M Montuenga
- Program in Solid Tumors, Center for Applied Medical Research (CIMA), University of Navarra, Pamplona, Spain.,Navarra Institute for Health Research (IdISNA), Pamplona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Madrid, Spain.,Department of Pathology, Anatomy and Physiology, Schools of Medicine and Sciences, University of Navarra, Pamplona, Spain
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185
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Masud M, Sikder N, Nahid AA, Bairagi AK, AlZain MA. A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework. SENSORS 2021; 21:s21030748. [PMID: 33499364 PMCID: PMC7865416 DOI: 10.3390/s21030748] [Citation(s) in RCA: 69] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 01/10/2021] [Accepted: 01/18/2021] [Indexed: 12/19/2022]
Abstract
The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.
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Affiliation(s)
- Mehedi Masud
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
- Correspondence:
| | - Niloy Sikder
- Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh; (N.S.); (A.K.B.)
| | - Abdullah-Al Nahid
- Electronics and Communication Engineering Discipline, Khulna University, Khulna 9208, Bangladesh;
| | - Anupam Kumar Bairagi
- Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh; (N.S.); (A.K.B.)
| | - Mohammed A. AlZain
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
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186
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Prospective evaluation of metabolic intratumoral heterogeneity in patients with advanced gastric cancer receiving palliative chemotherapy. Sci Rep 2021; 11:296. [PMID: 33436659 PMCID: PMC7804009 DOI: 10.1038/s41598-020-78963-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 12/01/2020] [Indexed: 12/23/2022] Open
Abstract
Although metabolic intratumoral heterogeneity (ITH) gives important value on treatment responses and prognoses, its association with treatment outcomes have not been reported in gastric cancer (GC). We aimed to evaluate temporal changes in metabolic ITH and the associations with treatment responses, progression-free survival (PFS), and overall survival (OS) in advanced GC patients. Eighty-five patients with unresectable, locally advanced, or metastatic GC were prospectively enrolled before the first-line palliative chemotherapy and underwent [18F]FDG PET at baseline (TP1) and the first response follow-up evaluation (TP2). Standardized uptake values (SUVs), volumetric parameters, and textural features were evaluated in primary gastric tumor at TP1 and TP2. Of 85 patients, 44 had partial response, 33 had stable disease, and 8 progressed. From TP1 to TP2, metabolic ITH was significantly reduced (P < 0.01), and the degree of the decrease was greater in responders than in non-responders (P < 0.01). Using multiple Cox regression analyses, a low SUVmax at TP2, a high kurtosis at TP2 and larger decreases in the coefficient of variance were associated with better PFS. A low SUVmax at TP2, larger decreases in the metabolic tumor volume and larger decreased in the energy were associated with better OS. Age older than 60 years and responders also showed better OS. An early reduction in metabolic ITH is useful to predict treatment outcomes in advanced GC patients.
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187
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Lung cancer histology classification from CT images based on radiomics and deep learning models. Med Biol Eng Comput 2021; 59:215-226. [PMID: 33411267 DOI: 10.1007/s11517-020-02302-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 12/22/2020] [Indexed: 12/18/2022]
Abstract
Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor's CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc = 0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM + Inception outperformed experts by 7-25% (p < 0.05). The proposed methodology does not require detailed segmentation of the tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making. Lung cancer histology classification from CT images based on CNN + LSTM.
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Abstract
PURPOSE OF REVIEW Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications. RECENT FINDINGS The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others. Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.
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189
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Tang M, Hu X, Wang Y, Yao X, Zhang W, Yu C, Cheng F, Li J, Fang Q. Ivermectin, a potential anticancer drug derived from an antiparasitic drug. Pharmacol Res 2021; 163:105207. [PMID: 32971268 PMCID: PMC7505114 DOI: 10.1016/j.phrs.2020.105207] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/11/2020] [Accepted: 09/11/2020] [Indexed: 12/30/2022]
Abstract
Ivermectin is a macrolide antiparasitic drug with a 16-membered ring that is widely used for the treatment of many parasitic diseases such as river blindness, elephantiasis and scabies. Satoshi ōmura and William C. Campbell won the 2015 Nobel Prize in Physiology or Medicine for the discovery of the excellent efficacy of ivermectin against parasitic diseases. Recently, ivermectin has been reported to inhibit the proliferation of several tumor cells by regulating multiple signaling pathways. This suggests that ivermectin may be an anticancer drug with great potential. Here, we reviewed the related mechanisms by which ivermectin inhibited the development of different cancers and promoted programmed cell death and discussed the prospects for the clinical application of ivermectin as an anticancer drug for neoplasm therapy.
