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Magoulianitis V, Yang J, Yang Y, Xue J, Kaneko M, Cacciamani G, Abreu A, Duddalwar V, Kuo CCJ, Gill IS, Nikias C. PCa-RadHop: A transparent and lightweight feed-forward method for clinically significant prostate cancer segmentation. Comput Med Imaging Graph 2024; 116:102408. [PMID: 38908295 DOI: 10.1016/j.compmedimag.2024.102408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Revised: 05/30/2024] [Accepted: 05/31/2024] [Indexed: 06/24/2024]
Abstract
Prostate Cancer is one of the most frequently occurring cancers in men, with a low survival rate if not early diagnosed. PI-RADS reading has a high false positive rate, thus increasing the diagnostic incurred costs and patient discomfort. Deep learning (DL) models achieve a high segmentation performance, although require a large model size and complexity. Also, DL models lack of feature interpretability and are perceived as "black-boxes" in the medical field. PCa-RadHop pipeline is proposed in this work, aiming to provide a more transparent feature extraction process using a linear model. It adopts the recently introduced Green Learning (GL) paradigm, which offers a small model size and low complexity. PCa-RadHop consists of two stages: Stage-1 extracts data-driven radiomics features from the bi-parametric Magnetic Resonance Imaging (bp-MRI) input and predicts an initial heatmap. To reduce the false positive rate, a subsequent stage-2 is introduced to refine the predictions by including more contextual information and radiomics features from each already detected Region of Interest (ROI). Experiments on the largest publicly available dataset, PI-CAI, show a competitive performance standing of the proposed method among other deep DL models, achieving an area under the curve (AUC) of 0.807 among a cohort of 1,000 patients. Moreover, PCa-RadHop maintains orders of magnitude smaller model size and complexity.
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Affiliation(s)
- Vasileios Magoulianitis
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA.
| | - Jiaxin Yang
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Yijing Yang
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Jintang Xue
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Masatomo Kaneko
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Giovanni Cacciamani
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Andre Abreu
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Vinay Duddalwar
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA; Department of Radiology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - C-C Jay Kuo
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
| | - Inderbir S Gill
- Department of Urology, Keck School of Medicine, University of Southern California (USC), 1975 Zonal Ave., Los Angeles, 90033, CA, USA
| | - Chrysostomos Nikias
- Electrical and Computer Engineering Department, University of Southern California (USC), 3740 McClintock Ave., Los Angeles, 90089, CA, USA
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Xia W, Sun T, Wang Y, Tian Y, Yan L, Liang Y, He C, Zhang J, Huang H. A morphological study of symptomatic uterine niche using three-dimensional models from thin-slice magnetic resonance imaging. Reprod Biomed Online 2024; 48:103683. [PMID: 38340538 DOI: 10.1016/j.rbmo.2023.103683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/31/2023] [Accepted: 11/02/2023] [Indexed: 02/12/2024]
Abstract
RESEARCH QUESTION Is there a correlation between various morphological parameters of the uterine niche and post-menstrual spotting using three-dimensional models from thin-slice (1 mm) magnetic resonance imaging (MRI)? DESIGN This study retrospectively identified women diagnosed with a symptomatic niche by thin-slice MRI between December 2019 and December 2021. Univariable and multivariable linear regression models assessed the correlations between morphological parameters and the duration post-menstrual spotting. Morphological differences of the niche formed by one versus two Caesarean sections were analysed by univariable and multivariable logistic analysis. RESULTS A total of 205 women diagnosed with symptomatic niche were included in the study. The niche among most women with post-menstrual spotting was ellipsoidal, with width greater than length greater than depth, from which niche volume was estimated based on manual measurements (volume = 0.520 × length × width × depth). Manually calculated niche length (β = 0.257, 95% confidence interval [CI] 0.040-0.473, P = 0.020) and radiomically assessed minor axis length (β = 0.329, 95% CI 0.009-0.795, P = 0.045) both positively correlated with the duration of post-menstrual spotting, whereas the distance between the niche and external os (β = -0.120, 95% CI -0.202 to -0.038, P = 0.004) was inversely correlated. Women with two Cesarean sections reported more days of post-menstrual spotting (8.76 ± 3.54 versus 6.68 ± 3.90 days, P < 0.001) and had increased niche length diameter (adjusted odds ratio [aOR] 1.304, 95% CI 1.190-1.429) and a smaller surface-area-to-volume ratio (aOR 0.296, 95% CI 0.129-0.680). CONCLUSIONS Niche-associated post-menstrual spotting correlates with the length diameter of the niche and the distance between the niche and external os. Niches in women after two Caesarean sections tend to be longer in length diameter and more spherical.
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Affiliation(s)
- Wei Xia
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China
| | - Taotao Sun
- Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China; Department of Radiology, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yang Wang
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China
| | - Yuan Tian
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China
| | - Li Yan
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China
| | - Yan Liang
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China
| | - Chuqing He
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China
| | - Jian Zhang
- Department of Gynecology and Obstetrics, International Peace Maternity and Child Health Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key Laboratory of Embryo Original Diseases, Shanghai, China; Shanghai Municipal Key Clinical Specialty, Shanghai, China.
| | - Hefeng Huang
- Obstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, China.
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Mukherjee S, Papadopoulos D, Chari N, Ellis D, Charitopoulos K, Charitopoulos I, Bishara S. High-grade prostate cancer demonstrates preferential growth in the cranio-caudal axis and provides discrimination of disease grade in an MRI parametric model. Br J Radiol 2024; 97:574-582. [PMID: 38276882 PMCID: PMC11027337 DOI: 10.1093/bjr/tqad066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/06/2023] [Accepted: 10/16/2023] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES To determine if multiparametric MRI prostate cancer (PC) lesion dimensions in different axes could distinguish between PC, grade group (GG) >2, and GG >3 on targeted transperineal biopsy and create and validate a predictive model on a separate cohort. METHODS The maximum transverse, anterio-posterior, and cranio-caudal lesion dimensions were assessed against the presence of any cancer, GG >2, and GG >3 on biopsy by binary logistic regression. The optimum multivariate models were evaluated on a separate cohort. RESULTS One hundred and ninety-three lesions from 148 patients were evaluated. Increased lesion volume, Prostate Specific Antigen (PSA), Prostate Imaging Reporting and Data System score, and decreased Apparent Diffusion Coefficient (ADC) were associated with increased GG (P < .001). The ratio of cranio-caudal to anterior-posterior lesion dimension increased from 1.20 (95% CI, 1.14-1.25) for GG ≤ 3 to 1.43 (95% CI, 1.28-1.57) for GG > 3 (P = .0022). The cranio-caudal dimension of the lesion was the strongest predictor of GG >3 (P = .000, area under the receiver operator characteristic curve [AUC] = 0.81). The best multivariate models had an AUC of 0.84 for cancer, 0.88 for GG > 2, and 0.89 for GG > 3. These models were evaluated on a separate cohort of 40 patients with 61 lesions. They demonstrated an AUC, sensitivity, and specificity of 0.82, 82.3%, and 55.5%, respectively, for the detection of cancer. For GG > 2, the models achieved an AUC of 0.84, sensitivity of 91.7%, and specificity of 69.4%. Additionally, for GG > 3, the models showed an AUC of 0.92, sensitivity of 88.9%, and specificity of 98.1%. CONCLUSIONS Cranio-caudal lesion dimension when used in conjunction with other parameters can create a model superior to the Prostate Imaging Reporting and Data Systems score in predicting cancer. ADVANCES IN KNOWLEDGE Higher-grade PC has a propensity to grow in the cranio-caudal direction, and this could be factored into MRI-based predictive models of prostate biopsy grade.
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Affiliation(s)
- Subhabrata Mukherjee
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Dimitrios Papadopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Natasha Chari
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - David Ellis
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Konstantinos Charitopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Ivo Charitopoulos
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
| | - Samuel Bishara
- Department of Urology, West Middlesex Hospital, Chelsea and Westminster NHS Trust, Twickenham Road, London, TW7 6AF, United Kingdom
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Zhang YF, Zhou C, Guo S, Wang C, Yang J, Yang ZJ, Wang R, Zhang X, Zhou FH. Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer. J Cancer Res Clin Oncol 2024; 150:78. [PMID: 38316655 PMCID: PMC10844393 DOI: 10.1007/s00432-023-05574-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Accepted: 12/04/2023] [Indexed: 02/07/2024]
Abstract
PURPOSE Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer. METHODS AND MATERIALS Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors. RESULTS The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit. CONCLUSION Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.
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Affiliation(s)
- Yun-Feng Zhang
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Chuan Zhou
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Sheng Guo
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Chao Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Jin Yang
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China
| | - Zhi-Jun Yang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Rong Wang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
- Department of Nuclear Medicine, Gansu Provincial Hospital, Lanzhou, 730000, China
| | - Xu Zhang
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Feng-Hai Zhou
- The First Clinical Medical College of Gansu University of Chinese Medicine, Lanzhou, 730000, China.
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China.
- Department of Urology, Gansu Provincial Hospital, Lanzhou, 730000, China.
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Nicoletti G, Mazzetti S, Maimone G, Cignini V, Cuocolo R, Faletti R, Gatti M, Imbriaco M, Longo N, Ponsiglione A, Russo F, Serafini A, Stanzione A, Regge D, Giannini V. Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI. Cancers (Basel) 2024; 16:203. [PMID: 38201630 PMCID: PMC10778513 DOI: 10.3390/cancers16010203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/19/2023] [Accepted: 12/29/2023] [Indexed: 01/12/2024] Open
Abstract
In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.
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Affiliation(s)
- Giulia Nicoletti
- Department of Electronics and Telecommunications, Polytechnic of Turin, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy;
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Giovanni Maimone
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Valentina Cignini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Renato Cuocolo
- Department of Medicine, Surgery, and Dentistry, University of Salerno, Via Salvador Allende, 43, 84081 Baronissi, Italy;
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy;
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Filippo Russo
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
| | - Alessandro Serafini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Via Pansini, 5, 80131 Naples, Italy; (M.I.); (A.P.)
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
- Department of Translational Research, Via Risorgimento, 36, University of Pisa, 56126 Pisa, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Corso Dogliotti, 14, 10126 Turin, Italy; (V.C.); (R.F.); (A.S.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Strada Provinciale, 142—KM 3.95, 10060 Candiolo, Italy; (S.M.); (G.M.); (F.R.); (D.R.)
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Zhou G, Zhou Y, Xu X, Zhang J, Xu C, Xu P, Zhu F. MRI-based radiomics signature: a potential imaging biomarker for prediction of microvascular invasion in combined hepatocellular-cholangiocarcinoma. Abdom Radiol (NY) 2024; 49:49-59. [PMID: 37831165 DOI: 10.1007/s00261-023-04049-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 09/03/2023] [Accepted: 09/04/2023] [Indexed: 10/14/2023]
Abstract
PURPOSE To investigate the potential of radiomics analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in preoperatively predicting microvascular invasion (MVI) in patients with combined hepatocellular-cholangiocarcinoma (cHCC-CC) before surgery. METHODS A cohort of 91 patients with histologically confirmed cHCC-CC who underwent preoperative liver DCE-MRI were enrolled and divided into a training cohort (27 MVI-positive and 37 MVI-negative) and a validation cohort (11 MVI-positive and 16 MVI-negative). Clinical characteristics and MR features of the patients were evaluated. Radiomics features were extracted from DCE-MRI, and a radiomics signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm in the training cohort. Prediction performance of the developed radiomics signature was evaluated by utilizing the receiver operating characteristic (ROC) analysis. RESULTS Larger tumor size and higher Radscore were associated with the presence of MVI in the training cohort (p = 0.026 and < 0.001, respectively), and theses findings were also confirmed in the validation cohort (p = 0.040 and 0.001, respectively). The developed radiomics signature, composed of 4 stable radiomics features, showed high prediction performance in both the training cohort (AUC = 0.866, 95% CI 0.757-0.938, p < 0.001) and validation cohort (AUC = 0.841, 95% CI 0.650-0.952, p < 0.001). CONCLUSIONS The radiomics signature developed from DCE-MRI can be a reliable imaging biomarker to preoperatively predict MVI in cHCC-CC.
