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Zhang Y, Li Z, Gao C, Zhang L, Huang Y, Qu H, Shu C, Wei Y, Xu M, Cui F. Radiomic nomogram based on bi-parametric magnetic resonance imaging to predict the International Society of Urological Pathology grading ≥ 3 prostate cancer: a multicenter study. Clin Radiol 2024; 79:e985-e993. [PMID: 38763807 DOI: 10.1016/j.crad.2024.04.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 01/29/2024] [Accepted: 04/18/2024] [Indexed: 05/21/2024]
Abstract
PURPOSE To create a reliable radiomic nomogram for the prediction of the International Society of Urological Pathology (ISUP) grading ≥ 3 prostate cancer (PCa) patients. METHODS patients with verified PCa were obtained from three different hospitals. The patients were divided into training, internal validation, and two external validation groups. A radiomic signature (rad-score) extracted from T2WI, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps were constructed in the training cohort. Eight clinical features were performed to develop a clinical model using univariate and multivariate logistic regression. The combined model incorporated the radiomic signature and clinical model. The model's performance was assessed by the receiver operating characteristic (ROC) curve. RESULTS Rad-score, magnetic resonance imaging T-stage, and ADC value were significant predictors of ISUP ≥ 3 PCa. A nomogram of these three factors was shown to have greater diagnostic accuracy than using only the radiomic signature or clinical model alone. The area under the ROC curve was 0.85, 0.88, 0.81, 0.81 for the training, internal, and two external validation cohorts, respectively. In the stratified analysis based on the MR scanner model, the area under the ROC curve of predicting ISUP ≥ 3 PCa for GE, Siemens, and combined groups were 0.84, 0.83, and 0.84, respectively, in the combined training group and an internal validation group. CONCLUSIONS The proposed nomogram has the potential to predict the differentiation degree of ISUP PCa patients.
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Affiliation(s)
- Y Zhang
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
| | - Z Li
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - C Gao
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China
| | - L Zhang
- Department of Radiology, Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Y Huang
- Department of Urology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - H Qu
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - C Shu
- Department of Pathology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
| | - Y Wei
- Advanced Analytics, Global Medical Service, GE Healthcare, Hangzhou, 310007, China
| | - M Xu
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
| | - F Cui
- Department of Radiology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.
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Zhang XF, Wu HY, Liang XW, Chen JL, Li J, Zhang S, Liu Z. Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma. BMC Womens Health 2024; 24:182. [PMID: 38504245 PMCID: PMC10949581 DOI: 10.1186/s12905-024-03001-6] [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: 02/27/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).
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Grants
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20211800500322 CHINA,Guangdong Sci-tech Commissoner
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20231800935742 CHINA,Dongguan City Social Science and Technology Development (Key) Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
- 20221800902092 CHINA,Dongguan City Social Science and Technology Development Project
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Affiliation(s)
- Xue-Fang Zhang
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Hong-Yuan Wu
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Xu-Wei Liang
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Jia-Luo Chen
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China
| | - Jianpeng Li
- Radiology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
| | - Shihao Zhang
- Pathology Department, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China
| | - Zhigang Liu
- Radiotherapy department, Cancer center, The Tenth Affiliated Hospital, Southern Medical University(Dongguan People's Hospital), No.78 Wandaonan Road, Dongguan, 523059, Guangdong, People's Republic of China.
- Dongguan Key Laboratory of Precision Diagnosis and Treatment for Tumors, Dongguan, 523059, Guangdong, People's Republic of China.
