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Valeri A, Nguyen TA. Research on texture images and radiomics in urology: a review of urological MR imaging applications. Curr Opin Urol 2023; 33:428-436. [PMID: 37727910 DOI: 10.1097/mou.0000000000001131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/21/2023]
Abstract
PURPOSE OF REVIEW Tumor volume and heterogenicity are associated with diagnosis and prognosis of urological cancers, and assessed by conventional imaging. Quantitative imaging, Radiomics, using advanced mathematical analysis may contain information imperceptible to the human eye, and may identify imaging-based biomarkers, a new field of research for individualized medicine. This review summarizes the recent literature on radiomics in kidney and prostate cancers and the future perspectives. RECENT FINDINGS Radiomics studies have been developed and showed promising results in diagnosis, in characterization, prognosis, treatment planning and recurrence prediction in kidney tumors and prostate cancer, but its use in guiding clinical decision-making remains limited at present due to several limitations including lack of external validations in most studies, lack of prospective studies and technical standardization. SUMMARY Future challenges, besides developing prospective and validated studies, include automated segmentation using artificial intelligence deep learning networks and hybrid radiomics integrating clinical data, combining imaging modalities and genomic features. It is anticipated that these improvements may allow identify these noninvasive, imaging-based biomarkers, to enhance precise diagnosis, improve decision-making and guide tailored treatment.
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Affiliation(s)
- Antoine Valeri
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
- CeRePP, Paris, France
| | - Truong An Nguyen
- Urology Department, CHU Brest
- Faculté de Médecine et des Sciences de la Santé, Université de Brest
- LaTIM, INSERM, UMR 1101, CHU Brest, Brest
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Zhou Y, Yuan J, Xue C, Poon DMC, Yang B, Yu SK, Cheung KY. A pilot study of MRI radiomics for high-risk prostate cancer stratification in 1.5 T MR-guided radiotherapy. Magn Reson Med 2023; 89:2088-2099. [PMID: 36572990 DOI: 10.1002/mrm.29564] [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: 08/30/2022] [Revised: 11/09/2022] [Accepted: 12/09/2022] [Indexed: 12/28/2022]
Abstract
PURPOSE To investigate the potential value of MRI radiomics obtained from a 1.5 T MRI-guided linear accelerator (MR-LINAC) for D'Amico high-risk prostate cancer (PC) classification in MR-guided radiotherapy (MRgRT). METHODS One hundred seventy-six consecutive PC patients underwent 1.5 T MRgRT treatment were retrospectively enrolled. Each patient received one or two pretreatment T2 -weighted MRI scans on a 1.5 T MR-LINAC. The endpoint was to differentiate high-risk from low/intermediate-risk PC based on D'Amico criteria using MRI-radiomics. Totally 1023 features were extracted from clinical target volume (CTV) and planning target volume (PTV). Intraclass correlation coefficient of scan-rescan repeatability, feature correlation, and recursive feature elimination were used for feature dimension reduction. Least absolute shrinkage and selection operator regression was employed for model construction. Receiver operating characteristic area under the curve (AUC) analysis was used for model performance assessment in both training and testing data. RESULTS One hundred and eleven patients fulfilled all criteria were finally included: 76 for training and 35 for testing. The constructed MRI-radiomics models extracted from CTV and PTV achieved the AUC of 0.812 and 0.867 in the training data, without significant difference (P = 0.083). The model performances remained in the testing. The sensitivity, specificity, and accuracy were 85.71%, 64.29%, and 77.14% for the PTV-based model; and 71.43%, 71.43%, and 71.43% for the CTV-based model. The corresponding AUCs were 0.718 and 0.750 (P = 0.091) for CTV- and PTV-based models. CONCLUSION MRI-radiomics obtained from a 1.5 T MR-LINAC showed promising results in D'Amico high-risk PC stratification, potentially helpful for the future PC MRgRT. Prospective studies with larger sample sizes and external validation are warranted for further verification.