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Affiliation(s)
- Guofeng Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Xun Xu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Pengju Xu
- Department of Radiology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
- Department of Radiology, Zhongshan Hospital, Shanghai Institute of Medical Imaging, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
| | - Feipeng Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
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7
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Stefano A, Bertelli E, Comelli A, Gatti M, Stanzione A. Editorial: Radiomics and radiogenomics in genitourinary oncology: artificial intelligence and deep learning applications. FRONTIERS IN RADIOLOGY 2023; 3:1325594. [PMID: 38192376 PMCID: PMC10773800 DOI: 10.3389/fradi.2023.1325594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 12/06/2023] [Indexed: 01/10/2024]
Affiliation(s)
- Alessandro Stefano
- Institute ofMolecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy
| | - Elena Bertelli
- Department of Radiology, Careggi University Hospital, Florence, Italy
| | | | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, Naples, Italy
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8
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Duenweg SR, Bobholz SA, Barrett MJ, Lowman AK, Winiarz A, Nath B, Stebbins M, Bukowy J, Iczkowski KA, Jacobsohn KM, Vincent-Sheldon S, LaViolette PS. T2-Weighted MRI Radiomic Features Predict Prostate Cancer Presence and Eventual Biochemical Recurrence. Cancers (Basel) 2023; 15:4437. [PMID: 37760407 PMCID: PMC10526331 DOI: 10.3390/cancers15184437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Prostate cancer (PCa) is the most diagnosed non-cutaneous cancer in men. Despite therapies such as radical prostatectomy, which is considered curative, distant metastases may form, resulting in biochemical recurrence (BCR). This study used radiomic features calculated from multi-parametric magnetic resonance imaging (MP-MRI) to evaluate their ability to predict BCR and PCa presence. Data from a total of 279 patients, of which 46 experienced BCR, undergoing MP-MRI prior to surgery were assessed for this study. After surgery, the prostate was sectioned using patient-specific 3D-printed slicing jigs modeled using the T2-weighted imaging (T2WI). Sectioned tissue was stained, digitized, and annotated by a GU-fellowship trained pathologist for cancer presence. Digitized slides and annotations were co-registered to the T2WI and radiomic features were calculated across the whole prostate and cancerous lesions. A tree regression model was fitted to assess the ability of radiomic features to predict BCR, and a tree classification model was fitted with the same radiomic features to classify regions of cancer. We found that 10 radiomic features predicted eventual BCR with an AUC of 0.97 and classified cancer at an accuracy of 89.9%. This study showcases the application of a radiomic feature-based tool to screen for the presence of prostate cancer and assess patient prognosis, as determined by biochemical recurrence.
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Affiliation(s)
- Savannah R. Duenweg
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Samuel A. Bobholz
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Michael J. Barrett
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Allison K. Lowman
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Aleksandra Winiarz
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Biprojit Nath
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - Margaret Stebbins
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
| | - John Bukowy
- Department of Electrical Engineering and Computer Science, Milwaukee School of Engineering, 1025 N Broadway, Milwaukee, WI 53202, USA
| | - Kenneth A. Iczkowski
- Department of Pathology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA;
| | - Kenneth M. Jacobsohn
- Department of Urology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Stephanie Vincent-Sheldon
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
| | - Peter S. LaViolette
- Department of Biophysics, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA; (S.R.D.); (M.S.)
- Department of Radiology, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
- Department of Biomedical Engineering, Medical College of Wisconsin, 8701 Watertown Plank Rd., Milwaukee, WI 53226, USA
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9
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Özdemir H, Azamat S, Sam Özdemir M. Can Only the Shape Feature in Radiomics Help Machine Learning Show That Bladder Cancer Has Invaded Muscles? Cureus 2023; 15:e45488. [PMID: 37859896 PMCID: PMC10584356 DOI: 10.7759/cureus.45488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/18/2023] [Indexed: 10/21/2023] Open
Abstract
OBJECTIVES The presence of muscle invasion is an important factor in establishing a treatment strategy for bladder cancer (BCa). The aim of this study is to reveal the diagnostic performance of radiomic shape features in predicting muscle-invasive BCa. METHODS In this study, 60 patients with histologically proven BCa who underwent a preoperative MRI were retrospectively recruited. The whole tumor volume was segmented on apparent diffusion coefficient (ADC) maps and T2W images. Afterward, the shape features of the volume of interest were extracted using PyRadiomics. Machine learning classification was performed using statistically different shape features in MATLAB® (The MathWorks, Inc., Natick, Massachusetts, United States). RESULTS The findings revealed that 27 bladder cancer patients had muscle invasion, while 33 had superficial bladder cancer (53 men and seven women; mean age: 62±14). Surface area, volume, and relevant features were significantly greater in the invasive group than in the non-invasive group based on the ADC maps (P<0.05). Superficial bladder cancer had a more spherical form compared to invasive bladder cancer (P=0.05) with both imaging modalities. Flatness and elongation did not differ significantly between groups with either modality (P>0.05). Logistic regression had the highest accuracy of 83.3% (sensitivity 82.8%, specificity 84%) in assessing invasion based on the shape features of ADC maps, while K-nearest neighbors had the highest accuracy of 78.2% (sensitivity 79.1%, specificity 69.4%) in assessing invasion based on T2W images. CONCLUSIONS Shape features can be helpful in predicting muscle invasion in bladder cancer using machine learning methods.
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Affiliation(s)
- Harun Özdemir
- Department of Urology, Başakşehir Çam and Sakura City Hospital, Istanbul, TUR
| | - Sena Azamat
- Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, TUR
| | - Merve Sam Özdemir
- Department of Radiology, Başakşehir Çam and Sakura City Hospital, Istanbul, TUR
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10
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Li L, Shiradkar R, Tirumani SH, Bittencourt LK, Fu P, Mahran A, Buzzy C, Stricker PD, Rastinehad AR, Magi-Galluzzi C, Ponsky L, Klein E, Purysko AS, Madabhushi A. Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer. Eur J Radiol Open 2023; 10:100496. [PMID: 37396490 PMCID: PMC10311200 DOI: 10.1016/j.ejro.2023.100496] [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: 03/22/2023] [Revised: 06/01/2023] [Accepted: 06/04/2023] [Indexed: 07/04/2023] Open
Abstract
Background around one third of clinically significant prostate cancer (CsPCa) foci are reported to be MRI non-visible (MRI─). Objective To quantify the differences between MR visible (MRI+) and MRI─ CsPCa using intra- and peri-lesional radiomic features on bi-parametric MRI (bpMRI). Methods This retrospective and multi-institutional study comprised 164 patients with pre-biopsy 3T prostate multi-parametric MRI from 2014 to 2017. The MRI─ CsPCa referred to lesions with PI-RADS v2 score < 3 but ISUP grade group > 1. Three experienced radiologists were involved in annotating lesions and PI-RADS assignment. The validation set (Dv) comprised 52 patients from a single institution, the remaining 112 patients were used for training (Dt). 200 radiomic features were extracted from intra-lesional and peri-lesional regions on bpMRI.Logistic regression with least absolute shrinkage and selection operator (LASSO) and 10-fold cross-validation was applied on Dt to identify radiomic features associated with MRI─ and MRI+ CsPCa to generate corresponding risk scores RMRI─ and RMRI+. RbpMRI was further generated by integrating RMRI─ and RMRI+. Statistical significance was determined using the Wilcoxon signed-rank test. Results Both intra-lesional and peri-lesional bpMRI Haralick and CoLlAGe radiomic features were significantly associated with MRI─ CsPCa (p < 0.05). Intra-lesional ADC Haralick and CoLlAGe radiomic features were significantly different among MRI─ and MRI+ CsPCa (p < 0.05). RbpMRI yielded the highest AUC of 0.82 (95 % CI 0.72-0.91) compared to AUCs of RMRI+ 0.76 (95 % CI 0.63-0.89), and PI-RADS 0.58 (95 % CI 0.50-0.72) on Dv. RbpMRI correctly reclassified 10 out of 14 MRI─ CsPCa on Dv. Conclusion Our preliminary results demonstrated that both intra-lesional and peri-lesional bpMRI radiomic features were significantly associated with MRI─ CsPCa. These features could assist in CsPCa identification on bpMRI.
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Affiliation(s)
- Lin Li
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | - Rakesh Shiradkar
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology
| | | | | | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Amr Mahran
- Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Christina Buzzy
- Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA
| | | | | | | | - Lee Ponsky
- Urology Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Eric Klein
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Andrei S. Purysko
- Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH, USA
- Imaging Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology
- Atlanta Veterans Affairs Medical Center, Atlanta, GA, United States
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11
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Özer H, Koplay M, Baytok A, Seher N, Demir LS, Kılınçer A, Kaynar M, Göktaş S. Texture analysis of multiparametric magnetic resonance imaging for differentiating clinically significant prostate cancer in the peripheral zone. Turk J Med Sci 2023; 53:701-711. [PMID: 37476894 PMCID: PMC10387871 DOI: 10.55730/1300-0144.5633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 02/01/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Texture analysis (TA) provides additional tissue heterogeneity data that may assist in differentiating peripheral zone(PZ) lesions in multiparametric magnetic resonance imaging (mpMRI). This study investigates the role of magnetic resonance imaging texture analysis (MRTA) in detecting clinically significant prostate cancer (csPCa) in the PZ. METHODS This retrospective study included 80 consecutive patients who had an mpMRI and a prostate biopsy for suspected prostate cancer. Two radiologists in consensus interpreted mpMRI and performed texture analysis based on their histopathology. The first-, second-, and higher-order texture parameters were extracted from mpMRI and were compared between groups. Univariate and multivariate logistic regression analyses were performed using the texture parameters to determine the independent predictors of csPCa. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance of the texture parameters. RESULTS : In the periferal zone, 39 men had csPCa, while 41 had benign lesions or clinically insignificant prostate cancer (cisPCa). Themajority of texture parameters showed statistically significant differences between the groups. Univariate ROC analysis showed that the ADC mean and ADC median were the best variables in differentiating csPCa (p < 0.001). The first-order logistic regression model (mean + entropy) based on the ADC maps had a higher AUC value (0.996; 95% CI: 0.989-1) than other texture-based logistic regression models (p < 0.001). DISCUSSION MRTA is useful in differentiating csPCa from other lesions in the PZ. Consequently, the first-order multivariate regressionmodel based on ADC maps had the highest diagnostic performance in differentiating csPCa.
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Affiliation(s)
- Halil Özer
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Mustafa Koplay
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Ahmet Baytok
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Nusret Seher
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Lütfi Saltuk Demir
- Department of Public Health, Faculty of Medicine, Necmettin Erbakan University, Konya, Turkey
| | - Abidin Kılınçer
- Department of Radiology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Mehmet Kaynar
- Department of Urology, Faculty of Medicine, Selcuk University, Konya, Turkey
| | - Serdar Göktaş
- Department of Urology, Faculty of Medicine, Selcuk University, Konya, Turkey
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12
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Goh V. PI-RADS and Multiparametric MRI: The Shape of Things to Come for Prostate Cancer. Radiology 2023; 307:e230643. [PMID: 37129487 DOI: 10.1148/radiol.230643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Affiliation(s)
- Vicky Goh
- From the School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, United Kingdom; and Department of Radiology, Guy's & St Thomas' NHS Foundation Trust, First Floor, Lambeth Wing, St Thomas' Hospital, Westminster Bridge Road, London SE1 7EH, United Kingdom
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13
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Nai YH, Cheong DLH, Roy S, Kok T, Stephenson MC, Schaefferkoetter J, Totman JJ, Conti M, Eriksson L, Robins EG, Wang Z, Chua WY, Ang BWL, Singha AK, Thamboo TP, Chiong E, Reilhac A. Comparison of quantitative parameters and radiomic features as inputs into machine learning models to predict the Gleason score of prostate cancer lesions. Magn Reson Imaging 2023; 100:64-72. [PMID: 36933775 DOI: 10.1016/j.mri.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 03/01/2023] [Accepted: 03/12/2023] [Indexed: 03/18/2023]
Abstract
INTRODUCTION The classification of prostate cancer (PCa) lesions using Prostate Imaging Reporting and Data System (PI-RADS) suffers from poor inter-reader agreement. This study compared quantitative parameters or radiomic features from multiparametric magnetic resonance imaging (mpMRI) or positron emission tomography (PET), as inputs into machine learning (ML) to predict the Gleason scores (GS) of detected lesions for improved PCa lesion classification. METHODS 20 biopsy-confirmed PCa subjects underwent imaging before radical prostatectomy. A pathologist assigned GS from tumour tissue. Two radiologists and one nuclear medicine physician delineated the lesions on the mpMR and PET images, yielding 45 lesion inputs. Seven quantitative parameters were extracted from the lesions, namely T2-weighted (T2w) image intensity, apparent diffusion coefficient (ADC), transfer constant (KTRANS), efflux rate constant (Kep), and extracellular volume ratio (Ve) from mpMR images, and SUVmean and SUVmax from PET images. Eight radiomic features were selected out of 109 radiomic features from T2w, ADC and PET images. Quantitative parameters or radiomic features, with risk factors of age, prostate-specific antigen (PSA), PSA density and volume, of 45 different lesion inputs were input in different combinations into four ML models - Decision Tree (DT), Support Vector Machine (SVM), k-Nearest-Neighbour (kNN), Ensembles model (EM). RESULTS SUVmax yielded the highest accuracy in discriminating detected lesions. Among the 4 ML models, kNN yielded the highest accuracies of 0.929 using either quantitative parameters or radiomic features with risk factors as input. CONCLUSIONS ML models' performance is dependent on the input combinations and risk factors further improve ML classification accuracy.