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Affiliation(s)
- Mingyang Tang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Clinical Medical Department, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Xiaodong Hu
- Department of Histology and Embryology, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Yi Wang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Department of Microbiology and Parasitology, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Xin Yao
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Department of Microbiology and Parasitology, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Wei Zhang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Clinical Medical Department, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Chenying Yu
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Clinical Medical Department, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Fuying Cheng
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Clinical Medical Department, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Jiangyan Li
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Department of Microbiology and Parasitology, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
| | - Qiang Fang
- Anhui Key Laboratory of Infection and Immunity, Bengbu Medical College, Bengbu, Anhui Province 233030, China; Department of Microbiology and Parasitology, Bengbu Medical College, Bengbu, Anhui Province 233030, China; School of Fundamental Sciences, Bengbu Medical College, Bengbu, Anhui Province 233030, China.
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190
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Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment. Eur J Nucl Med Mol Imaging 2020; 48:1785-1794. [PMID: 33326049 PMCID: PMC8113210 DOI: 10.1007/s00259-020-05142-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/29/2020] [Indexed: 02/08/2023]
Abstract
Purpose Advanced medical image analytics is increasingly used to predict clinical outcome in patients diagnosed with gastrointestinal tumors. This review provides an overview on the value of radiomics in predicting response to treatment in patients with gastrointestinal tumors. Methods A systematic review was conducted, according to PRISMA guidelines. The protocol was prospectively registered (PROSPERO: CRD42019128408). PubMed, Embase, and Cochrane databases were searched. Original studies reporting on the value of radiomics in predicting response to treatment in patients with a gastrointestinal tumor were included. A narrative synthesis of results was conducted. Results were stratified by tumor type. Quality assessment of included studies was performed, according to the radiomics quality score. Results The comprehensive literature search identified 1360 unique studies, of which 60 articles were included for analysis. In 37 studies, radiomics models and individual radiomic features showed good predictive performance for response to treatment (area under the curve or accuracy > 0.75). Various strategies to construct predictive models were used. Internal validation of predictive models was often performed, while the majority of studies lacked external validation. None of the studies reported predictive models implemented in clinical practice. Conclusion Radiomics is increasingly used to predict response to treatment in patients suffering from gastrointestinal cancer. This review demonstrates its great potential to help predict response to treatment and improve patient selection and early adjustment of treatment strategy in a non-invasive manner. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-020-05142-w.
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Li Y, Shang K, Bian W, He L, Fan Y, Ren T, Zhang J. Prediction of disease progression in patients with COVID-19 by artificial intelligence assisted lesion quantification. Sci Rep 2020; 10:22083. [PMID: 33328512 PMCID: PMC7745019 DOI: 10.1038/s41598-020-79097-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 11/27/2020] [Indexed: 01/08/2023] Open
Abstract
To investigate the value of artificial intelligence (AI) assisted quantification on initial chest CT for prediction of disease progression and clinical outcome in patients with coronavirus disease 2019 (COVID-19). Patients with confirmed COVID-19 infection and initially of non-severe type were retrospectively included. The initial CT scan on admission was used for imaging analysis. The presence of ground glass opacity (GGO), consolidation and other findings were visually evaluated. CT severity score was calculated according to the extent of lesion involvement. In addition, AI based quantification of GGO and consolidation volume were also performed. 123 patients (mean age: 64.43 ± 14.02; 62 males) were included. GGO + consolidation was more frequently revealed in progress-to-severe group whereas pure GGO was more likely to be found in non-severe group. Compared to non-severe group, patients in progress-to-severe group had larger GGO volume (167.33 ± 167.88 cm3 versus 101.12 ± 127 cm3, p = 0.013) as well as consolidation volume (40.85 ± 60.4 cm3 versus 6.63 ± 14.91 cm3, p < 0.001). Among imaging parameters, consolidation volume had the largest area under curve (AUC) in discriminating non-severe from progress-to-severe group (AUC = 0.796, p < 0.001) and patients with or without critical events (AUC = 0.754, p < 0.001). According to multivariate regression, consolidation volume and age were two strongest predictors for disease progression (hazard ratio: 1.053 and 1.071, p: 0.006 and 0.008) whereas age and diabetes were predictors for unfavorable outcome. Consolidation volume quantified on initial chest CT was the strongest predictor for disease severity progression and larger consolidation volume was associated with unfavorable clinical outcome.