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Wei Z, Liu H, Xv Y, Liao F, He Q, Xie Y, Lv F, Jiang Q, Xiao M. Development and validation of a CT-based deep learning radiomics nomogram to predict muscle invasion in bladder cancer. Heliyon 2024; 10:e24878. [PMID: 38304824 PMCID: PMC10831750 DOI: 10.1016/j.heliyon.2024.e24878] [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: 12/21/2023] [Revised: 01/16/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
Objective This study aimed to develop a nomogram combining CT-based handcrafted radiomics and deep learning (DL) features to preoperatively predict muscle invasion in bladder cancer (BCa) with multi-center validation. Methods In this retrospective study, 323 patients underwent radical cystectomy with pathologically confirmed BCa were enrolled and randomly divided into the training cohort (n = 226) and internal validation cohort (n = 97). And fifty-two patients from another independent medical center were enrolled as an independent external validation cohort. Handcrafted radiomics and DL features were constructed from preoperative nephrographic phase CT images. Least absolute shrinkage and selection operator (LASSO) regression was used to identify the most discriminative features in train cohort. Multivariate logistic regression was used to develop the predictive model and a deep learning radiomics nomogram (DLRN) was constructed. The predictive performance of models was evaluated by area under the curves (AUC) in the three cohorts. The calibration and clinical usefulness of DLRN were estimated by calibration curve and decision curve analysis. Results The nomogram that incorporated radiomics signature and DL signature demonstrated satisfactory predictive performance for differentiating non-muscle invasive bladder cancer (NMIBC) from muscle invasive bladder cancer (MIBC), with an AUC of 0.884 (95 % CI: 0.813-0.953) in internal validation cohort and 0.862 (95 % CI: 0.756-0.968) in external validation cohort, respectively. Decision curve analysis confirmed the clinical usefulness of the nomogram. Conclusions A CT-based deep learning radiomics nomogram exhibited a promising performance for preoperative prediction of muscle invasion in bladder cancer, and may be helpful in the clinical decision-making process.
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Affiliation(s)
- Zongjie Wei
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Huayun Liu
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yingjie Xv
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fangtong Liao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Quanhao He
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongpeng Xie
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fajin Lv
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qing Jiang
- Department of Urology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Mingzhao Xiao
- Department of Urology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Qiu Y, Liu YF, Shu X, Qiao XF, Ai GY, He XJ. Peritumoral Radiomics Strategy Based on Ensemble Learning for the Prediction of Gleason Grade Group of Prostate Cancer. Acad Radiol 2023; 30 Suppl 1:S1-S13. [PMID: 37393175 DOI: 10.1016/j.acra.2023.06.011] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/14/2023] [Accepted: 06/15/2023] [Indexed: 07/03/2023]
Abstract
RATIONALE AND OBJECTIVES To develop and evaluate a peritumoral radiomic-based machine learning model to differentiate low-Gleason grade group (L-GGG) and high-GGG (H-GGG) prostate lesions. MATERIALS AND METHODS In this retrospective study, a total of 175 patients with prostate cancer (PCa) confirmed by puncture biopsy were recruited and included 59 patients with L-GGG and 116 patients with H-GGG. The original PCa regions of interest (ROIs) were delineated on T2-weighted (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) maps, and then centra-tumoral and peritumoral ROIs were defined. Features were meticulously extracted from each ROI to establish radiomics models, employing distinct sequence datasets. Peritumoral radiomics models were specifically developed for both the peripheral zone (PZ) and transitional zone (TZ), utilizing dedicated PZ and TZ datasets, respectively. The performances of the models were evaluated by using the receiver operating characteristic (ROC) curve and precision-recall curve. RESULTS The classification model with combined peritumoral features based on T2 + DWI + ADC sequence dataset demonstrated superior performance compared to the original tumor and centra-tumoral classification models. It achieved an area under the ROC curve (AUC) of 0.850 [95% confidence interval, 0.849, 0.860] and an average accuracy of 0.950. The combined peritumoral model outperformed the regional peritumoral models with AUC of 0.85 versus 0.75 for PZ lesions and 0.88 versus 0.69 for TZ lesions, respectively. The peritumoral classification models exhibit greater efficacy in predicting PZ lesions as opposed to TZ lesions. CONCLUSION The peritumoral radiomics features showed excellent performance in predicting GGG in PCa patients and might be a valuable addition to the non-invasive assessment of PCa aggressiveness.