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Affiliation(s)
- Ying-Hwey Nai
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Dennis Lai Hong Cheong
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Sharmili Roy
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Trina Kok
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mary C Stephenson
- Centre for Translational MR, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Josh Schaefferkoetter
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - John J Totman
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Maurizio Conti
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Lars Eriksson
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN, USA
| | - Edward G Robins
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Singapore BioImaging Consortium, Agency for Science, Technology and Research (A*Star), Singapore
| | - Ziting Wang
- Department of Urology, National University Hospital, Singapore
| | - Wynne Yuru Chua
- Department of Diagnostic Imaging, National University Hospital, Singapore
| | | | | | | | - Edmund Chiong
- Department of Diagnostic Imaging, National University Hospital, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anthonin Reilhac
- Clinical Imaging Research Centre, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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14
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Belue MJ, Harmon SA, Lay NS, Daryanani A, Phelps TE, Choyke PL, Turkbey B. The Low Rate of Adherence to Checklist for Artificial Intelligence in Medical Imaging Criteria Among Published Prostate MRI Artificial Intelligence Algorithms. J Am Coll Radiol 2023; 20:134-145. [PMID: 35922018 PMCID: PMC9887098 DOI: 10.1016/j.jacr.2022.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Revised: 05/13/2022] [Accepted: 05/18/2022] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To determine the rigor, generalizability, and reproducibility of published classification and detection artificial intelligence (AI) models for prostate cancer (PCa) on MRI using the Checklist for Artificial Intelligence in Medical Imaging (CLAIM) guidelines, a 42-item checklist that is considered a measure of best practice for presenting and reviewing medical imaging AI research. MATERIALS AND METHODS This review searched English literature for studies proposing PCa AI detection and classification models on MRI. Each study was evaluated with the CLAIM checklist. The additional outcomes for which data were sought included measures of AI model performance (eg, area under the curve [AUC], sensitivity, specificity, free-response operating characteristic curves), training and validation and testing group sample size, AI approach, detection versus classification AI, public data set utilization, MRI sequences used, and definition of gold standard for ground truth. The percentage of CLAIM checklist fulfillment was used to stratify studies into quartiles. Wilcoxon's rank-sum test was used for pair-wise comparisons. RESULTS In all, 75 studies were identified, and 53 studies qualified for analysis. The original CLAIM items that most studies did not fulfill includes item 12 (77% no): de-identification methods; item 13 (68% no): handling missing data; item 15 (47% no): rationale for choosing ground truth reference standard; item 18 (55% no): measurements of inter- and intrareader variability; item 31 (60% no): inclusion of validated interpretability maps; item 37 (92% no): inclusion of failure analysis to elucidate AI model weaknesses. An AUC score versus percentage CLAIM fulfillment quartile revealed a significant difference of the mean AUC scores between quartile 1 versus quartile 2 (0.78 versus 0.86, P = .034) and quartile 1 versus quartile 4 (0.78 versus 0.89, P = .003) scores. Based on additional information and outcome metrics gathered in this study, additional measures of best practice are defined. These new items include disclosure of public dataset usage, ground truth definition in comparison to other referenced works in the defined task, and sample size power calculation. CONCLUSION A large proportion of AI studies do not fulfill key items in CLAIM guidelines within their methods and results sections. The percentage of CLAIM checklist fulfillment is weakly associated with improved AI model performance. Additions or supplementations to CLAIM are recommended to improve publishing standards and aid reviewers in determining study rigor.
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Affiliation(s)
- Mason J Belue
- Medical Research Scholars Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Stephanie A Harmon
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Nathan S Lay
- Staff Scientist, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Asha Daryanani
- Intramural Research Training Program Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Tim E Phelps
- Postdoctoral Fellow, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Peter L Choyke
- Artificial Intelligence Resource, Chief of Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland
| | - Baris Turkbey
- Senior Clinician/Director, Artificial Intelligence Resource, Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland.
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15
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Li S, Zheng T, Fan Z, Qu H, Wang J, Bi J, Lv Q, Zhang G, Cui X, Zhao Y. A dynamic-static combination model based on radiomics features for prostate cancer using multiparametric MRI. Phys Med Biol 2022; 68. [PMID: 36541844 DOI: 10.1088/1361-6560/aca954] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 12/06/2022] [Indexed: 12/12/2022]
Abstract
Objective. To propose a new dynamic multiparametric magnetic resonance imaging (mpMRI) radiomics method for the detection of prostate cancer (PCa), and establish a combined model using dynamic and static radiomics features based on this method.Approach. A total of 166 patients (82 PCa patients and 84 non-PCa patients) were enrolled in the study, and 31 872 mpMRI images were performed in a radiomics workflow. The whole prostate segmentation and traditional static radiomics features extraction were performed on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI,bvalue of 10, 50, 100, 150, 200, 400, 600, 800, 1000, 1500 s mm-2respectively), apparent diffusion coefficient (ADC), and T2-weighted imaging (T2WI) sequences respectively. Through the building of eachb-value DWI model and the analysis of the static key radiomics features, three types of dynamic features called standard discrete (SD), parameter (P) and relative change rate (RCR) were constructed. And the b-value parameters used to construct dynamic features were divided into three groups ('Df_', 'Db_' and 'Da_'): the front part (10-200 s mm-2), the back part (400-1500 s mm-2), and all (10-1500 s mm-2) of theb-values set, respectively. Afterwards, the dynamic mpMRI model and combined model construction were constructed, and the PCa discrimination performance of each model was evaluated.Main results.The models based on dynamic features showed good potential for PCa identification. Where, the results of Db_SD, Da_P and Db_P models were encouraging (test cohort AUCs: 90.78%, 87.60%, 86.3%), which was better than the commonly used ADC model (AUC of ADC was 75.48%). Among the combined models, the models using static features of T2WI and dynamic features performed the best. The AUC of Db_SD + T2WI, Db_P + T2WI and Db_RCR + T2WI model was 92.90%, 91.29% and 81.46%.Significance.The dynamic-static combination model based on dynamic mpMRI radiomics method has a good effect on the identification of PCa. This method has broad application prospects in PCa individual diagnosis management.
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Affiliation(s)
- Shuqin Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Tingting Zheng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Zhou Fan
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Hui Qu
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Jianfeng Wang
- Department of Urology Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, People's Republic of China
| | - Jianbin Bi
- Department of Urology Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, People's Republic of China
| | - Qingjie Lv
- Department of Pathology, Shengjing Hospital of China Medical University, Sanhao Street 36, Shenyang, 110001, People's Republic of China
| | - Gejun Zhang
- Department of Urology Surgery, The First Hospital of China Medical University, No.155 Nanjing North Street, Heping District, Shenyang, People's Republic of China
| | - Xiaoyu Cui
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China.,Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110169, People's Republic of China
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, People's Republic of China.,National and Local Joint Engineering Research Center of Immunodermatological Theranostics, No.155 Nanjing Bei Street, Heping District, Shenyang, 110001, People's Republic of China
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16
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Beyond Multiparametric MRI and towards Radiomics to Detect Prostate Cancer: A Machine Learning Model to Predict Clinically Significant Lesions. Cancers (Basel) 2022; 14:cancers14246156. [PMID: 36551642 PMCID: PMC9776977 DOI: 10.3390/cancers14246156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/08/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
The risk of misclassifying clinically significant prostate cancer (csPCa) by multiparametric magnetic resonance imaging is consistent, also using the updated PIRADS score and although different definitions of csPCa, patients with Gleason Grade group (GG) ≥ 3 have a significantly worse prognosis. This study aims to develop a machine learning model predicting csPCa (i.e., any GG ≥ 3 lesion at target biopsy) by mpMRI radiomic features and analyzing similarities between GG groups. One hundred and two patients with 117 PIRADS ≥ 3 lesions at mpMRI underwent target+systematic biopsy, providing histologic diagnosis of PCa, 61 GG < 3 and 56 GG ≥ 3. Features were generated locally from an apparent diffusion coefficient and selected, using the LASSO method and Wilcoxon rank-sum test (p < 0.001), to achieve only four features. After data augmentation, the features were exploited to train a support vector machine classifier, subsequently validated on a test set. To assess the results, Kruskal−Wallis and Wilcoxon rank-sum tests (p < 0.001) and receiver operating characteristic (ROC)-related metrics were used. GG1 and GG2 were equivalent (p = 0.26), whilst clear separations between either GG[1,2] and GG ≥ 3 exist (p < 10−6). On the test set, the area under the curve = 0.88 (95% CI, 0.68−0.94), with positive and negative predictive values being 84%. The features retain a histological interpretation. Our model hints at GG2 being much more similar to GG1 than GG ≥ 3.
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17
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68Ga-PSMA-11 PET/CT Features Extracted from Different Radiomic Zones Predict Response to Androgen Deprivation Therapy in Patients with Advanced Prostate Cancer. Cancers (Basel) 2022; 14:cancers14194838. [PMID: 36230761 PMCID: PMC9563455 DOI: 10.3390/cancers14194838] [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: 08/01/2022] [Revised: 09/19/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022] Open
Abstract
Purpose: Prediction of treatment response to androgen deprivation therapy (ADT) prior to treatment initiation remains difficult. This study was undertaken to investigate whether 68Ga-PSMA-11 PET/CT features extracted from different radiomic zones within the prostate gland might predict response to ADT in patients with advanced prostate cancer (PCa). Methods: A total of 35 patients with prostate adenocarcinoma underwent two 68Ga-PSMA-11 PET/CT scans—termed PET-1 and PET-2—before and after 3 months of ADT, respectively. The prostate was divided into three radiomic zones, with zone-1 being the metabolic tumor zone, zone-2 the proximal peripheral tumor zone, and zone-3 the extended peripheral tumor zone. Patients in the response group were those who showed a reduction ratio > 30% for PET-derived parameters measured at PET-1 and PET-2. The remaining patients were classified as non-responders. Results: Seven features (glcm_idmn, glcm_idn, glcm_imc1, ngtdm_Contrast, glrlm_rln, gldm_dn, and shape_MeshVolume) from zone-1, two features (gldm_sdlgle and shape_MinorAxisLength) from zone-2, and two features (diagnostics_Mask-interpolated_Minimum and shape_Sphericity) from zone-3 successfully distinguished responders from non-responders to ADT. One predictive feature (shape_SurfaceVolumeRatio) was consistently identified in all of the three zones. Conclusions: this study demonstrates the potential usefulness of radiomic features extracted from different prostatic zones in distinguishing responders from non-responders prior to ADT initiation.