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Affiliation(s)
- Yihang Zhou
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Jing Yuan
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Cindy Xue
- Research Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Darren M C Poon
- Comprehensive Oncology Center, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Bin Yang
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Siu Ki Yu
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
| | - Kin Yin Cheung
- Medical Physics Department, Hong Kong Sanatorium & Hospital, Hong Kong, People's Republic of China
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Stanzione A, Ponsiglione A, Alessandrino F, Brembilla G, Imbriaco M. Beyond diagnosis: is there a role for radiomics in prostate cancer management? Eur Radiol Exp 2023; 7:13. [PMID: 36907973 PMCID: PMC10008761 DOI: 10.1186/s41747-023-00321-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/05/2023] [Indexed: 03/13/2023] Open
Abstract
The role of imaging in pretreatment staging and management of prostate cancer (PCa) is constantly evolving. In the last decade, there has been an ever-growing interest in radiomics as an image analysis approach able to extract objective quantitative features that are missed by human eye. However, most of PCa radiomics studies have been focused on cancer detection and characterisation. With this narrative review we aimed to provide a synopsis of the recently proposed potential applications of radiomics for PCa with a management-based approach, focusing on primary treatments with curative intent and active surveillance as well as highlighting on recurrent disease after primary treatment. Current evidence is encouraging, with radiomics and artificial intelligence appearing as feasible tools to aid physicians in planning PCa management. However, the lack of external independent datasets for validation and prospectively designed studies casts a shadow on the reliability and generalisability of radiomics models, delaying their translation into clinical practice.Key points• Artificial intelligence solutions have been proposed to streamline prostate cancer radiotherapy planning.• Radiomics models could improve risk assessment for radical prostatectomy patient selection.• Delta-radiomics appears promising for the management of patients under active surveillance.• Radiomics might outperform current nomograms for prostate cancer recurrence risk assessment.• Reproducibility of results, methodological and ethical issues must still be faced before clinical implementation.
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Affiliation(s)
- Arnaldo Stanzione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Andrea Ponsiglione
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy.
| | | | - Giorgio Brembilla
- Department of Radiology, IRCCS San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Massimo Imbriaco
- Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
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Zhang Y, Li W, Zhang Z, Xue Y, Liu YL, Nie K, Su MY, Ye Q. Differential diagnosis of prostate cancer and benign prostatic hyperplasia based on DCE-MRI using bi-directional CLSTM deep learning and radiomics. Med Biol Eng Comput 2023; 61:757-771. [PMID: 36598674 PMCID: PMC10548872 DOI: 10.1007/s11517-022-02759-x] [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: 05/27/2022] [Accepted: 12/22/2022] [Indexed: 01/05/2023]
Abstract
Dynamic contrast-enhanced MRI (DCE-MRI) is routinely included in the prostate MRI protocol for a long time; its role has been questioned. It provides rich spatial and temporal information. However, the contained information cannot be fully extracted in radiologists' visual evaluation. More sophisticated computer algorithms are needed to extract the higher-order information. The purpose of this study was to apply a new deep learning algorithm, the bi-directional convolutional long short-term memory (CLSTM) network, and the radiomics analysis for differential diagnosis of PCa and benign prostatic hyperplasia (BPH). To systematically investigate the optimal amount of peritumoral tissue for improving diagnosis, a total of 9 ROIs were delineated by using 3 different methods. The results showed that bi-directional CLSTM with ± 20% region growing peritumoral ROI achieved the mean AUC of 0.89, better than the mean AUC of 0.84 by using the tumor alone without any peritumoral tissue (p = 0.25, not significant). For all 9 ROIs, deep learning had higher AUC than radiomics, but only reaching the significant difference for ± 20% region growing peritumoral ROI (0.89 vs. 0.79, p = 0.04). In conclusion, the kinetic information extracted from DCE-MRI using bi-directional CLSTM may provide helpful supplementary information for diagnosis of PCa.
