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Michael W. Kattan - One of the best experts on this subject based on the ideXlab platform.
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development of Nomograms to predict the recovery of erectile function following radical prostatectomy
The Journal of Sexual Medicine, 2019Co-Authors: John P Mulhall, Michael W. Kattan, Nelson E. Bennett, Jason Stasi, Bruno Nascimento, James A Eastham, Bertrand Guillonneau, Peter T ScardinoAbstract:Abstract Introduction Given the number of confounders in predicting erectile function recovery after radical prostatectomy (RP), a Nomogram predicting the chance to be functional after RP would be useful to patients’ and clinicians’ discussions. Aim To develop preoperative and postoperative Nomograms to aid in the prediction of erectile function recovery after RP. Main Outcome Measures International Index of Erectile Function (IIEF) erectile function domain score-based erectile function. Methods A prospective quality-of-life database was used to develop a series of Nomograms using multivariable ordinal logistic regression models. Standard preoperative and postoperative factors were included. Main Outcome Measures The Nomograms predicted the probability of recovering functional erections (erectile function domain scores ≥24) and severe erectile dysfunction (≤10) 2 years after RP. Results 3 Nomograms have been developed, including a preoperative, an early postoperative, and a 12-month postoperative version. The concordance indexes for all 3 exceeded 0.78, and the calibration was good. Clinical Implications These Nomograms may aid clinicians in discussing erectile function recovery with patients undergoing RP. Strengths & Limitations Strengths of this study included a large population, validated instrument, nerve-sparing grading, and Nomograms that are well calibrated with excellent discrimination ability. Limitations include current absence of external validation and an overall low comorbidity index. Conclusions It is hoped that these Nomograms will allow for a more accurate discussion between patients and clinicians regarding erectile function recovery after RP. Mulhall JP, Kattan MW, Bennett NE, et al. Development of Nomograms to Predict the Recovery of Erectile Function Following Radical Prostatectomy J Sex Med 2019;16:1796–1802.
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drawing Nomograms with r applications to categorical outcome and survival data
Annals of Translational Medicine, 2017Co-Authors: Zhongheng Zhang, Michael W. KattanAbstract:Outcome prediction is a major task in clinical medicine. The standard approach to this work is to collect a variety of predictors and build a model of appropriate type. The model is a mathematical equation that connects the outcome of interest with the predictors. A new patient with given clinical characteristics can be predicted for outcome with this model. However, the equation describing the relationship between predictors and outcome is often complex and the computation requires software for practical use. There is another method called Nomogram which is a graphical calculating device allowing an approximate graphical computation of a mathematical function. In this article, we describe how to draw Nomograms for various outcomes with Nomogram() function. Binary outcome is fit by logistic regression model and the outcome of interest is the probability of the event of interest. Ordinal outcome variable is also discussed. Survival analysis can be fit with parametric model to fully describe the distributions of survival time. Statistics such as the median survival time, survival probability up to a specific time point are taken as the outcome of interest.
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an independently validated Nomogram for individualized estimation of survival among patients with newly diagnosed glioblastoma nrg oncology rtog 0525 and 0825
Neuro-oncology, 2016Co-Authors: Haley Gittleman, Michael W. Kattan, Andrew E Sloan, Mitchell Machtay, Daniel Lim, Arnab Chakravarti, Mark R Gilbert, Andrew B Lassman, Erik P Sulman, Devin TianAbstract:Background Glioblastoma (GBM) is the most common primary malignant brain tumor. Nomograms are often used for individualized estimation of prognosis. This study aimed to build and independently validate a Nomogram to estimate individualized survival probabilities for patients with newly diagnosed GBM, using data from 2 independent NRG Oncology Radiation Therapy Oncology Group (RTOG) clinical trials. Methods This analysis included information on 799 (RTOG 0525) and 555 (RTOG 0825) eligible and randomized patients with newly diagnosed GBM and contained the following variables: age at diagnosis, race, gender, Karnofsky performance status (KPS), extent of resection, O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and survival (in months). Survival was assessed using Cox proportional hazards regression, random survival forests, and recursive partitioning analysis, with adjustment for known prognostic factors. The models were developed using the 0525 data and were independently validated using the 0825 data. Models were internally validated using 10-fold cross-validation, and individually predicted 6-, 12-, and 24-month survival probabilities were generated to measure the predictive accuracy and calibration against the actual survival status. Results A final Nomogram was built using the Cox proportional hazards model. Factors that increased the probability of shorter survival included greater age at diagnosis, male gender, lower KPS, not having total resection, and unmethylated MGMT status. Conclusions A Nomogram that assesses individualized survival probabilities (6-, 12-, and 24-mo) for patients with newly diagnosed GBM could be useful to health care providers for counseling patients regarding treatment decisions and optimizing therapeutic approaches. Free software for implementing this Nomogram is provided: http://cancer4.case.edu/rCalculator/rCalculator.html.