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Villalonga JF, Solari D, Cuocolo R, De Lucia V, Ugga L, Gragnaniello C, Pailler JI, Cervio A, Campero A, Cavallo LM, Cappabianca P. Clinical application of the “sellar barrier’s concept” for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis. Front Surg 2022; 9:934721. [PMID: 36157423 PMCID: PMC9492953 DOI: 10.3389/fsurg.2022.934721] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Recently, it was defined that the sellar barrier entity could be identified as a predictor of cerebrospinal fluid (CSF) intraoperative leakage. The aim of this study is to validate the application of the sellar barrier concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning approach. Methods We conducted a prospective cohort study, from June 2019 to September 2020: data from 155 patients with pituitary subdiaphragmatic adenoma operated through endoscopic approach at the Division of Neurosurgery, Università degli Studi di Napoli “Federico II,” were included. Preoperative magnetic resonance images (MRI) and intraoperative findings were analyzed. After processing patient data, the experiment was conducted as a novelty detection problem, splitting outliers (i.e., patients with intraoperative fistula, n = 11/155) and inliers into separate datasets, the latter further separated into training (n = 115/144) and inlier test (n = 29/144) datasets. The machine learning analysis was performed using different novelty detection algorithms [isolation forest, local outlier factor, one-class support vector machine (oSVM)], whose performance was assessed separately and as an ensemble on the inlier and outlier test sets. Results According to the type of sellar barrier, patients were classified into two groups, i.e., strong and weak barrier; a third category of mixed barrier was defined when a case was neither weak nor strong. Significant differences between the three datasets were found for Knosp classification score (p = 0.0015), MRI barrier: strong (p = 1.405 × 10−6), MRI barrier: weak (p = 4.487 × 10−8), intraoperative barrier: strong (p = 2.788 × 10−7), and intraoperative barrier: weak (p = 2.191 × 10−10). We recorded 11 cases of intraoperative leakage that occurred in the majority of patients presenting a weak sellar barrier (p = 4.487 × 10−8) at preoperative MRI. Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF; 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble, respectively. Conclusions There is a true correlation between the type of sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.
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Affiliation(s)
- J. F. Villalonga
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
- Correspondence: J. F. Villalonga
| | - D. Solari
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - R. Cuocolo
- Department of Advanced Biomedical Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - V. De Lucia
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - L. Ugga
- Department of Advanced Biomedical Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - C. Gragnaniello
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
- Department of Neurological Surgery, Swedish Neuroscience Institute, Seattle, WA, United States
| | - J. I. Pailler
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
| | - A. Cervio
- Departamento de Neurocirugía, FLENI, Buenos Aires, Argentina
| | - A. Campero
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
| | - L. M. Cavallo
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
| | - P. Cappabianca
- Division of Neurosurgery, Department of Neurosciences, Reproductive and Odontostomatological Sciences, Universita’ degli Studi di Napoli Federico II, Naples, Italy
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19
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Fernandes MC, Yildirim O, Woo S, Vargas HA, Hricak H. The role of MRI in prostate cancer: current and future directions. MAGMA (NEW YORK, N.Y.) 2022; 35:503-521. [PMID: 35294642 PMCID: PMC9378354 DOI: 10.1007/s10334-022-01006-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 01/16/2022] [Accepted: 03/03/2022] [Indexed: 06/14/2023]
Abstract
There has been an increasing role of magnetic resonance imaging (MRI) in the management of prostate cancer. MRI already plays an essential role in the detection and staging, with the introduction of functional MRI sequences. Recent advancements in radiomics and artificial intelligence are being tested to potentially improve detection, assessment of aggressiveness, and provide usefulness as a prognostic marker. MRI can improve pretreatment risk stratification and therefore selection of and follow-up of patients for active surveillance. MRI can also assist in guiding targeted biopsy, treatment planning and follow-up after treatment to assess local recurrence. MRI has gained importance in the evaluation of metastatic disease with emerging technology including whole-body MRI and integrated positron emission tomography/MRI, allowing for not only better detection but also quantification. The main goal of this article is to review the most recent advances on MRI in prostate cancer and provide insights into its potential clinical roles from the radiologist's perspective. In each of the sections, specific roles of MRI tailored to each clinical setting are discussed along with its strengths and weakness including already established material related to MRI and the introduction of recent advancements on MRI.
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Affiliation(s)
- Maria Clara Fernandes
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Onur Yildirim
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Sungmin Woo
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
| | - Hebert Alberto Vargas
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
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20
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More than Meets the Eye: Using Textural Analysis and Artificial Intelligence as Decision Support Tools in Prostate Cancer Diagnosis—A Systematic Review. J Pers Med 2022; 12:jpm12060983. [PMID: 35743766 PMCID: PMC9225075 DOI: 10.3390/jpm12060983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 06/12/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
(1) Introduction: Multiparametric magnetic resonance imaging (mpMRI) is the main imagistic tool employed to assess patients suspected of harboring prostate cancer (PCa), setting the indication for targeted prostate biopsy. However, both mpMRI and targeted prostate biopsy are operator dependent. The past decade has been marked by the emerging domain of radiomics and artificial intelligence (AI), with extended application in medical diagnosis and treatment processes. (2) Aim: To present the current state of the art regarding decision support tools based on texture analysis and AI for the prediction of aggressiveness and biopsy assistance. (3) Materials and Methods: We performed literature research using PubMed MeSH, Scopus and WoS (Web of Science) databases and screened the retrieved papers using PRISMA principles. Articles that addressed PCa diagnosis and staging assisted by texture analysis and AI algorithms were included. (4) Results: 359 papers were retrieved using the keywords “prostate cancer”, “MRI”, “radiomics”, “textural analysis”, “artificial intelligence”, “computer assisted diagnosis”, out of which 35 were included in the final review. In total, 24 articles were presenting PCa diagnosis and prediction of aggressiveness, 7 addressed extracapsular extension assessment and 4 tackled computer-assisted targeted prostate biopsies. (5) Conclusions: The fusion of radiomics and AI has the potential of becoming an everyday tool in the process of diagnosis and staging of the prostate malignancies.
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21
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Discrimination of lipoma from atypical lipomatous tumor/well-differentiated liposarcoma using magnetic resonance imaging radiomics combined with machine learning. Jpn J Radiol 2022; 40:951-960. [DOI: 10.1007/s11604-022-01278-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
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22
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Abunahel BM, Pontre B, Ko J, Petrov MS. Towards developing a robust radiomics signature in diffuse diseases of the pancreas: Accuracy and stability of features derived from T1-weighted magnetic resonance imaging. J Med Imaging Radiat Sci 2022; 53:420-428. [DOI: 10.1016/j.jmir.2022.04.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 03/30/2022] [Accepted: 04/01/2022] [Indexed: 12/16/2022]
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23
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A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an Innovative 64Cu-Labeled Chelator in Mouse Models. J Imaging 2022; 8:jimaging8040092. [PMID: 35448219 PMCID: PMC9025273 DOI: 10.3390/jimaging8040092] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/16/2022] [Accepted: 03/23/2022] [Indexed: 02/05/2023] Open
Abstract
The 64Cu-labeled chelator was analyzed in vivo by positron emission tomography (PET) imaging to evaluate its biodistribution in a murine model at different acquisition times. For this purpose, nine 6-week-old female Balb/C nude strain mice underwent micro-PET imaging at three different time points after 64Cu-labeled chelator injection. Specifically, the mice were divided into group 1 (acquisition 1 h after [64Cu] chelator administration, n = 3 mice), group 2 (acquisition 4 h after [64Cu]chelator administration, n = 3 mice), and group 3 (acquisition 24 h after [64Cu] chelator administration, n = 3 mice). Successively, all PET studies were segmented by means of registration with a standard template space (3D whole-body Digimouse atlas), and 108 radiomics features were extracted from seven organs (namely, heart, bladder, stomach, liver, spleen, kidney, and lung) to investigate possible changes over time in [64Cu]chelator biodistribution. The one-way analysis of variance and post hoc Tukey Honestly Significant Difference test revealed that, while heart, stomach, spleen, kidney, and lung districts showed a very low percentage of radiomics features with significant variations (p-value < 0.05) among the three groups of mice, a large number of features (greater than 60% and 50%, respectively) that varied significantly between groups were observed in bladder and liver, indicating a different in vivo uptake of the 64Cu-labeled chelator over time. The proposed methodology may improve the method of calculating the [64Cu]chelator biodistribution and open the way towards a decision support system in the field of new radiopharmaceuticals used in preclinical imaging trials.
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24
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Zhou Y, Zhou G, Zhang J, Xu C, Zhu F, Xu P. DCE-MRI based radiomics nomogram for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from mass-forming intrahepatic cholangiocarcinoma. Eur Radiol 2022; 32:5004-5015. [PMID: 35128572 DOI: 10.1007/s00330-022-08548-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 12/19/2021] [Accepted: 12/20/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVE To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) MR images to preoperatively differentiate combined hepatocellular-cholangiocarcinoma (cHCC-CC) from mass-forming intrahepatic cholangiocarcinoma (IMCC). METHODS A total of 151 training cohort patients (45 cHCC-CC and 106 IMCC) and 65 validation cohort patients (19 cHCC-CC and 46 IMCC) were enrolled. Findings of clinical characteristics and MR features were analyzed. Radiomics features were extracted from the DCE-MR images. A radiomics signature was built based on radiomics features by the least absolute shrinkage and selection operator algorithm. Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical model. The radiomics signature and significant clinicoradiological variables were then incorporated into the radiomics nomogram by multivariate logistic regression analysis. Performance of the radiomics nomogram, radiomics signature, and clinical model was assessed by receiver operating characteristic and area under the curve (AUC) was compared. RESULTS Eleven radiomics features were selected to develop the radiomics signature. The radiomics nomogram integrating the alpha fetoprotein, background liver disease (cirrhosis or chronic hepatitis), and radiomics signature showed favorable calibration and discrimination performance with an AUC value of 0.945 in training cohort and 0.897 in validation cohort. The AUCs for the radiomics signature and clinical model were 0.848 and 0.856 in training cohort and 0.792 and 0.809 in validation cohort, respectively. The radiomics nomogram outperformed both the radiomics signature and clinical model alone (p < 0.05). CONCLUSION The radiomics nomogram based on DCE-MRI may provide an effective and noninvasive tool to differentiate cHCC-CC from IMCC, which could help guide treatment strategies. KEY POINTS • The radiomics signature based on dynamic contrast-enhanced magnetic resonance imaging is useful to preoperatively differentiate cHCC-CC from IMCC. • The radiomics nomogram showed the best performance in both training and validation cohorts for differentiating cHCC-CC from IMCC.
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Affiliation(s)
- Yang Zhou
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Guofeng Zhou
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China.,Shanghai Institute of Medical Imaging, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China
| | - Jiulou Zhang
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China
| | - Chen Xu
- Department of Pathology, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Feipeng Zhu
- Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, No 300, Guangzhou Road, Nanjing, 210029, Jiangsu Province, China.
| | - Pengju Xu
- Department of Radiology, Zhongshan Hospital, Fudan University, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China. .,Shanghai Institute of Medical Imaging, No.180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
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25
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Ghezzo S, Bezzi C, Presotto L, Mapelli P, Bettinardi V, Savi A, Neri I, Preza E, Samanes Gajate AM, De Cobelli F, Scifo P, Picchio M. State of the art of radiomic analysis in the clinical management of prostate cancer: A systematic review. Crit Rev Oncol Hematol 2021; 169:103544. [PMID: 34801699 DOI: 10.1016/j.critrevonc.2021.103544] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 10/18/2021] [Accepted: 10/18/2021] [Indexed: 02/04/2023] Open
Abstract
We present the current clinical applications of radiomics in the context of prostate cancer (PCa) management. Several online databases for original articles using a combination of the following keywords: "(radiomic or radiomics) AND (prostate cancer or prostate tumour or prostate tumor or prostate neoplasia)" have been searched. The selected papers have been pooled as focus on (i) PCa detection, (ii) assessing the clinical significance of PCa, (iii) biochemical recurrence prediction, (iv) radiation-therapy outcome prediction and treatment efficacy monitoring, (v) metastases detection, (vi) metastases prediction, (vii) prediction of extra-prostatic extension. Seventy-six studies were included for qualitative analyses. Classifiers powered with radiomic features were able to discriminate between healthy tissue and PCa and between low- and high-risk PCa. However, before radiomics can be proposed for clinical use its methods have to be standardized, and these first encouraging results need to be robustly replicated in large and independent cohorts.