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Affiliation(s)
- Yang Qiu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Yun-Fan Liu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xin Shu
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiao-Feng Qiao
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Guang-Yong Ai
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
| | - Xiao-Jing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
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Chiacchio G, Castellani D, Nedbal C, De Stefano V, Brocca C, Tramanzoli P, Galosi AB, Donalisio da Silva R, Teoh JYC, Tiong HY, Naik N, Somani BK, Merseburger AS, Gauhar V. Radiomics vs radiologist in prostate cancer. Results from a systematic review. World J Urol 2023; 41:709-724. [PMID: 36867239 DOI: 10.1007/s00345-023-04305-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 01/20/2023] [Indexed: 03/04/2023] Open
Abstract
PURPOSE Radiomics in uro-oncology is a rapidly evolving science proving to be a novel approach for optimizing the analysis of massive data from medical images to provide auxiliary guidance in clinical issues. This scoping review aimed to identify key aspects wherein radiomics can potentially improve the accuracy of diagnosis, staging, and extraprostatic extension in prostate cancer (PCa). METHODS The literature search was performed on June 2022 using PubMed, Embase, and Cochrane Central Controlled Register of Trials. Studies were included if radiomics were compared with radiological reports only. RESULTS Seventeen papers were included. The combination of PIRADS and radiomics score models improves the PIRADS score reporting of 2 and 3 lesions even in the peripheral zone. Multiparametric MRI-based radiomics models suggest that by simply omitting diffusion contrast enhancement imaging in radiomics models can simplify the process of analysis of clinically significant PCa by PIRADS. Radiomics features correlated with the Gleason grade with excellent discriminative ability. Radiomics has higher accuracy in predicting not only the presence but also the side of extraprostatic extension. CONCLUSIONS Radiomics research on PCa mainly uses MRI as an imaging modality and is focused on diagnosis and risk stratification and has the best future possibility of improving PIRADS reporting. Radiomics has established its superiority over radiologist-reported outcomes but the variability has to be taken into consideration before translating it to clinical practice.
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Affiliation(s)
- Giuseppe Chiacchio
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Daniele Castellani
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy.
| | - Carlotta Nedbal
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Virgilio De Stefano
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Carlo Brocca
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Pietro Tramanzoli
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | - Andrea Benedetto Galosi
- Urology Unit, Azienda Ospedaliero-Universitaria Ospedali Riuniti di Ancona, Università Politecnica delle Marche, Via Conca 71, 60126, Ancona, Italy
| | | | - Jeremy Yuen-Chun Teoh
- Department of Surgery, S.H.Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ho Yee Tiong
- Department of Urology, National University Hospital, Singapore, Singapore
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Bhaskar K Somani
- Department of Urology, University Hospitals Southampton, NHS Trust, Southampton, UK
| | - Axel S Merseburger
- Clinic of Urology, University Hospital Schleswig-Holstein, Lübeck, Germany
| | - Vineet Gauhar
- Department of Urology, Ng Teng Fong General Hospital, Singapore, Singapore
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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: 3] [Impact Index Per Article: 3.0] [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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Huang G, Song W, Zhang Y, Ren B, Lv Y, Liu K. Prognostic nomogram for cancer-specific survival in patients with intrahepatic cholangiocarcinoma after hepatectomy: A population study of 919 patients. Front Surg 2023; 9:1025521. [PMID: 36684170 PMCID: PMC9852639 DOI: 10.3389/fsurg.2022.1025521] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 11/21/2022] [Indexed: 01/09/2023] Open
Abstract
Background and Aims Intrahepatic cholangiocarcinoma has an increasing global incidence and mortality rate. Hepatectomy is still the most effective curative treatment for patients with ICC, but the prognosis of patients with ICC is still poor even after curative resection. This study aimed to incorporate important factors obtained from SEER database to construct and validate a nomogram for predicting the cancer-specific survival of patients with ICC after hepatectomy. Methods We obtained patient data from SEER database. The nomogram was constructed base on six prognostic factors for predicting CSS rates in ICC patients. The nomogram was validated by C-index, ROC curve and calibration curves. Results A total of 919 patients with ICC after hepatectomy between 2000 and 2018 were included in this study. A nomogram based on six independent prognostic factors (Black race, AJCC T, AJCC N, AJCC M, chemotherapy and PLNR ≥ 0.15) was developed for the prediction of CSS at 3 and 5 years. The C-index of the nomogram and AJCC stage system were 0.709 and 0.657 in the training cohort respectively. The 3- and 5-year AUCs of nomogram were 0.744 and 0.75 in the training cohort. The calibration plots indicated that there was good agreement between the actual observations and predictions. Conclusions In conclusion, we constructed and validated a nomogram for predicting the 3- and 5-year CSS in ICC patients after hepatectomy. We have confirmed the precise calibration and acceptable discrimination power of our nomogram. The predictive power of this nomogram may be improved by considering other potential important factors and also by external validation.