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Nomograms for predicting survival and recurrence in patients with adenoid cystic carcinoma an international collaborative study
European Journal of Cancer, 2015Co-Authors: Ian Ganly, Michael W. Kattan, Moran Amit, Lei Kou, Frank L Palmer, Jocelyn C Migliacci, Nora Katabi, Yoav Binenbaum, Kanika Sharma, Ramer NaomiAbstract:Due to the rarity of adenoid cystic carcinoma (ACC), information on outcome is based upon small retrospective case series. The aim of our study was to create a large multiinstitutional international dataset of patients with ACC in order to design predictive Nomograms for outcome.ACC patients managed at 10 international centers were identified. Patient, tumor, and treatment characteristics were recorded and an international collaborative dataset created. Multivariable competing risk models were then built to predict the 10 year recurrence free probability (RFP), distant recurrence free probability (DRFP), overall survival (OS) and cancer specific mortality (CSM). All predictors of interest were added in the starting full models before selection, including age, gender, tumor site, clinical T stage, perineural invasion, margin status, pathologic N-status, and M-status. Stepdown method was used in model selection to choose predictive variables. An external dataset of 99 patients from 2 other institutions was used to validate the Nomograms.Of 438 ACC patients, 27.2% (119/438) died from ACC and 38.8% (170/438) died of other causes. Median follow-up was 56 months (range 1-306). The Nomogram for OS had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N-status and M-status) with a concordance index (CI) of 0.71. The Nomogram for CSM had the same variables, except margin status, with a concordance index (CI) of 0.70. The Nomogram for RFP had 7 variables (age, gender, clinical T stage, tumor site, margin status, pathologic N status and perineural invasion) (CI 0.66). The Nomogram for DRFP had 6 variables (gender, clinical T stage, tumor site, pathologic N-status, perineural invasion and margin status) (CI 0.64). Concordance index for the external validation set were 0.76, 0.72, 0.67 and 0.70 respectively.Using an international collaborative database we have created the first Nomograms which estimate outcome in individual patients with ACC. These predictive Nomograms will facilitate patient counseling in terms of prognosis and subsequent clinical follow-up. They will also identify high risk patients who may benefit from clinical trials on new targeted therapies for patients with ACC.None.
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conditional probability of survival Nomogram for 1 2 and 3 year survivors after an r0 resection for gastric cancer
Annals of Surgical Oncology, 2013Co-Authors: Michael W. Kattan, Mithat Gonen, Johan L Dikken, Raymond E Baser, Manish A Shah, Marcel Verheij, Cornelis J H Van De Velde, Murray F Brennan, Daniel G CoitAbstract:Background Survival estimates after curative surgery for gastric cancer are based on AJCC staging, or on more accurate multivariable Nomograms. However, the risk of dying of gastric cancer is not constant over time, with most deaths occurring in the first 2 years after resection. Therefore, the prognosis for a patient who survives this critical period improves. This improvement over time is termed conditional probability of survival (CPS). Objectives of this study were to develop a CPS Nomogram predicting 5-year disease-specific survival (DSS) from the day of surgery for patients surviving a specified period of time after a curative gastrectomy and to explore whether variables available with follow-up improve the Nomogram in the follow-up setting.
Pierre I Karakiewicz - One of the best experts on this subject based on the ideXlab platform.
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initial biopsy outcome prediction head to head comparison of a logistic regression based Nomogram versus artificial neural network
European Urology, 2007Co-Authors: Michael W. Kattan, Markus Graefen, Felix K H Chun, Hartwig Huland, Alberto Briganti, Andrea Gallina, Julia Hopp, Pierre I KarakiewiczAbstract:Abstract Objectives Nomograms and artificial neural networks (ANNs) represent alternative methodologic approaches to predict the probability of prostate cancer on initial biopsy. We hypothesized that, in a head-to-head comparison, one of the approaches might demonstrate better accuracy and performance characteristics than the other. Methods A previously published Nomogram, which relies on age, digital rectal examination, serum prostate-specific antigen (PSA), and percent-free PSA, and an ANN, which relies on the same predictors plus prostate volume, were applied to a cohort of 3980 men, who were subjected to multicore systematic prostate biopsy. The accuracy and the performance characteristics were compared between these two approaches. Results The accuracy of the Nomogram was 71% versus 67% for the ANN ( p =0.0001). Graphical exploration of the performance characteristics demonstrated virtually perfect predictions for the Nomogram. Conversely, the ANN underestimated the observed rate of prostate cancer. Conclusions A 4% increase in predictive accuracy implies that the use of the Nomogram instead of the ANN will result in 40 additional patients who will be correctly classified between benign and cancer.