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Affiliation(s)
| | | | - Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Mapelli
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Ilaria Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Erik Preza
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Francesco De Cobelli
- Vita-Salute San Raffaele University, Milan, Italy; Radiology Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Vita-Salute San Raffaele University, Milan, Italy; Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy.
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26
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Wen X, Shu Z, Li Y, Hu X, Gong X. Developing a model for estimating infarction onset time based on computed tomography radiomics in patients with acute middle cerebral artery occlusion. BMC Med Imaging 2021; 21:147. [PMID: 34635087 PMCID: PMC8507216 DOI: 10.1186/s12880-021-00678-1] [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: 05/20/2021] [Accepted: 09/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). METHODS A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. RESULTS The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665-0.875) and 0.792 (95% CI: 0.633-0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701-0.916) and 0.833 (95% CI: 0.702-0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614-0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712-0.928). CONCLUSIONS The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.
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Affiliation(s)
- Xuehua Wen
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Zhenyu Shu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Yumei Li
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xingfei Hu
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
| | - Xiangyang Gong
- Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China. .,Institute of Artificial Intelligence and Remote Imaging, Hangzhou Medical College, Hangzhou, Zhejiang, China.
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27
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Multiparametric MRI and Radiomics in Prostate Cancer: A Review of the Current Literature. Diagnostics (Basel) 2021; 11:diagnostics11101829. [PMID: 34679527 PMCID: PMC8534893 DOI: 10.3390/diagnostics11101829] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 09/26/2021] [Accepted: 09/27/2021] [Indexed: 12/22/2022] Open
Abstract
Prostate cancer (PCa) represents the fourth most common cancer and the fifth leading cause of cancer death of men worldwide. Multiparametric MRI (mp-MRI) has high sensitivity and specificity in the detection of PCa, and it is currently the most widely used imaging technique for tumor localization and cancer staging. mp-MRI plays a key role in risk stratification of naïve patients, in active surveillance for low-risk patients, and in monitoring recurrence after definitive therapy. Radiomics is an emerging and promising tool which allows a quantitative tumor evaluation from radiological images via conversion of digital images into mineable high-dimensional data. The purpose of radiomics is to increase the features available to detect PCa, to avoid unnecessary biopsies, to define tumor aggressiveness, and to monitor post-treatment recurrence of PCa. The integration of radiomics data, including different imaging modalities (such as PET-CT) and other clinical and histopathological data, could improve the prediction of tumor aggressiveness as well as guide clinical decisions and patient management. The purpose of this review is to describe the current research applications of radiomics in PCa on MR images.
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28
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Lei M, Varghese B, Hwang D, Cen S, Lei X, Desai B, Azadikhah A, Oberai A, Duddalwar V. Benchmarking Various Radiomic Toolkit Features While Applying the Image Biomarker Standardization Initiative toward Clinical Translation of Radiomic Analysis. J Digit Imaging 2021; 34:1156-1170. [PMID: 34545475 PMCID: PMC8554949 DOI: 10.1007/s10278-021-00506-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 08/07/2021] [Accepted: 08/17/2021] [Indexed: 01/06/2023] Open
Abstract
The image biomarkers standardization initiative (IBSI) was formed to address the standardization of extraction of quantifiable imaging metrics. Despite its effort, there remains a lack of consensus or established guidelines regarding radiomic feature terminology, the underlying mathematics and their implementation across various software programs. This creates a scenario where features extracted using different toolboxes cannot be used to build or validate the same model leading to a non-generalization of radiomic results. In this study, IBSI-established phantom and benchmark values were used to compare the variation of the radiomic features while using 6 publicly available software programs and 1 in-house radiomics pipeline. All IBSI-standardized features (11 classes, 173 in total) were extracted. The relative differences between the extracted feature values from the different software programs and the IBSI benchmark values were calculated to measure the inter-software agreement. To better understand the variations, features are further grouped into 3 categories according to their properties: 1) morphology, 2) statistic/histogram and 3)texture features. While a good agreement was observed for a majority of radiomics features across the various tested programs, relatively poor agreement was observed for morphology features. Significant differences were also found in programs that use different gray-level discretization approaches. Since these software programs do not include all IBSI features, the level of quantitative assessment for each category was analyzed using Venn and UpSet diagrams and quantified using two ad hoc metrics. Morphology features earned lowest scores for both metrics, indicating that morphological features are not consistently evaluated among software programs. We conclude that radiomic features calculated using different software programs may not be interchangeable. Further studies are needed to standardize the workflow of radiomic feature extraction.
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Affiliation(s)
- Mingxi Lei
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089 USA
| | - Bino Varghese
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Darryl Hwang
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Steven Cen
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, 3740 McClintock Avenue, Los Angeles, CA 90089 USA
| | - Xiaomeng Lei
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Bhushan Desai
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Afshin Azadikhah
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
| | - Assad Oberai
- Department of Aerospace and Mechanical Engineering, University of Southern California, 854 Downey Way, Los Angeles, CA 90089 USA
| | - Vinay Duddalwar
- Department of Radiology, University of Southern California, 1500 San Pablo Street, Los Angeles, CA 90033 USA
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29
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Ferro M, Crocetto F, Bruzzese D, Imbriaco M, Fusco F, Longo N, Napolitano L, La Civita E, Cennamo M, Liotti A, Lecce M, Russo G, Insabato L, Imbimbo C, Terracciano D. Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer. Cancers (Basel) 2021; 13:cancers13184723. [PMID: 34572950 PMCID: PMC8466029 DOI: 10.3390/cancers13184723] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 09/16/2021] [Accepted: 09/18/2021] [Indexed: 12/01/2022] Open
Abstract
Simple Summary In the last decades, the widespread use of PSA as the standard tool for prostate cancer diagnosis led to a high rate of overdiagnosis and overtreatment. More recently, multiparametric magnetic resonance imaging (mpMRI) became part of the diagnostic pathway, and several next-generation PSA-based tests (PHI, PHI density, 4Kscore, STHLM3) have been proposed. The multivariable approach promises to help with a better stratification of PCa patients at initial diagnosis. In this study, we evaluated the performance of the prostate health index (PHI) and mpMRI for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). Our findings suggested that PHI had a better ability than mpMRI to predict positive biopsy, whereas a comparable performance in the identification of pathological aggressive PCa was pointed out. Notably, PHI and PHI density might represent useful biomarkers to recognize high-grade PCa in patients with low or uncertain PI-RADS scores on mpMRI. Abstract Widespread use of PSA as the standard tool for prostate cancer (PCa) diagnosis led to a high rate of overdiagnosis and overtreatment. In this study, we evaluated the performance of the prostate health index (PHI) and multiparametric magnetic resonance imaging (mpMRI) for the prediction of positive biopsy and of high-grade PCa at radical prostatectomy (RP). To this end, we prospectively enrolled 196 biopsy-naïve patients who underwent mpMRI. A subgroup of 116 subjects with biopsy-proven PCa underwent surgery. We found that PHI significantly outperformed both PI-RADS score (difference in AUC: 0.14; p < 0.001) and PHI density (difference in AUC: 0.08; p = 0.002) in the ability to predict positive biopsy with a cut-off value of 42.7 as the best threshold. Conversely, comparing the performance in the identification of clinically significant prostate cancer (csPCa) at RP, we found that PHI ≥ 61.68 and PI-RADS score ≥ 4 were able to identify csPCa (Gleason score ≥ 7 (3 + 4)) both alone and added to a base model including age, PSA, fPSA-to-tPSA ratio and prostate volume. In conclusion, PHI had a better ability than PI-RADS score to predict positive biopsy, whereas it had a comparable performance in the identification of pathological csPCa.
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Affiliation(s)
- Matteo Ferro
- Division of Urology, European Institute of Oncology (IEO), IRCCS, 20141 Milan, Italy;
| | - Felice Crocetto
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.C.); (N.L.); (L.N.); (C.I.)
| | - Dario Bruzzese
- Department of Public Health, University of Naples “Federico II”, 80131 Naples, Italy; (D.B.); (G.R.)
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.I.); (L.I.)
| | - Ferdinando Fusco
- Department of Woman, Child and General and Specialized Surgery, University of Campania Luigi Vanvitelli, 80138 Naples, Italy;
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.C.); (N.L.); (L.N.); (C.I.)
| | - Luigi Napolitano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.C.); (N.L.); (L.N.); (C.I.)
| | - Evelina La Civita
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (E.L.C.); (M.C.); (A.L.); (M.L.)
| | - Michele Cennamo
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (E.L.C.); (M.C.); (A.L.); (M.L.)
| | - Antonietta Liotti
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (E.L.C.); (M.C.); (A.L.); (M.L.)
| | - Manuela Lecce
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (E.L.C.); (M.C.); (A.L.); (M.L.)
| | - Gianluca Russo
- Department of Public Health, University of Naples “Federico II”, 80131 Naples, Italy; (D.B.); (G.R.)
| | - Luigi Insabato
- Department of Advanced Biomedical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (M.I.); (L.I.)
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples “Federico II”, 80131 Naples, Italy; (F.C.); (N.L.); (L.N.); (C.I.)
| | - Daniela Terracciano
- Department of Translational Medical Sciences, University of Naples “Federico II”, 80131 Naples, Italy; (E.L.C.); (M.C.); (A.L.); (M.L.)
- Correspondence: ; Tel.: +39-8-1746-2038
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Prostate Cancer Radiogenomics-From Imaging to Molecular Characterization. Int J Mol Sci 2021; 22:ijms22189971. [PMID: 34576134 PMCID: PMC8465891 DOI: 10.3390/ijms22189971] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 09/06/2021] [Accepted: 09/10/2021] [Indexed: 12/24/2022] Open
Abstract
Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.
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Stanzione A, Ricciardi C, Cuocolo R, Romeo V, Petrone J, Sarnataro M, Mainenti PP, Improta G, De Rosa F, Insabato L, Brunetti A, Maurea S. MRI Radiomics for the Prediction of Fuhrman Grade in Clear Cell Renal Cell Carcinoma: a Machine Learning Exploratory Study. J Digit Imaging 2021; 33:879-887. [PMID: 32314070 DOI: 10.1007/s10278-020-00336-y] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
The Fuhrman nuclear grade is a recognized prognostic factor for patients with clear cell renal cell carcinoma (CCRCC) and its pre-treatment evaluation significantly affects decision-making in terms of management. In this study, we aimed to assess the feasibility of a combined approach of radiomics and machine learning based on MR images for a non-invasive prediction of Fuhrman grade, specifically differentiation of high- from low-grade tumor and grade assessment. Images acquired on a 3-Tesla scanner (T2-weighted and post-contrast) from 32 patients (20 with low-grade and 12 with high-grade tumor) were annotated to generate volumes of interest enclosing CCRCC lesions. After image resampling, normalization, and filtering, 2438 features were extracted. A two-step feature reduction process was used to between 1 and 7 features depending on the algorithm employed. A J48 decision tree alone and in combination with ensemble learning methods were used. In the differentiation between high- and low-grade tumors, all the ensemble methods achieved an accuracy greater than 90%. On the other end, the best results in terms of accuracy (84.4%) in the assessment of tumor grade were achieved by the random forest. These evidences support the hypothesis that a combined radiomic and machine learning approach based on MR images could represent a feasible tool for the prediction of Fuhrman grade in patients affected by CCRCC.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Carlo Ricciardi
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Renato Cuocolo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy.
| | - Valeria Romeo
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Jessica Petrone
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Michela Sarnataro
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Pier Paolo Mainenti
- Institute of Biostructures and Bioimaging of the National Research Council (CNR), Naples, Italy
| | - Giovanni Improta
- Department of Public Health, University of Naples "Federico II", Naples, Italy
| | - Filippo De Rosa
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Luigi Insabato
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
| | - Simone Maurea
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via S. Pansini, 5, 80123, Naples, Italy
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Stefano A, Comelli A. Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images. J Imaging 2021; 7:131. [PMID: 34460767 PMCID: PMC8404925 DOI: 10.3390/jimaging7080131] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/28/2021] [Accepted: 08/01/2021] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND In the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images. METHODS In a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures. RESULTS The experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user. CONCLUSIONS We demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.