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Affiliation(s)
- Yuehua Li
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China
| | - Kai Shang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China
| | - Wei Bian
- Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Li He
- Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Ying Fan
- Department of Respiratory Medicine, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Tao Ren
- Department of Nephrology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, China
| | - Jiayin Zhang
- Institute of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, #600, Yishan Rd, Shanghai, 200233, China.
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Dang Y, Wang R, Qian K, Lu J, Zhang H, Zhang Y. Clinical and radiological predictors of epidermal growth factor receptor mutation in nonsmall cell lung cancer. J Appl Clin Med Phys 2020; 22:271-280. [PMID: 33314737 PMCID: PMC7856515 DOI: 10.1002/acm2.13107] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 10/26/2020] [Accepted: 10/30/2020] [Indexed: 01/06/2023] Open
Abstract
Purpose To determine the prognostic factors of epidermal growth factor receptor (EGFR) mutation status in a group of patients with nonsmall cell lung cancer (NSCLC) by analyzing their clinical and radiological features. Materials and methods Patients with NSCLC who underwent EGFR mutation detection between 2014 and 2017 were included. Clinical features and general imaging features were collected, and radiomic features were extracted from CT data by 3D Slicer software. Prognostic factors of EGFR mutation status were selected by least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and receiver operating characteristic (ROC) curves were drawn for each prediction model of EGFR mutation. Results A total of 118 patients were enrolled in this study. The smoking index (P = 0.028), pleural retraction (P = 0.041), and three radiomic features were significantly associated with EGFR mutation status. The areas under the ROC curve (AUCs) for prediction models of clinical features, general imaging features, and radiomic features were 0.284, 0.703, and 0.815, respectively, and the AUC for the combined prediction model of the three models was 0.894. Finally, a nomogram was established for individualized EGFR mutation prediction. Conclusions The combination of radiomic features with clinical features and general imaging features can enable discrimination of EGFR mutation status better than the use of any group of features alone. Our study may help develop a noninvasive biomarker to identify EGFR mutation status by using a combination of the three group features.
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Affiliation(s)
- Yutao Dang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.,Department of Thoracic Surgery, Shijingshan Hospital of Beijing City, Shijingshan Teaching Hospital of Capital Medical University, Beijing, China
| | - Ruotian Wang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Kun Qian
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Yi Zhang
- Department of Thoracic Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China
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Zarzeczny A, Babyn P, Adams SJ, Longo J. Artificial intelligence-based imaging analytics and lung cancer diagnostics: Considerations for health system leaders. Healthc Manage Forum 2020; 34:169-174. [PMID: 33297774 DOI: 10.1177/0840470420975062] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Lung cancer is a leading cause of cancer death in Canada, and accurate, early diagnosis are critical to improving clinical outcomes. Artificial Intelligence (AI)-based imaging analytics are a promising healthcare innovation that aim to improve the accuracy and efficiency of lung cancer diagnosis. Maximizing their clinical potential while mitigating their risks and limitations will require focused leadership informed by interdisciplinary expertise and system-wide insight. We convened a knowledge exchange workshop with diverse Saskatchewan health system leaders and stakeholders to explore issues surrounding the use of AI in diagnostic imaging for lung cancer, including implementation opportunities, challenges, and priorities. This technology is anticipated to improve patient outcomes, reduce unnecessary healthcare spending, and increase knowledge. However, health system leaders must also address the needs for robust data, financial investment, effective communication and collaboration between healthcare sectors, privacy and data protections, and continued interdisciplinary research to achieve this technology's potential benefits.