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a critical appraisal of logistic regression based Nomograms artificial neural networks classification and regression tree models look up tables and risk group stratification models for prostate cancer
BJUI, 2007Co-Authors: Felix K H Chun, Michael W. Kattan, Hartwig Huland, Pierre I Karakiewicz, A Briganti, J Walz, Markus GraefenAbstract:OBJECTIVE To evaluate several methods of predicting prostate cancer-related outcomes, i.e. Nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives. METHODS We present four direct comparisons, where a Nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models. RESULTS Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics. CONCLUSION These results suggest that Nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.
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precystectomy Nomogram for prediction of advanced bladder cancer stage
European Urology, 2006Co-Authors: Pierre I Karakiewicz, Shahrokh F Shariat, Ganesh S Palapattu, Paul Perrotte, Yair Lotan, Craig G Rogers, Gilad E Amiel, Amnon Vazina, Amit Gupta, Patrick J BastianAbstract:Abstract Objective To evaluate precystectomy prediction of pT and pN stages at cystectomy. Methods Multivariate logistic regression analyses modelled variables of 726 evaluable patients treated with radical cystectomy and bilateral pelvic lymphadenectomy. The first set of models predicted pT 3–4 stage at cystectomy, and the second set predicted pN 1–3 stages at cystectomy. Transurethral resection (TUR) predictors consisted of 2002 T stage, 1973 WHO tumour grade, presence of carcinoma in situ, age, gender, and delivery of neo-adjuvant chemotherapy. The area under the ROC curve quantified Nomogram accuracy. Two hundred bootstrap resamples were used to reduce overfit bias. Results At TUR, 11% of patients were staged as pT 3–4 versus 42% at cystectomy. Lymph node metastases were found in 24% of patients at cystectomy (pN 1–3 ). The multivariate pT 3–4 Nomogram was 75.7% accurate versus 71.4% for TUR T stage. The multivariate pN 1–3 Nomogram was 63.1% accurate versus 61.0% for TUR T stage. Conclusion Multivariate Nomograms are not perfect, but they do predict more accurately than TUR T stage alone.
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high incidence of prostate cancer detected by saturation biopsy after previous negative biopsy series
European Urology, 2006Co-Authors: Jochen Walz, Markus Graefen, Felix K H Chun, Andreas Erbersdobler, Alexander Haese, Thomas Steuber, Thorsten Schlomm, Hartwig Huland, Pierre I KarakiewiczAbstract:Abstract Objectives We explored the yield of saturation biopsy and developed a Nomogram predicting the probability of prostate cancer (PCa) on the basis of saturation biopsy. Materials and methods Between 2001 and 2004, saturation biopsies (average of 24 cores) were performed in 161 men with persistently elevated prostate specific antigen (PSA) level (median, 9ng/ml). All had at least two previously negative, eight-core biopsy sessions. PCa predictors on saturation biopsy were integrated within multivariate Nomograms. Results PCa detection was 41% ( n =66 of 161). PSA density and transition zone volume were the most significant predictors of PCa on saturation biopsy. The accuracy of the Nomogram with the best performance characteristics was 72%. Conclusions Saturation biopsy may be indicated in men with a persistent suspicion of PCa. High-risk individuals can be identified accurately with our Nomogram.