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Affiliation(s)
- Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
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Montoya Perez I, Merisaari H, Jambor I, Ettala O, Taimen P, Knaapila J, Kekki H, Khan FL, Syrjälä E, Steiner A, Syvänen KT, Verho J, Seppänen M, Rannikko A, Riikonen J, Mirtti T, Lamminen T, Saunavaara J, Falagario U, Martini A, Pahikkala T, Pettersson K, Boström PJ, Aronen HJ. Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer. J Magn Reson Imaging 2021; 55:465-477. [PMID: 34227169 DOI: 10.1002/jmri.27811] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group ≥ 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). PURPOSE To develop and validate radiomics and kallikrein models for the detection of csPCa. STUDY TYPE Retrospective. POPULATION A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated. FIELD STRENGTH/SEQUENCE A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. ASSESSMENT In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. STATISTICAL TESTS For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value < 0.05 were considered significant. RESULTS The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488). DATA CONCLUSION The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains. LEVEL OF EVIDENCE 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ileana Montoya Perez
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Computing, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Harri Merisaari
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Computing, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland.,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Otto Ettala
- Department of Urology, University of Turku, Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, Department of Pathology, University of Turku, Turku University Hospital, Turku, Finland
| | - Juha Knaapila
- Department of Urology, University of Turku, Turku University Hospital, Turku, Finland
| | - Henna Kekki
- Department of Biotechnology, University of Turku, Turku, Finland
| | - Ferdhos L Khan
- Department of Biotechnology, University of Turku, Turku, Finland
| | - Elise Syrjälä
- Department of Computing, University of Turku, Turku, Finland
| | - Aida Steiner
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Kari T Syvänen
- Department of Urology, University of Turku, Turku University Hospital, Turku, Finland
| | - Janne Verho
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Marjo Seppänen
- Department of Surgery, Satakunta Central Hospital, Pori, Finland
| | - Antti Rannikko
- Department of Urology, Helsinki University, Helsinki University Hospital, Helsinki, Finland
| | - Jarno Riikonen
- Department of Urology, Tampere University Hospital, University of Tampere, Tampere, Finland
| | - Tuomas Mirtti
- Department of Pathology, University of Helsinki, Helsinki, Finland
| | - Tarja Lamminen
- Department of Urology, University of Turku, Turku University Hospital, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Ugo Falagario
- Department of Urology, University of Foggia, Foggia, Italy
| | - Alberto Martini
- Department of Oncology/Unit of Urology, Urological Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Tapio Pahikkala
- Department of Computing, University of Turku, Turku, Finland
| | - Kim Pettersson
- Department of Biotechnology, University of Turku, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku, Turku University Hospital, Turku, Finland
| | - Hannu J Aronen
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
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Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Eid M, Iannicelli E, Laghi A. Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications. Cancers (Basel) 2021; 13:cancers13112681. [PMID: 34072366 PMCID: PMC8197789 DOI: 10.3390/cancers13112681] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/26/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023] Open
Abstract
Simple Summary This Part II is an overview of the main applications of Radiomics in oncologic imaging with a focus on diagnosis, prognosis prediction and assessment of response to therapy in thoracic, genito-urinary, breast, neurologic, hematologic and musculoskeletal oncology. In this part II we describe the radiomic applications, limitations and future perspectives for each pre-eminent tumor. In the future, Radiomics could have a pivotal role in management of cancer patients as an imaging tool to support clinicians in decision making process. However, further investigations need to obtain some stable results and to standardize radiomic analysis (i.e., image acquisitions, segmentation and model building) in clinical routine. Abstract Radiomics has the potential to play a pivotal role in oncological translational imaging, particularly in cancer detection, prognosis prediction and response to therapy evaluation. To date, several studies established Radiomics as a useful tool in oncologic imaging, able to support clinicians in practicing evidence-based medicine, uniquely tailored to each patient and tumor. Mineable data, extracted from medical images could be combined with clinical and survival parameters to develop models useful for the clinicians in cancer patients’ assessment. As such, adding Radiomics to traditional subjective imaging may provide a quantitative and extensive cancer evaluation reflecting histologic architecture. In this Part II, we present an overview of radiomic applications in thoracic, genito-urinary, breast, neurological, hematologic and musculoskeletal oncologic applications.
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Affiliation(s)
- Damiano Caruso
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Michela Polici
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marta Zerunian
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Francesco Pucciarelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Gisella Guido
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Tiziano Polidori
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Federica Landolfi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Matteo Nicolai
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Elena Lucertini
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Mariarita Tarallo
- Department of Surgery “Pietro Valdoni”, Sapienza University of Rome, 00161 Rome, Italy;
| | - Benedetta Bracci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Ilaria Nacci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Carlotta Rucci
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Marwen Eid
- Internal Medicine, Northwell Health Staten Island University Hospital, Staten Island, New York, NY 10305, USA;
| | - Elsa Iannicelli
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
| | - Andrea Laghi
- Radiology Unit, Department of Medical Surgical Sciences and Translational Medicine, Sapienza University of Rome-Sant’Andrea University Hospital, Via di Grottarossa, 1035-1039, 00189 Rome, Italy; (D.C.); (M.P.); (M.Z.); (F.P.); (G.G.); (T.P.); (F.L.); (M.N.); (E.L.); (B.B.); (I.N.); (C.R.); (E.I.)
- Correspondence: ; Tel.: +39-0633775285
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Deep Myometrial Infiltration of Endometrial Cancer on MRI: A Radiomics-Powered Machine Learning Pilot Study. Acad Radiol 2021; 28:737-744. [PMID: 32229081 DOI: 10.1016/j.acra.2020.02.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 02/26/2020] [Accepted: 02/26/2020] [Indexed: 02/08/2023]
Abstract
RATIONALE AND OBJECTIVES To evaluate an MRI radiomics-powered machine learning (ML) model's performance for the identification of deep myometrial invasion (DMI) in endometrial cancer (EC) patients and explore its clinical applicability. MATERIALS AND METHODS Preoperative MRI scans of EC patients were retrospectively selected. Three radiologists performed whole-lesion segmentation on T2-weighted images for feature extraction. Feature robustness was tested before randomly splitting the population in training and test sets (80/20% proportion). A multistep feature selection was applied to the first, excluding noninformative, low variance features and redundant, highly-intercorrelated ones. A Random Forest wrapper was used to identify the most informative among the remaining. An ensemble of J48 decision trees was tuned and finalized in the training set using 10-fold cross-validation, and then assessed on the test set. A radiologist evaluated all MRI scans without and with the aid of ML to detect the presence of DMI. McNemars's test was employed to compare the two readings. RESULTS Of the 54 patients included, 17 had DMI. In all, 1132 features were extracted. After feature selection, the Random Forest wrapper identified the three most informative which were used for ML training. The classifier reached an accuracy of 86% and 91% and areas under the Receiver Operating Characteristic curve of 0.92 and 0.94 in the cross-validation and final testing, respectively. The radiologist performance increased from 82% to 100% when using ML (p = 0.48). CONCLUSION We proved the feasibility of a radiomics-powered ML model for DMI detection on MR T2-w images that might help radiologists to increase their performance.
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Huang Y, Zeng H, Chen L, Luo Y, Ma X, Zhao Y. Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma. Front Oncol 2021; 11:640881. [PMID: 33763374 PMCID: PMC7982462 DOI: 10.3389/fonc.2021.640881] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 01/26/2021] [Indexed: 02/05/2023] Open
Abstract
Background Clear cell renal cell carcinoma (ccRCC) is one of the most common malignancies in urinary system, and radiomics has been adopted in tumor staging and prognostic evaluation in renal carcinomas. This study aimed to integrate image features of contrast-enhanced CT and underlying genomics features to predict the overall survival (OS) of ccRCC patients. Method We extracted 107 radiomics features out of 205 patients with available CT images obtained from TCIA database and corresponding clinical and genetic information from TCGA database. LASSO-COX and SVM-RFE were employed independently as machine-learning algorithms to select prognosis-related imaging features (PRIF). Afterwards, we identified prognosis-related gene signature through WGCNA. The random forest (RF) algorithm was then applied to integrate PRIF and the genes into a combined imaging-genomics prognostic factors (IGPF) model. Furthermore, we constructed a nomogram incorporating IGPF and clinical predictors as the integrative prognostic model for ccRCC patients. Results A total of four PRIF and four genes were identified as IGPF and were represented by corresponding risk score in RF model. The integrative IGPF model presented a better prediction performance than the PRIF model alone (average AUCs for 1-, 3-, and 5-year were 0.814 vs. 0.837, 0.74 vs. 0.806, and 0.689 vs. 0.751 in test set). Clinical characteristics including gender, TNM stage and IGPF were independent risk factors. The nomogram integrating clinical predictors and IGPF provided the best net benefit among the three models. Conclusion In this study we established an integrative prognosis-related nomogram model incorporating imaging-genomic features and clinical indicators. The results indicated that IGPF may contribute to a comprehensive prognosis assessment for ccRCC patients.
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Affiliation(s)
- Yeqian Huang
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Zeng
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Linyan Chen
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yuling Luo
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China.,State Key Laboratory of Biotherapy and Cancer Center, Collaborative Innovation Center for Biotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Ye Zhao
- School of Bioscience and Technology, Chengdu Medical College, Chengdu, China
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37
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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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Binczyk F, Prazuch W, Bozek P, Polanska J. Radiomics and artificial intelligence in lung cancer screening. Transl Lung Cancer Res 2021; 10:1186-1199. [PMID: 33718055 PMCID: PMC7947422 DOI: 10.21037/tlcr-20-708] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Lung cancer is responsible for more fatalities than any other cancer worldwide, with 1.76 million associated deaths reported in 2018. The key issue in the fight against this disease is the detection and diagnosis of all pulmonary nodules at an early stage. Artificial intelligence (AI) algorithms play a vital role in the automated detection, segmentation, and computer-aided diagnosis of malignant lesions. Among the existing algorithms, radiomics and deep-learning-based types appear to show the most promise. Radiomics is a growing field related to the extraction of a set of features from an image, which allows for automated classification of medical images into a predefined group. The process comprises a series of consecutive steps including image acquisition and pre-processing, segmentation of the desired region of interest, calculation of defined features, feature engineering, and construction of the classification model. The features calculated in this process are mainly shape features, as well as first- and higher-order texture features. To date, more than 100 features have been defined, although this number varies depending on the application. The greatest challenge in radiomics is building a cross-validated model based on a selected set of calculated features known as the radiomic signature. Numerous radiomic signatures have successfully been developed; however, reproducibility and clinical validity of the results obtained constitutes a considerable challenge of modern radiomics. Deep learning algorithms are another rapidly evolving technique and are recognized as a valuable tool in the field of medical image analysis for the detection, characterization, and assessment of lesions. Such an approach involves the design of artificial neural network architecture while upholding the goal of high classification accuracy. This paper illuminates the evolution and current state of artificial intelligence methods in lung imaging and the detection and diagnosis of pulmonary nodules, with a particular emphasis on radiomics and deep learning methods.
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Affiliation(s)
- Franciszek Binczyk
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Wojciech Prazuch
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
| | - Paweł Bozek
- Department of Radiology and Radiodiagnostics, Medical University of Silesia, Katowice, Poland
| | - Joanna Polanska
- Department of Data Science and Engineering, Silesian University of Technology, Gliwice, Poland
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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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Comelli A, Dahiya N, Stefano A, Vernuccio F, Portoghese M, Cutaia G, Bruno A, Salvaggio G, Yezzi A. Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging. APPLIED SCIENCES (BASEL, SWITZERLAND) 2021; 11:782. [PMID: 33680505 PMCID: PMC7932306 DOI: 10.3390/app11020782] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Magnetic Resonance Imaging-based prostate segmentation is an essential task for adaptive radiotherapy and for radiomics studies whose purpose is to identify associations between imaging features and patient outcomes. Because manual delineation is a time-consuming task, we present three deep-learning (DL) approaches, namely UNet, efficient neural network (ENet), and efficient residual factorized convNet (ERFNet), whose aim is to tackle the fully-automated, real-time, and 3D delineation process of the prostate gland on T2-weighted MRI. While UNet is used in many biomedical image delineation applications, ENet and ERFNet are mainly applied in self-driving cars to compensate for limited hardware availability while still achieving accurate segmentation. We apply these models to a limited set of 85 manual prostate segmentations using the k-fold validation strategy and the Tversky loss function and we compare their results. We find that ENet and UNet are more accurate than ERFNet, with ENet much faster than UNet. Specifically, ENet obtains a dice similarity coefficient of 90.89% and a segmentation time of about 6 s using central processing unit (CPU) hardware to simulate real clinical conditions where graphics processing unit (GPU) is not always available. In conclusion, ENet could be efficiently applied for prostate delineation even in small image training datasets with potential benefit for patient management personalization.