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Affiliation(s)
- Amy Zarzeczny
- Johnson Shoyama Graduate School of Public Policy, 6846University of Regina, Regina, Saskatchewan, Canada
| | - Paul Babyn
- Department of Medical Imaging, Saskatchewan Health Authority, Saskatoon City Hospital, Saskatoon, Saskatchewan, Canada.,Department of Medical Imaging, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Scott J Adams
- Department of Medical Imaging, University of Saskatchewan, Royal University Hospital, Saskatoon, Saskatchewan, Canada
| | - Justin Longo
- Johnson Shoyama Graduate School of Public Policy, 6846University of Regina, Regina, Saskatchewan, Canada
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Vuong D, Tanadini-Lang S, Wu Z, Marks R, Unkelbach J, Hillinger S, Eboulet EI, Thierstein S, Peters S, Pless M, Guckenberger M, Bogowicz M. Radiomics Feature Activation Maps as a New Tool for Signature Interpretability. Front Oncol 2020; 10:578895. [PMID: 33364192 PMCID: PMC7753181 DOI: 10.3389/fonc.2020.578895] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 10/22/2020] [Indexed: 12/25/2022] Open
Abstract
Introduction In the field of personalized medicine, radiomics has shown its potential to support treatment decisions. However, the limited feature interpretability hampers its introduction into the clinics. Here, we propose a new methodology to create radiomics feature activation maps, which allows to identify the spatial-anatomical locations responsible for signature activation based on local radiomics. The feasibility of this technique will be studied for histological subtype differentiation (adenocarcinoma versus squamous cell carcinoma) in non-small cell lung cancer (NSCLC) using computed tomography (CT) radiomics. Materials and Methods Pre-treatment CT scans were collected from a multi-centric Swiss trial (training, n=73, IIIA/N2 NSCLC, SAKK 16/00) and an independent cohort (validation, n=32, IIIA/N2/IIIB NSCLC). Based on the gross tumor volume (GTV), four peritumoral region of interests (ROI) were defined: lung_exterior (expansion into the lung), iso_exterior (expansion into lung and soft tissue), gradient (GTV border region), GTV+Rim (GTV and iso_exterior). For each ROI, 154 radiomic features were extracted using an in-house developed software implementation (Z-Rad, Python v2.7.14). Features robust against delineation variability served as an input for a multivariate logistic regression analysis. Model performance was quantified using the area under the receiver operating characteristic curve (AUC) and verified using five-fold cross validation and internal validation. Local radiomic features were extracted from the GTV+Rim ROI using non-overlapping 3x3x3 voxel patches previously marked as GTV or rim. A binary activation map was created for each patient using the median global feature value from the training. The ratios of activated/non-activated patches of GTV and rim regions were compared between histological subtypes (Wilcoxon test). Results Iso_exterior, gradient, GTV+Rim showed good performances for histological subtype prediction (AUCtraining=0.68-0.72 and AUCvalidation=0.73-0.74) whereas GTV and lung_exterior models failed validation. GTV+Rim model feature activation maps showed that local texture feature distribution differed significantly between histological subtypes in the rim (p=0.0481) but not in the GTV (p=0.461). Conclusion In this exploratory study, radiomics-based prediction of NSCLC histological subtypes was predominantly based on the peritumoral region indicating that radiomics activation maps can be useful for tracing back the spatial location of regions responsible for signature activation.