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Affiliation(s)
- Yang Zhang
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA
| | - Weikang Li
- Department of Radiology, The Children's Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhao Zhang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yingnan Xue
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Yan-Lin Liu
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA
| | - Ke Nie
- Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Robert Wood Johnson Medical School, New Brunswick, NJ, USA
| | - Min-Ying Su
- Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697, USA.
| | - Qiong Ye
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, 350 Shushanhu Road, Hefei, 230031, Anhui, People's Republic of China.
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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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Zhu X, Shen J, Zhang H, Wang X, Zhang H, Yu J, Zhang Q, Song D, Guo L, Zhang D, Zhu R, Wu J. A Novel Combined Nomogram Model for Predicting the Pathological Complete Response to Neoadjuvant Chemotherapy in Invasive Breast Carcinoma of No Specific Type: Real-World Study. Front Oncol 2022; 12:916526. [PMID: 35734603 PMCID: PMC9207207 DOI: 10.3389/fonc.2022.916526] [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: 04/09/2022] [Accepted: 05/02/2022] [Indexed: 12/03/2022] Open
Abstract
Objective To explore the value of a predictive model combining the multiparametric magnetic resonance imaging (mpMRI) radiomics score (RAD-score), clinicopathologic features, and morphologic features for the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in invasive breast carcinoma of no specific type (IBC-NST). Methods We enrolled, retrospectively and consecutively, 206 women with IBC-NST who underwent surgery after NAC and obtained pathological results from August 2018 to October 2021. Four RAD-scores were constructed for predicting the pCR based on fat-suppression T2-weighted imaging (FS-T2WI), diffusion-weighted imaging (DWI), contrast-enhanced T1-weighted imaging (T1WI+C) and their combination, which was called mpMRI. The best RAD-score was combined with clinicopathologic and morphologic features to establish a nomogram model through binary logistic regression. The predictive performance of the nomogram was evaluated using the area under receiver operator characteristic (ROC) curve (AUC) and calibration curve. The clinical net benefit of the model was evaluated using decision curve analysis (DCA). Results The mpMRI RAD-score had the highest diagnostic performance, with AUC of 0.848 among the four RAD-scores. T stage, human epidermal growth factor receptor-2 (HER2) status, RAD-score, and roundness were independent factors for predicting the pCR (P < 0.05 for all). The combined nomogram model based on these factors achieved AUCs of 0.930 and 0.895 in the training cohort and validation cohort, respectively, higher than other models (P < 0.05 for all). The calibration curve showed that the predicted probabilities of the nomogram were in good agreement with the actual probabilities, and DCA indicated that it provided more net benefit than the treat-none or treat-all scheme by decision curve analysis in both training and validation datasets. Conclusion The combined nomogram model based on the mpMRI RAD-score combined with clinicopathologic and morphologic features may improve the predictive performance for the pCR of NAC in patients with IBC-NST.
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Affiliation(s)
- Xuelin Zhu
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.,Department of Ultrasound, Qingzhou People's Hospital, Weifang, China
| | - Jing Shen
- Graduate School, Tianjin Medical University, Tianjin, China.,Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Huanlei Zhang
- Department of Radiology, Yidu Central Hospital of Weifang, Weifang, China
| | - Xiulin Wang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.,School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China
| | - Huihui Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jing Yu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Qing Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Dongdong Song
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Liping Guo
- Department of Ultrasound, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Dianlong Zhang
- Department of Breast and Thyroid Surgery, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Ruiping Zhu
- Department of Pathology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China
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Liu K, Yan S, Liu Z, Wang D, Yang Q, Jiang X, Chen L, Tang H. New anti-tumor strategy based on acid-triggered self-destructive and near-infrared laser light responses of nano-biocatalysts integrating starvation–chemo–photothermal therapies. Cancer Nanotechnol 2022. [DOI: 10.1186/s12645-022-00117-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Abstract
Background
Inherent limitations of single cancer therapy are overcome by multi-therapy modality, which integrates characteristics of each therapeutic modality and material chemistry. The multi-modal method has the potential for becoming one of the next generation options for cancer treatments. Photothermal therapy (PTT) is an efficient, non-invasive treatment method that can be used on various cancer types. We propose an acid-triggered self-destructing nano-biocatalyst integrated starvation/chemical/photothermal triple therapy that is based on design principles and biomedical applications of GOx cancer treatment methods.