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Affiliation(s)
- Albert Comelli
- Ri.MED Foundation, Via Bandiera, 11, 90133 Palermo, Italy
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Navdeep Dahiya
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
| | - Federica Vernuccio
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, 90127 Palermo, Italy
| | - Marzia Portoghese
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, 90127 Palermo, Italy
| | - Giuseppe Cutaia
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, 90127 Palermo, Italy
| | - Alberto Bruno
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, 90127 Palermo, Italy
| | - Giuseppe Salvaggio
- Dipartimento di Biomedicina, Neuroscienze e Diagnostica avanzata (BIND), University of Palermo, 90127 Palermo, Italy
| | - Anthony Yezzi
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
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Stanzione A, Creta M, Imbriaco M, La Rocca R, Capece M, Esposito F, Imbimbo C, Fusco F, Celentano G, Napolitano L, Mangiapia F, Mirone V, Longo N. Attitudes and perceptions towards multiparametric magnetic resonance imaging of the prostate: A national survey among Italian urologists. ACTA ACUST UNITED AC 2020; 92. [PMID: 33348956 DOI: 10.4081/aiua.2020.4.291] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Accepted: 09/03/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVE We aimed to assess the attitudes and perceptions towards multiparametric magnetic resonance imaging (mpMRI) of the prostate among Italian urologists. MATERIAL AND METHODS A national, web-based survey was performed. A questionnaire composed of 18 multiple choice questions was e-mailed to 941 currently active urologists, members of the Italian Society of Urology. Preserving anonymity, respondents' demographics were collected (e.g. geographic region, type of workplace, prostate procedures performed) as well as data concerning their attitudes and perceptions towards mpMRI (e.g. indications deemed appropriate, degree of confidence in mpMRI results). Data were expressed as raw numbers and percentages of survey answers. RESULTS In total, 98 responses were received (participation rate = 10.4%). Respondents mostly worked in urban areas (96%) and primarily in hospital settings (89%), while 48% of them worked in southern Italy. 97% of respondents considered mpMRI useful to detect Prostate Cancer (PCa) in patients with prior negative biopsy, 64% in biopsy-naïve patients and 60% for PCa pre-operatory staging. About half (42%) of the participants declared that mpMRI results frequently lead them to change PCa management strategy. Standardization of mpMRI acquisition and reporting was partially unsatisfactory. Reported waiting time for mpMRI scans was longer than 4 weeks for 51% of respondents. The major limitation of this survey includes the small number of participants. CONCLUSIONS Prostate mpMRI is used by Italian urologists mainly for detection and for pre-operative staging of PCa. Further improvements in terms of mpMRI availability and report standardization are required.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples.
| | - Massimiliano Creta
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples "Federico II", Naples.
| | - Roberto La Rocca
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Marco Capece
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Fabio Esposito
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples, Italy..
| | - Ciro Imbimbo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Ferdinando Fusco
- Department of Woman Child and of General and Specialist Surgery, University of Campania "Luigi Vanvitelli", Naples.
| | - Giuseppe Celentano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Luigi Napolitano
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Francesco Mangiapia
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Vincenzo Mirone
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
| | - Nicola Longo
- Department of Neurosciences, Reproductive Sciences and Odontostomatology, University of Naples "Federico II", Naples.
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Comelli A, Coronnello C, Dahiya N, Benfante V, Palmucci S, Basile A, Vancheri C, Russo G, Yezzi A, Stefano A. Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies. J Imaging 2020; 6:125. [PMID: 34460569 PMCID: PMC8321165 DOI: 10.3390/jimaging6110125] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 11/11/2020] [Accepted: 11/18/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. METHODS Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources' requirements. RESULTS E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. CONCLUSIONS We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.
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Affiliation(s)
- Albert Comelli
- Ri.MED Foundation, 90133 Palermo, Italy;
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (V.B.); (G.R.); (A.S.)
| | | | - Navdeep Dahiya
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (N.D.); (A.Y.)
| | - Viviana Benfante
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (V.B.); (G.R.); (A.S.)
| | - Stefano Palmucci
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.)
| | - Antonio Basile
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.)
| | - Carlo Vancheri
- Regional Referral Centre for Rare Lung Diseases, A.O.U. Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy;
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (V.B.); (G.R.); (A.S.)
| | - Anthony Yezzi
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (N.D.); (A.Y.)
| | - Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (V.B.); (G.R.); (A.S.)
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Delgadillo R, Ford JC, Abramowitz MC, Dal Pra A, Pollack A, Stoyanova R. The role of radiomics in prostate cancer radiotherapy. Strahlenther Onkol 2020; 196:900-912. [PMID: 32821953 PMCID: PMC7545508 DOI: 10.1007/s00066-020-01679-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 08/07/2020] [Indexed: 12/24/2022]
Abstract
"Radiomics," as it refers to the extraction and analysis of a large number of advanced quantitative radiological features from medical images using high-throughput methods, is perfectly suited as an engine for effectively sifting through the multiple series of prostate images from before, during, and after radiotherapy (RT). Multiparametric (mp)MRI, planning CT, and cone beam CT (CBCT) routinely acquired throughout RT and the radiomics pipeline are developed for extraction of thousands of variables. Radiomics data are in a format that is appropriate for building descriptive and predictive models relating image features to diagnostic, prognostic, or predictive information. Prediction of Gleason score, the histopathologic cancer grade, has been the mainstay of the radiomic efforts in prostate cancer. While Gleason score (GS) is still the best predictor of treatment outcome, there are other novel applications of quantitative imaging that are tailored to RT. In this review, we summarize the radiomics efforts and discuss several promising concepts such as delta-radiomics and radiogenomics for utilizing image features for assessment of the aggressiveness of prostate cancer and its outcome. We also discuss opportunities for quantitative imaging with the advance of instrumentation in MRI-guided therapies.
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Affiliation(s)
- Rodrigo Delgadillo
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - John C Ford
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Matthew C Abramowitz
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Alan Dal Pra
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Alan Pollack
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA
| | - Radka Stoyanova
- Department of Radiation Oncology, University of Miami Miller School of Medicine, 1121 NW 14th St, 33136, Miami, FL, USA.
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Machine Learning-Based Differentiation of Nontuberculous Mycobacteria Lung Disease and Pulmonary Tuberculosis Using CT Images. BIOMED RESEARCH INTERNATIONAL 2020; 2020:6287545. [PMID: 33062689 PMCID: PMC7545409 DOI: 10.1155/2020/6287545] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/11/2020] [Accepted: 09/22/2020] [Indexed: 11/17/2022]
Abstract
An increasing number of patients infected with nontuberculous mycobacteria (NTM) are observed worldwide. However, it is challenging to identify NTM lung diseases from pulmonary tuberculosis (PTB) due to considerable overlap in classic manifestations and clinical and radiographic characteristics. This study quantifies both cavitary and bronchiectasis regions in CT images and explores a machine learning approach for the differentiation of NTM lung diseases and PTB. It involves 116 patients and 103 quantitative features. After the selection of informative features, a linear support vector machine performs disease classification, and simultaneously, discriminative features are recognized. Experimental results indicate that bronchiectasis is relatively more informative, and two features are figured out due to promising prediction performance (area under the curve, 0.84 ± 0.06; accuracy, 0.85 ± 0.06; sensitivity, 0.88 ± 0.07; and specificity, 0.80 ± 0.12). This study provides insight into machine learning-based identification of NTM lung diseases from PTB, and more importantly, it makes early and quick diagnosis of NTM lung diseases possible that can facilitate lung disease management and treatment planning.
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Hu L, Zhou DW, Fu CX, Benkert T, Jiang CY, Li RT, Wei LM, Zhao JG. Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study. Eur Radiol 2020; 31:1760-1769. [PMID: 32935192 DOI: 10.1007/s00330-020-07227-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 07/16/2020] [Accepted: 08/26/2020] [Indexed: 01/16/2023]
Abstract
OBJECTIVES We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI. METHODS A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500 s/mm2 (f-DWIb1500), advanced zoomed DWI images with b-value = 1500 s/mm2 (z-DWIb1500), calculated zoomed DWI with b-value = 2000 s/mm2 (z-calDWIb2000), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC). Single-imaging modality radiomics signature, mp-MRI radiomics signature, and a mixed model based on mp-MRI and clinically independent risk factors were built to predict PCa probability. The diagnostic efficacy and the potential net benefits of each model were evaluated. RESULTS Both z-DWIb1500 and z-calDWIb2000 had significantly better predictive performance than f-DWIb1500 (z-DWIb1500 vs. f-DWIb1500: p = 0.048; z-calDWIb2000 vs. f-DWIb1500: p = 0.014). z-ADC had a slightly higher area under the curve (AUC) value compared with f-ADC value but was not significantly different (p = 0.127). For predicting the presence of PCa, the AUCs of clinical independent risk factors model, mp-MRI model, and mixed model were 0.81, 0.93, and 0.94 in training sets, and 0.74, 0.92, and 0.93 in validation sets, respectively. CONCLUSION Radiomics signatures based on the z-DWI technology had better diagnostic accuracy for PCa than that based on the f-DWI technology. The mixed model was better at diagnosing PCa and guiding clinical interventions for patients with suspected PCa compared with mp-MRI signatures and clinically independent risk factors. KEY POINTS • Advanced zoomed DWI technology can improve the diagnostic accuracy of radiomics signatures for PCa. • Radiomics signatures based on z-calDWIb2000 have the best diagnostic performance among individual imaging modalities. • Compared with the independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency.
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Affiliation(s)
- Lei Hu
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China
| | - Da Wei Zhou
- State Key Laboratory of Integrated Services Networks, School of Telecommunications Engineering, Xidian University, 2 South Taibai Road, Xi'an, 710071, Shaanxi, China
| | - Cai Xia Fu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Chun Yu Jiang
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China
| | - Rui Ting Li
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China
| | - Li Ming Wei
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China
| | - Jun Gong Zhao
- Department of Diagnostic and Interventional Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, No. 600, Yi Shan Road, Shanghai, 200233, China.
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Gugliandolo SG, Pepa M, Isaksson LJ, Marvaso G, Raimondi S, Botta F, Gandini S, Ciardo D, Volpe S, Riva G, Rojas DP, Zerini D, Pricolo P, Alessi S, Petralia G, Summers PE, Mistretta FA, Luzzago S, Cattani F, De Cobelli O, Cassano E, Cremonesi M, Bellomi M, Orecchia R, Jereczek-Fossa BA. MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218). Eur Radiol 2020; 31:716-728. [PMID: 32852590 DOI: 10.1007/s00330-020-07105-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 06/18/2020] [Accepted: 07/23/2020] [Indexed: 10/23/2022]
Abstract
OBJECTIVES Radiomic involves testing the associations of a large number of quantitative imaging features with clinical characteristics. Our aim was to extract a radiomic signature from axial T2-weighted (T2-W) magnetic resonance imaging (MRI) of the whole prostate able to predict oncological and radiological scores in prostate cancer (PCa). METHODS This study included 65 patients with localized PCa treated with radiotherapy (RT) between 2014 and 2018. For each patient, the T2-W MRI images were normalized with the histogram intensity scale standardization method. Features were extracted with the IBEX software. The association of each radiomic feature with risk class, T-stage, Gleason score (GS), extracapsular extension (ECE) score, and Prostate Imaging Reporting and Data System (PI-RADS v2) score was assessed by univariate and multivariate analysis. RESULTS Forty-nine out of 65 patients were eligible. Among the 1702 features extracted, 3 to 6 features with the highest predictive power were selected for each outcome. This analysis showed that texture features were the most predictive for GS, PI-RADS v2 score, and risk class; intensity features were highly associated with T-stage, ECE score, and risk class, with areas under the receiver operating characteristic curve (ROC AUC) ranging from 0.74 to 0.94. CONCLUSIONS MRI-based radiomics is a promising tool for prediction of PCa characteristics. Although a significant association was found between the selected features and all the mentioned clinical/radiological scores, further validations on larger cohorts are needed before these findings can be applied in the clinical practice. KEY POINTS • A radiomic model was used to classify PCa aggressiveness. • Radiomic analysis was performed on T2-W magnetic resonance images of the whole prostate gland. • The most predictive features belong to the texture (57%) and intensity (43%) domains.