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Affiliation(s)
- Diem Vuong
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Stephanie Tanadini-Lang
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Ze Wu
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Robert Marks
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Jan Unkelbach
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sven Hillinger
- Department of Thoracic Surgery, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Eric Innocents Eboulet
- Department of Clinical Trial Management, Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Sandra Thierstein
- Department of Clinical Trial Management, Swiss Group for Clinical Cancer Research (SAKK) Coordinating Center, Bern, Switzerland
| | - Solange Peters
- Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland
| | - Miklos Pless
- Department of Medical Oncology, Kantonsspital Winterthur, Winterthur, Switzerland
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Marta Bogowicz
- Department of Radiation Oncology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
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Alvarez-Jimenez C, Sandino AA, Prasanna P, Gupta A, Viswanath SE, Romero E. Identifying Cross-Scale Associations between Radiomic and Pathomic Signatures of Non-Small Cell Lung Cancer Subtypes: Preliminary Results. Cancers (Basel) 2020; 12:cancers12123663. [PMID: 33297357 PMCID: PMC7762258 DOI: 10.3390/cancers12123663] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 12/18/2022] Open
Abstract
(1) Background: Despite the complementarity between radiology and histopathology, both from a diagnostic and a prognostic perspective, quantitative analyses of these modalities are usually performed in disconnected silos. This work presents initial results for differentiating two major non-small cell lung cancer (NSCLC) subtypes by exploring cross-scale associations between Computed Tomography (CT) images and corresponding digitized pathology images. (2) Methods: The analysis comprised three phases, (i) a multi-resolution cell density quantification to identify discriminant pathomic patterns for differentiating adenocarcinoma (ADC) and squamous cell carcinoma (SCC), (ii) radiomic characterization of CT images by using Haralick descriptors to quantify tumor textural heterogeneity as represented by gray-level co-occurrences to discriminate the two pathological subtypes, and (iii) quantitative correlation analysis between the multi-modal features to identify potential associations between them. This analysis was carried out using two publicly available digitized pathology databases (117 cases from TCGA and 54 cases from CPTAC) and a public radiological collection of CT images (101 cases from NSCLC-R). (3) Results: The top-ranked cell density pathomic features from the histopathology analysis were correlation, contrast, homogeneity, sum of entropy and difference of variance; which yielded a cross-validated AUC of 0.72 ± 0.02 on the training set (CPTAC) and hold-out validation AUC of 0.77 on the testing set (TCGA). Top-ranked co-occurrence radiomic features within NSCLC-R were contrast, correlation and sum of entropy which yielded a cross-validated AUC of 0.72 ± 0.01. Preliminary but significant cross-scale associations were identified between cell density statistics and CT intensity values using matched specimens available in the TCGA cohort, which were used to significantly improve the overall discriminatory performance of radiomic features in differentiating NSCLC subtypes (AUC = 0.78 ± 0.01). (4) Conclusions: Initial results suggest that cross-scale associations may exist between digital pathology and CT imaging which can be used to identify relevant radiomic and histopathology features to accurately distinguish lung adenocarcinomas from squamous cell carcinomas.
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Affiliation(s)
- Charlems Alvarez-Jimenez
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Alvaro A. Sandino
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
| | - Prateek Prasanna
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11794, USA;
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA;
| | - Satish E. Viswanath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Eduardo Romero
- Computer Imaging and Medical Application Laboratory, Universidad Nacional de Colombia, Bogotá 111321, Colombia; (C.A.-J.); (A.A.S.)
- Correspondence:
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Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neurooncol Adv 2020; 2:iv3-iv14. [PMID: 33521636 PMCID: PMC7829475 DOI: 10.1093/noajnl/vdaa148] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Neuro-oncology largely consists of malignancies of the brain and central nervous system including both primary as well as metastatic tumors. Currently, a significant clinical challenge in neuro-oncology is to tailor therapies for patients based on a priori knowledge of their survival outcome or treatment response to conventional or experimental therapies. Radiomics or the quantitative extraction of subvisual data from conventional radiographic imaging has recently emerged as a powerful data-driven approach to offer insights into clinically relevant questions related to diagnosis, prediction, prognosis, as well as assessing treatment response. Furthermore, radiogenomic approaches provide a mechanism to establish statistical correlations of radiomic features with point mutations and next-generation sequencing data to further leverage the potential of routine MRI scans to serve as "virtual biopsy" maps. In this review, we provide an introduction to radiomic and radiogenomic approaches in neuro-oncology, including a brief description of the workflow involving preprocessing, tumor segmentation, and extraction of "hand-crafted" features from the segmented region of interest, as well as identifying radiogenomic associations that could ultimately lead to the development of reliable prognostic and predictive models in neuro-oncology applications. Lastly, we discuss the promise of radiomics and radiogenomic approaches in personalizing treatment decisions in neuro-oncology, as well as the challenges with clinical adoption, which will rely heavily on their demonstrated resilience to nonstandardization in imaging protocols across sites and scanners, as well as in their ability to demonstrate reproducibility across large multi-institutional cohorts.