Methods
Scanning electron microscopy (SEM), transmission electron microscopy (TEM), dynamic light scattering (DLS), and zeta potentials were used to analyze the physical as well as chemical properties of MoS2@DOX/GOx@MnO2 (M@D/G@M). Further, Fourier transform infra-red (FTIR), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) were used to assess the compositions of the nanocatalysts. The biological effects of M@D/G@M on cells were studied in vitro by inverted fluorescence microscopy, confocal laser scanning microscopy (CLSM), flow cytometry, CCK-8 test, and hemolysis test. Treatment effects of the nanocatalysts were evaluated in MHCC-97H tumor BALB/c mice, whose body weights, tumor local temperature, tumor volumes, and tumor histological changes were evaluated.
Results
There was a high DOX encapsulation efficiency of M@D/G@M (90.233%). The photothermal conversion efficiency (η) of M@D/G@M is 25.2%, and its oxygen production within 5 min reached 27.5 mg L−1. Cell internalization analysis showed that within 4 h, M@D/G@M was almost completely absorbed by HepG2 cells. Further, the highest red fluorescence and apoptosis effects of dead cells (59.07% apoptosis) as well as the lowest tumor volume index of mice (0.2862%) were observed in the M@D/G@M + pH6.0 + NIR treatment group.
Conclusions
Our findings inform the development and applications of multi-modal methods in tumor therapy.
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Yao K, Li S, Liu Q, Wu F. The emerging role of cross-resistance between taxanes and AR-targeting therapy in metastatic prostate cancer. JOURNAL OF CLINICAL UROLOGY 2022. [DOI: 10.1177/20514158221088689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: To date, the number of prostate cancer ranked first among newly diagnosed malignant tumors in men from multiple countries. Localized prostate cancer could be controlled by curative therapy. However, for patients with metastatic prostate cancer (mPC), the prognosis is poor. As among first-line treatments of systemic therapies for mPC, docetaxel and androgen receptor (AR)-targeted therapies have been widely used. However, mPC patients inevitably developed resistance to the current therapy. More importantly, there is a cross-resistance between docetaxel-based chemotherapy and AR-targeting therapy during the treatment process, which could impair the overall survival benefits without proper administration. Objective: Therefore, it is urgent to elucidate the mechanism of cross-resistance and explore the optimal sequential strategy. Methods: Here, in this review, we systematically reviewed and summarised the updated literature on clinical evidence and mechanistic research of treatment resistance in mPC. Results: Emerging evidence indicated that AR splice variants, AR overexpression or mutations, AR nuclear translocation, as well as AR signaling reactivation collectively contributed to the cross-resistance. With the current understanding of cross-resistance, multiple solutions are promising for improving the benefits, including refining the sequencing of available therapies for mPC, in combination with potential targeted inhibitors or immune checkpoint inhibitors. Further studies are needed to explore the combination of emerging strategies and eventually control the progression of prostate cancer. Conclusions: This review defined the mutual and unique resistant mechanism of these treatments, which might help to focus and accelerate therapeutic research that may ultimately improve clinical outcomes for patients with prostate cancer. Level of evidence: Not applicable
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Affiliation(s)
- Kang Yao
- Department of Urology, Shandong Provincial Qianfoshan Hospital, Shandong University, P.R. China
| | - Shun Li
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, P.R. China
| | - Qingyong Liu
- Department of Urology, Shandong Provincial Qianfoshan Hospital, Shandong University, P.R. China
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, P.R. China
| | - Fei Wu
- Department of Urology, The First Affiliated Hospital of Shandong First Medical University, P.R. China
- Shandong Provincial Key Laboratory of Radiation Oncology, Cancer Research Center, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, P.R. China
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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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