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Affiliation(s)
- Gaobo Huang
- National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Oncology, Xi'an No.3 Hospital, Xi'an, China
| | - Weilun Song
- National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yanchao Zhang
- National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Bingyi Ren
- National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Yi Lv
- National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Kang Liu
- National Local Joint Engineering Research Center for Precision Surgery and Regenerative Medicine, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China,Correspondence: Kang Liu
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8
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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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Lu Y, Li B, Huang H, Leng Q, Wang Q, Zhong R, Huang Y, Li C, Yuan R, Zhang Y. Biparametric MRI-based radiomics classifiers for the detection of prostate cancer in patients with PSA serum levels of 4∼10 ng/mL. Front Oncol 2022; 12:1020317. [PMID: 36582803 PMCID: PMC9793773 DOI: 10.3389/fonc.2022.1020317] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/21/2022] [Indexed: 12/07/2022] Open
Abstract
Purpose To investigate the predictive performance of the combined model by integrating clinical variables and radiomic features for the accurate detection of prostate cancer (PCa) in patients with prostate-specific antigen (PSA) serum levels of 4-10 ng/mL. Methods A retrospective study of 136 males (mean age, 67.3 ± 8.4 years) with Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 category ≤3 lesions and PSA serum levels of 4-10 ng/mL were performed. All patients underwent multiparametric MRI at 3.0T and transrectal ultrasound-guided systematic prostate biopsy in their clinical workup. Radiomic features were extracted from axial T2-weighted images (T2WI) and apparent diffusion coefficient (ADC) maps of each patient using PyRadiomics. Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were implemented to identify the most significant radiomic features. Independent clinic-radiological factors were identified via univariate and multivariate regression analyses. Seven machine-learning algorithms were compared to construct a single-layered radiomic score (ie, radscore) and multivariate regression analysis was applied to construct the fusion radscore. Finally, the radiomic nomogram was further developed by integrating useful clinic-radiological factors and fusion radscore using multivariate regression analysis. The discriminative power of the nomogram was evaluated by area under the curve (AUC), DeLong test, calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC). Results The transitional zone-specific antigen density was identified as the only independent clinic-radiological factor, which yielded an AUC of 0.592 (95% confidence interval [CI]: 0.527-0.657). The ADC radscore based on six features and Naive Bayes achieved an AUC of 0.779 (95%CI: 0.730-0.828); the T2WI radscore based on 13 features and Support Vector Machine yielded an AUC of 0.808 (95%CI: 0.761-0.855). The fusion radscore obtained an improved AUC of 0.844 (95%CI: 0.801-0.887), which was higher than the single-layered radscores (both P<0.05). The radiomic nomogram achieved the highest value among all models (all P<0.05), with an AUC of 0.872 (95%CI: 0.835-0.909). Calibration curve showed good agreement and DCA together with CIC confirmed the clinical benefits of the radiomic nomogram. Conclusion The radiomic nomogram holds the potential for accurate and noninvasive identification of PCa in patients with PI-RADS ≤3 lesions and PSA of 4-10 ng/mL, which could reduce unnecessary biopsy.
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Affiliation(s)
- Yangbai Lu
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Binfei Li
- Department of Anesthesiology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Hongxing Huang
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Qu Leng
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Qiang Wang
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Rui Zhong
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Yaqiang Huang
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Canyong Li
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Runqiang Yuan
- Department of Urology, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
| | - Yongxin Zhang
- Department of Magnetic Resonance Imaging, Zhongshan City People’s Hospital, Zhongshan, Guangdong, China
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Wang K, Chen P, Feng B, Tu J, Hu Z, Zhang M, Yang J, Zhan Y, Yao J, Xu D. Machine learning prediction of prostate cancer from transrectal ultrasound video clips. Front Oncol 2022; 12:948662. [PMID: 36091110 PMCID: PMC9459141 DOI: 10.3389/fonc.2022.948662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 08/08/2022] [Indexed: 11/14/2022] Open
Abstract
Objective To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI). Methods We systematically collated data from 501 patients—276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model. We extracted 851 features from ultrasound video clips. After dimensionality reduction with the least absolute shrinkage and selection operator (LASSO) regression, 14 features were finally selected and the support vector machine (SVM) and random forest (RF) algorithms were used to establish radiomics models based on those features. In addition, we creatively proposed a machine learning models aided diagnosis algorithm (MLAD) composed of SVM, RF, and radiologists’ diagnosis based on MRI to evaluate the performance of ML models in computer-aided diagnosis (CAD). We evaluated the area under the curve (AUC) as well as the sensitivity, specificity, and precision of the ML models and radiologists’ diagnosis based on MRI by employing receiver operator characteristic curve (ROC) analysis. Results The AUC, sensitivity, specificity, and precision of the SVM in the diagnosis of PCa in the validation set and the test set were 0.78, 63%, 80%; 0.75, 65%, and 67%, respectively. Additionally, the SVM model was found to be superior to senior radiologists’ (SR, more than 10 years of experience) diagnosis based on MRI (AUC, 0.78 vs. 0.75 in the validation set and 0.75 vs. 0.72 in the test set), and the difference was statistically significant (p< 0.05). Conclusion The prediction model constructed by the ML algorithm has good diagnostic efficiency for prostate cancer. The SVM model’s diagnostic efficiency is superior to that of MRI, as it has a more focused application value. Overall, these prediction models can aid radiologists in making better diagnoses.