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Affiliation(s)
- Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Pallavi Tiwari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
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197
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Liu R, Zhang Y, Ding Y, Zhang S, Pan L. Characteristics of TGFBR1-EGFR-CTNNB1-CDH1 Signaling Axis in Wnt-Regulated Invasion and Migration in Lung Cancer. Cell Transplant 2020; 29:963689720969167. [PMID: 33231090 PMCID: PMC7784602 DOI: 10.1177/0963689720969167] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
This study aimed to explore the characteristics of TGFBR1-epidermal growth factor receptor (EGFR)-CTNNB1-CDH1 axis in regulating the invasion and migration in lung cancer. Using the small interfering RNA technology, EGFR was silenced in H2170 and H1299 cells. Then, the colony formation, migration, and invasion abilities were detected using colony-forming assay and transwell assay. Moreover, the mRNA expression of smad2, smad3, CTNNB1, and CDH1, and the protein expression of TGFBR1, CDH1, and TCF were determined using the real-time polymerase chain reaction and western blotting. The results showed that silencing EGFR could significantly decrease the colony-forming ability in H2170 and H1299. Knocking down EGFR could significantly inhibit the invasion and migration ability of H2179 and H1299. Inhibiting the expression of EGFR could significantly decrease the expression of smad2, smad3, CDH1, and CTNNB1, with all P-values <0.05. In addition, silencing EGFR could markedly decrease the expression of TGFBR1 and CDH1 in H1299 and H2170, with all P-values <0.05. In conclusion, silencing EGFR could significantly regulate the progression of lung cancer via TGFBR1-EGFR-CTNNB1-CDH1 axis in Wnt signaling pathway.
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Affiliation(s)
- Rong Liu
- Department of Radiology, First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Yusui Zhang
- Department of Radiology, First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Yuan Ding
- Department of Radiology, First Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, China
| | - Shuai Zhang
- Department of Interventional Radiology, the Affiliated Cancer Hospital of Guizhou Medical University, Guiyang, China
- Shuai Zhang, Department of Interventional Radiology, the Affiliated Cancer Hospital of Guizhou Medical University, No. 1 West Beijing Road, Guiyang 550004, China.
| | - Long Pan
- Department of Interventional and Vascular Surgery, Tenth People’s Hospital of Tongji University, Shanghai, China
- Long Pan, Department of Interventional and Vascular Surgery, Tenth People’s Hospital of Tongji University. No. 301 Middle Yan Chang Road, Shanghai 200072, China.
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Shi H, Zhang N, Wu X, Zhang Y. Multimodal lung tumor image recognition algorithm based on integrated convolutional neural network. CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE 2020; 32. [DOI: 10.1002/cpe.4965] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2018] [Accepted: 07/31/2018] [Indexed: 08/30/2023]
Abstract
SummaryLung cancer has become one of the major diseases that seriously threaten human health. Early diagnosis of lung cancer is very important for the quality of lung cancer patients. Researching computer‐assisted systems for lung cancer recognition is one of the effective means to help physicians quickly diagnose lung cancer. Therefore, this paper studies the multimodal recognition algorithm for lung images. This algorithm uses CT images, PET images, and PET/CT images of lung tumors as the experimental objects and uses convolutional neural network to realize lung tumor recognition. Based on the recognition of lung tumors by convolutional neural networks, an integrated convolutional neural network was used to identify lung tumors to improve the recognition accuracy and reduce the training time. The experimental results show that the convolutional neural network can effectively identify the lung tumor images. The number of iterations and batch size in the training process will have an impact on the recognition of lung tumors. The integrated convolutional neural network designed is superior to single convolutional neural network in terms of recognition accuracy and time consumption.