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Affiliation(s)
| | - Matteo Pepa
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- European School of Molecular Medicine, IFOM-IEO Campus, Via Adamello, 16, 20139, Milan, Italy
| | - Giulia Marvaso
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy.
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy.
| | - Sara Raimondi
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Delia Ciardo
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Stefania Volpe
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Giulia Riva
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Clinical Department, National Center for Oncological Hadrontherapy (CNAO), Pavia, Italy
| | - Damari Patricia Rojas
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Dario Zerini
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Paola Pricolo
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Sarah Alessi
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Giuseppe Petralia
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Paul Eugene Summers
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Frnacesco Alessandro Mistretta
- Division of Urology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
| | - Stefano Luzzago
- Division of Urology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Federica Cattani
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Ottavio De Cobelli
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
- Division of Urology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Enrico Cassano
- Breast Imaging Division, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Massimo Bellomi
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
- Division of Radiology, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiotherapy, IEO, European Institute of Oncology IRCCS, Via Ripamonti 435, 20141, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Via Festa del Perdono 7, 20122, Milan, Italy
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Hiremath A, Shiradkar R, Merisaari H, Prasanna P, Ettala O, Taimen P, Aronen HJ, Boström PJ, Jambor I, Madabhushi A. Test-retest repeatability of a deep learning architecture in detecting and segmenting clinically significant prostate cancer on apparent diffusion coefficient (ADC) maps. Eur Radiol 2020; 31:379-391. [PMID: 32700021 DOI: 10.1007/s00330-020-07065-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 05/22/2020] [Accepted: 07/02/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice- and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADCm. METHODS One hundred twelve patients with prostate cancer (PCa) underwent 2 prostate MRI examinations on the same day. PCa areas were annotated using whole mount prostatectomy sections. Two U-Net-based convolutional neural networks were trained on three different ADCm b value settings for (a) slice- and (b) lesion-level detection and (c) segmentation of csPCa. Short-term test-retest repeatability was estimated using intra-class correlation coefficient (ICC(3,1)), proportionate agreement, and dice similarity coefficient (DSC). A 3-fold cross-validation was performed on training set (N = 78 patients) and evaluated for performance and repeatability on testing data (N = 34 patients). RESULTS For the three ADCm b value settings, repeatability of mean ADCm of csPCa lesions was ICC(3,1) = 0.86-0.98. Two CNNs with U-Net-based architecture demonstrated ICC(3,1) in the range of 0.80-0.83, agreement of 66-72%, and DSC of 0.68-0.72 for slice- and lesion-level detection and segmentation of csPCa. Bland-Altman plots suggest that there is no systematic bias in agreement between inter-scan ground truth segmentation repeatability and segmentation repeatability of the networks. CONCLUSIONS For the three ADCm b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility. KEY POINTS • For the three ADCm b value settings, two CNNs with U-Net-based architecture were repeatable for the problem of detection of csPCa at the slice-level. • The network repeatability in segmenting csPCa lesions is affected by inter-scan variability and ground truth segmentation repeatability and may thus improve with better inter-scan reproducibility.
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Affiliation(s)
- Amogh Hiremath
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.
| | - Rakesh Shiradkar
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA
| | - Harri Merisaari
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.,Department of Diagnostic Radiology, University of Turku, Turku, Finland
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.,Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | - Otto Ettala
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Pekka Taimen
- Institute of Biomedicine, Department of Pathology, University of Turku and Turku University Hospital, Turku, Finland
| | - Hannu J Aronen
- Medical Imaging Centre of Southwest Finland, Turku University Hospital, Turku, Finland
| | - Peter J Boström
- Department of Urology, University of Turku and Turku University Hospital, Turku, Finland
| | - Ivan Jambor
- Department of Diagnostic Radiology, University of Turku, Turku, Finland.,Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH, 44106, USA.,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA
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Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI. Neuroradiology 2020; 62:1649-1656. [PMID: 32705290 PMCID: PMC7666676 DOI: 10.1007/s00234-020-02502-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 07/17/2020] [Indexed: 12/16/2022]
Abstract
Purpose Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery, especially when using a transsphenoidal approach. Unfortunately, it is not assessable on standard qualitative MRI. Radiomic texture analysis could help in extracting mineable quantitative tissue characteristics. We aimed to assess the accuracy of texture analysis combined with machine learning in the preoperative evaluation of pituitary macroadenoma consistency in patients undergoing endoscopic endonasal surgery. Methods Data of 89 patients (68 soft and 21 fibrous macroadenomas) who underwent MRI and transsphenoidal surgery at our institution were retrospectively reviewed. After manual segmentation, radiomic texture features were extracted from original and filtered MR images. Feature stability analysis and a multistep feature selection were performed. After oversampling to balance the classes, 80% of the data was used for hyperparameter tuning via stratified 5-fold cross-validation, while a 20% hold-out set was employed for its final testing, using an Extra Trees ensemble meta-algorithm. The reference standard was based on surgical findings. Results A total of 1118 texture features were extracted, of which 741 were stable. After removal of low variance (n = 4) and highly intercorrelated (n = 625) parameters, recursive feature elimination identified a subset of 14 features. After hyperparameter tuning, the Extra Trees classifier obtained an accuracy of 93%, sensitivity of 100%, and specificity of 87%. The area under the receiver operating characteristic and precision-recall curves was 0.99. Conclusion Preoperative T2-weighted MRI texture analysis and machine learning could predict pituitary macroadenoma consistency. Electronic supplementary material The online version of this article (10.1007/s00234-020-02502-z) contains supplementary material, which is available to authorized users.
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49
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Mahjoub S, Baur ADJ, Lenk J, Lee CH, Hartenstein A, Rudolph MM, Cash H, Hamm B, Asbach P, Haas M, Penzkofer T. Optimizing size thresholds for detection of clinically significant prostate cancer on MRI: Peripheral zone cancers are smaller and more predictable than transition zone tumors. Eur J Radiol 2020; 129:109071. [PMID: 32531720 DOI: 10.1016/j.ejrad.2020.109071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 05/08/2020] [Accepted: 05/10/2020] [Indexed: 11/27/2022]
Abstract
PURPOSE To evaluate if size-based cut-offs based on MR imaging can successfully assess clinically significant prostate cancer (csPCA). The goal was to improve the currently applied size-based differentiation criterion in PI-RADS. METHODS AND MATERIALS MRIs of 293 patients who had undergone 3 T MR imaging with subsequent confirmation of prostate cancer on systematic and targeted MRI/TRUS-fusion biopsy were re-read by three radiologists. All identifiable tumors were measured on T2WI for lesions originating in the transition zone (TZ) and on DWI for lesions from the peripheral zone (PZ) and tabulated against their Gleason grade. RESULTS 309 lesions were analyzed, 213 (68.9 %) in the PZ and 96 (31.1 %) in the TZ. ROC-Analysis showed a stronger correlation between lesion size and clinically significant (defined as Gleason Grade Group ≥ 2) prostate cancer (PCa) for the PZ (AUC = 0.73) compared to the TZ (AUC = 0.63). The calculated Youden index resulted in size cut-offs of 14 mm for PZ and 21 mm for TZ tumors. CONCLUSION Size cut-offs can be used to stratify prostate cancer with different optimal size thresholds in the peripheral zone and transition zone. There was a clearer separation of clinically significant tumors in peripheral zone cancers compared to transition zone cancers. Future iterations of PI-RADS could therefore take different size-based cut-offs for peripheral zone and transition zone cancers into account.
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Affiliation(s)
- Samy Mahjoub
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Department of Urology, Cologne University Hospital, Cologne, Germany.
| | - Alexander D J Baur
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Julian Lenk
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Chau Hung Lee
- Department of Radiology, Tan Tock Seng Hospital, Singapore
| | - Alexander Hartenstein
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Madhuri M Rudolph
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Hannes Cash
- Department of Urology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Bernd Hamm
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Patrick Asbach
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Matthias Haas
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany
| | - Tobias Penzkofer
- Department of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin and Berlin Institute of Health, Berlin, Germany; Berlin Institute of Health (BIH), Anna-Louisa-Karsch 2, 10178 Berlin, Germany.
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50
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Stefano A, Gioè M, Russo G, Palmucci S, Torrisi SE, Bignardi S, Basile A, Comelli A, Benfante V, Sambataro G, Falsaperla D, Torcitto AG, Attanasio M, Yezzi A, Vancheri C. Performance of Radiomics Features in the Quantification of Idiopathic Pulmonary Fibrosis from HRCT. Diagnostics (Basel) 2020; 10:E306. [PMID: 32429182 PMCID: PMC7277964 DOI: 10.3390/diagnostics10050306] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 05/10/2020] [Accepted: 05/13/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Our study assesses the diagnostic value of different features extracted from high resolution computed tomography (HRCT) images of patients with idiopathic pulmonary fibrosis. These features are investigated over a range of HRCT lung volume measurements (in Hounsfield Units) for which no prior study has yet been published. In particular, we provide a comparison of their diagnostic value at different Hounsfield Unit (HU) thresholds, including corresponding pulmonary functional tests. METHODS We consider thirty-two patients retrospectively for whom both HRCT examinations and spirometry tests were available. First, we analyse the HRCT histogram to extract quantitative lung fibrosis features. Next, we evaluate the relationship between pulmonary function and the HRCT features at selected HU thresholds, namely -200 HU, 0 HU, and +200 HU. We model the relationship using a Poisson approximation to identify the measure with the highest log-likelihood. RESULTS Our Poisson models reveal no difference at the -200 and 0 HU thresholds. However, inferential conclusions change at the +200 HU threshold. Among the HRCT features considered, the percentage of normally attenuated lung at -200 HU shows the most significant diagnostic utility. CONCLUSIONS The percentage of normally attenuated lung can be used together with qualitative HRCT assessment and pulmonary function tests to enhance the idiopathic pulmonary fibrosis (IPF) diagnostic process.
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Affiliation(s)
- Alessandro Stefano
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (A.S.); (A.C.); (V.B.)
| | - Mauro Gioè
- Department of Economics, Business, and Statistics (DSEAS), University of Palermo, 90133 Palermo, Italy; (M.G.); (M.A.)
| | - Giorgio Russo
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (A.S.); (A.C.); (V.B.)
| | - Stefano Palmucci
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.); (G.S.); (D.F.); (A.G.T.)
| | - Sebastiano Emanuele Torrisi
- Regional Referral Centre for Rare Lung Diseases, A.O.U. Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy; (S.E.T.); (C.V.)
| | - Samuel Bignardi
- Laboratory of Computational Computer Vision (LCCV), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.B.); (A.Y.)
| | - Antonio Basile
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.); (G.S.); (D.F.); (A.G.T.)
| | - Albert Comelli
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (A.S.); (A.C.); (V.B.)
- Ri.Med Foundation, 90133 Palermo, Italy
| | - Viviana Benfante
- Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy; (A.S.); (A.C.); (V.B.)
| | - Gianluca Sambataro
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.); (G.S.); (D.F.); (A.G.T.)
- Artroreuma S.R.L., Rheumatology Outpatient Clinic Associated with the National Health System, 95030 Mascalucia (Catania), Italy
| | - Daniele Falsaperla
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.); (G.S.); (D.F.); (A.G.T.)
| | - Alfredo Gaetano Torcitto
- Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy; (S.P.); (A.B.); (G.S.); (D.F.); (A.G.T.)
| | - Massimo Attanasio
- Department of Economics, Business, and Statistics (DSEAS), University of Palermo, 90133 Palermo, Italy; (M.G.); (M.A.)
| | - Anthony Yezzi
- Laboratory of Computational Computer Vision (LCCV), School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; (S.B.); (A.Y.)
| | - Carlo Vancheri
- Regional Referral Centre for Rare Lung Diseases, A.O.U. Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy; (S.E.T.); (C.V.)
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