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Affiliation(s)
- Kai Wang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Peizhe Chen
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, China
| | - Bojian Feng
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
| | - Jing Tu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Zhengbiao Hu
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Maoliang Zhang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jie Yang
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Ying Zhan
- Department of Ultrasound, The Affiliated Dongyang Hospital of Wenzhou Medical University, Dongyang, China
| | - Jincao Yao
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- *Correspondence: Dong Xu, ; Jincao Yao,
| | - Dong Xu
- Department of Ultrasound, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
- Key Laboratory of Head & Neck Cancer Translational Research of Zhejiang Province, Hangzhou, China
- Zhejiang Provincial Research Center for Cancer Intelligent Diagnosis and Molecular Technology, Hangzhou, China
- *Correspondence: Dong Xu, ; Jincao Yao,
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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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Chen C, Yang Z, Sweeney E, Hectors SJ, Hu JC, Margolis DJ. Prostate heterogeneity correlates with clinical features on multiparametric MRI. Abdom Radiol (NY) 2021; 46:5369-5376. [PMID: 34292363 DOI: 10.1007/s00261-021-03221-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 07/15/2021] [Accepted: 07/16/2021] [Indexed: 11/25/2022]
Abstract
BACKGROUND Prostate heterogeneity on multi-parametric MRI (mpMRI) may confound image interpretation by obscuring lesions; systematic biopsy may have a role in this context. PURPOSE To determine if prostate heterogeneity (1) correlates with clinical risk factors for prostate cancer and (2) associates with higher-grade tumor in systematic biopsy (SB), compared with MRI-directed target biopsy (MDTB), i.e., SB > MDTB, thus providing a rationale for combined biopsy. METHODS IRB-approved retrospective study included men who underwent mpMRI, SB, and MDTB between 2015 and 2017. Regions of interest were applied to the entire transition zone (TZ) and peripheral zone (PZ) on T2-weighted imaging (T2WI), apparent diffusion coefficient maps (ADC), and early dynamic contrast-enhanced (DCE) images on the midgland slice. Mean signal intensities and standard deviation (SD) of each zone were calculated. SD served as a measure of heterogeneity. Spearman's rank correlation analysis of clinical and imaging variables was performed. Univariate logistic regression was used to determine if any imaging variable associated with SB > MDTB. RESULTS 93 patients were included. Significant correlations included age and TZ ADC heterogeneity (rho = 0.34, p = 0.013), PSA density, and mean TZ ADC (rho = - 0.29, p = 0.049). PZ T2WI heterogeneity correlated with PZ ADC heterogeneity (rho = 0.48, p < 0.001). PZ DCE heterogeneity correlated with TZ DCE heterogeneity (rho = 0.46, p < 0.001). TZ ADC heterogeneity was associated with SB > MDTB prior to multiple comparison correction (p = 0.032). p value after correction was 0.24. CONCLUSION TZ ADC heterogeneity correlated with age and may reflect prostatic hyperplasia and/or prostate cancer. PZ heterogeneity, possibly a measure of prostatitis, correlated with TZ hyperplasia and/or inflammation. TZ ADC heterogeneity was associated with SB > MDTB with p value of < 0.05 prior to multiple correction; future investigation is needed to further elucidate significance of ADC heterogeneity in prostate imaging.
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Affiliation(s)
- Christine Chen
- Department of Radiology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA.
| | - Zihan Yang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Elizabeth Sweeney
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | | | - Jim C Hu
- Department of Urology, Weill Cornell Medicine, New York, NY, USA
| | - Daniel J Margolis
- Department of Radiology, Weill Cornell Medicine, 525 East 68th Street, New York, NY, 10065, USA
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