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Affiliation(s)
- Hongyan Shi
- College of Mechanical Engineering Inner Mongolia University for the Nationalities Tongliao 028000 China
| | - NanDong Zhang
- Urinery of National University of the Inner Mongol hospital. Master of medicine Tongliao 028000 China
| | - Xiao‐qiang Wu
- College of Mechanical Engineering Inner Mongolia University for the Nationalities Tongliao 028000 China
| | - Yu‐Dong Zhang
- School of Computer Science University of Leicester Leicester, Leicester LE1 7RH UK
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Abunahel BM, Pontre B, Kumar H, Petrov MS. Pancreas image mining: a systematic review of radiomics. Eur Radiol 2020; 31:3447-3467. [PMID: 33151391 DOI: 10.1007/s00330-020-07376-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/25/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To systematically review published studies on the use of radiomics of the pancreas. METHODS The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study. RESULTS A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003). CONCLUSIONS Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice. KEY POINTS • Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.
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Affiliation(s)
| | - Beau Pontre
- School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Maxim S Petrov
- School of Medicine, University of Auckland, Auckland, New Zealand.
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Zhuo E, Cai C, Liu W, Li K, Zhao W. Downregulated microRNA-140-5p expression regulates apoptosis, migration and invasion of lung cancer cells by targeting zinc finger protein 800. Oncol Lett 2020; 20:390. [PMID: 33193850 PMCID: PMC7656116 DOI: 10.3892/ol.2020.12253] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 09/18/2020] [Indexed: 02/06/2023] Open
Abstract
Despite advances in the diagnosis and treatment in recent years, lung cancer is still one of the primary causes of cancer-associated morbidity and mortality in globally. Abnormally expressed microRNAs (miRNAs/miRs) in tumor tissues serve vital roles in the pathological mechanism of tumors and have become prospective biomarkers for cancer diagnosis. The present study aimed to investigate the effects of the miR-140-5p/zinc finger protein 800 (ZNF800) axis in lung carcinoma, and determine its potential underlying molecular mechanisms. The degree of cell proliferation was assessed via the MTT assay, while the migratory and invasive abilities of lung cancer cells were determined via the Transwell and Matrigel assays. The expression levels of miR-140-5p and ZNF800 were detected via reverse transcription-quantitative PCR and western blot analyses. The results demonstrated that miR-140-5p expression was notably higher in normal human bronchial epithelial cells compared with the respective lung cancer cell lines, H292, PC-9, CL1-5 and H460. Furthermore, miR-140-5p expression increased in the lung cancer cells compared with the control cells following transfection with miR-140-5p mimic. Overexpressing miR-140-5p significantly suppressed the proliferative, invasive and migratory abilities of H460 and PC-9 cells, and stimulated cell apoptosis by upregulating the expression of cleaved-caspase-3. Notably, these effects were reversed following transfection with miR-140-5p inhibitor. miR-140-5p was predicted as a negative regulator of ZNF800, and ZNF800 knockdown significantly suppressed the proliferative and metastatic abilities of lung adenocarcinoma (LUAD) cells, which was comparable to the effects of miR-140-5p mimic. Taken together, these results suggest that miR-140-5p may block the malignant phenotype of LUAD by negatively regulating ZNF800 expression. Thus, the miR-140-5p/ZNF800 axis may be used as an alternative therapeutic target for lung carcinoma in general, and LUAD in particular.
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Affiliation(s)
- Enqing Zhuo
- Department of 2nd Oncology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510317, P.R. China
| | - Changqing Cai
- Department of 2nd Oncology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510317, P.R. China
| | - Wenzhe Liu
- Department of 2nd Oncology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510317, P.R. China
| | - Kunsong Li
- Department of 2nd Oncology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510317, P.R. China
| | - Wenzhen Zhao
- Department of 2nd Oncology, Guangdong Second Provincial General Hospital, Guangzhou, Guangdong 510317, P.R. China
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