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pre treatment Nomogram for disease specific survival of patients with chemotherapy naive androgen independent prostate cancer
European Urology, 2006Co-Authors: Robert S Svatek, Pierre I Karakiewicz, Paul Perrotte, Michael J Shulman, Jose A Karam, Elie A BenaimAbstract:OBJECTIVE: Our objective was to develop a Nomogram that predicts the probability of cancer-specific survival in men with untreated androgen-independent prostate cancer (AIPC). METHODS: AIPC was diagnosed in 129 consecutive patients between 1989 and 2002. No patient received cytotoxic chemotherapy. Univariate and multivariate Cox regression models were used to test the association between prostate-specific antigen (PSA) level at initiation of androgen deprivation, PSA doubling time (PSADT), PSA nadir on androgen deprivation therapy (ADT), time from ADT to AIPC, and AIPC-specific mortality. Multivariate regression coefficients were then used to develop a Nomogram predicting AIPC-specific survival at 12-60 mo after AIPC diagnosis. Two-hundred bootstrap resamples were used to internally validate the Nomogram. RESULTS: AIPC-specific mortality was recorded in 74 of 129 patients (57.4%). Other-cause mortality was recorded in 7 men (5.4%). Median overall survival was 52.0 mo (mean, 36.0 mo) and median AIPC-specific survival was 54.0 mo (mean, 35.0 mo). In univariate regression models, all variables were significant predictors of AIPC-specific survival (p < or = 0.02). In multivariate models, PSADT and time from androgen deprivation to AIPC remained statistically significant (p < or = 0.004). Bootstrap-corrected predictive accuracy of the Nomogram was 80.9% versus 74.9% for our previous model. CONCLUSIONS: A Nomogram predicting AIPC-specific survival is between 13% and 14% more accurate than previous Nomograms and 6% more accurate than tree regression-based predictions obtained from the same data. Moreover, a Nomogram approach combines several advantages, such as user-friendly interface and precise estimation of individual recurrence probability at several time points after AIPC diagnosis, which all patients deserve to know and all treating physicians need to know.
Jie Tian - One of the best experts on this subject based on the ideXlab platform.
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integrating manual diagnosis into radiomics for reducing the false positive rate of 18 f fdg pet ct diagnosis in patients with suspected lung cancer
European Journal of Nuclear Medicine and Molecular Imaging, 2019Co-Authors: Fei Kang, Jie Gong, Wei Qin, Shengjun Wang, Jie Tian, Jing WangAbstract:The high false positive rate (FPR) of 18F-FDG PET/CT in lung cancer screening represents a severe challenge for clinical decision-making. This study aimed to develop a clinical-translatable radiomics Nomogram for reducing the FPR of PET/CT in lung cancer diagnosis, and to determine the impact of integrating manual diagnosis to the performance of the radiomics Nomogram. Among 3,947 18F-FDG PET/CT-screened patients with lung lesion, 157 malignant and 111 benign patients were retrospectively enrolled and divided into training and test cohorts. The data of manual diagnosis were recorded. A total of 4,338 features were extracted from CT, thin-section CT, PET and PET/CT, and the four radiomics signatures (RS) were then generated by LASSO method. Radiomics prediction Nomogram integrating imaging-based RS and manual diagnosis was developed using multivariable logistic regression. The performances of RS and prediction Nomograms were independently validated through key discrimination index and clinical benefit. The FPR of manual diagnosis was found to be 30.6%. Among the four RS, PET/CT RS exhibited the best performance. By integrating manual diagnosis, the hybrid Nomogram integrating PET/CT RS and manual diagnosis demonstrated lowest FPR and highest area under curve (AUC) and Youden index (YI) in both training and test cohorts (FPR: 5.4% and 9.1%, AUC: 0.98 and 0.92, YI: 85.8% and 75.5%, respectively). This hybrid Nomogram respectively corrected 78.6% and 37.5% among FPR cases produced by PET/CT RS, without significantly sacrificing its sensitivity. The net benefit of hybrid Nomogram appeared highest at <85% threshold probability. The established hybrid Nomogram integrating PET/CT RS and manual diagnosis can significantly reduce FPR, improve diagnostic accuracy and enhance clinical benefit compared to manual diagnosis. By integrating manual diagnosis, the performance of this hybrid Nomogram is superior to PET/CT RS, indicating the importance of clinicians’ judgement as an essential information source for improving radiomics diagnostic approaches.
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ultrasound based radiomics score a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma
European Radiology, 2019Co-Authors: Zhu Wang, Jie Tian, Wei Wang, Xiaowen Huang, Shuling Chen, Xin Zheng, Simin Ruan, Xiaoyan Xie, Ping Liang, Ming KuangAbstract:To develop an ultrasound (US)-based radiomics score for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC). Between January 1, 2012, and October 31, 2017, a total of 482 HCC patients who underwent contrast-enhanced ultrasound (CEUS) were retrospectively reviewed. The study population was divided into a training cohort (n = 341) and a validation cohort (n = 141) based on a cutoff time of January 1, 2016. Radiomics features were extracted from the grayscale US images of HCC. After features selection, a radiomics score was developed from the training cohort. The incremental value of the radiomics score to the clinic-pathological factors for MVI prediction was assessed in the validation cohort with respect to discrimination, calibration, and clinical usefulness. The US-based radiomics score consisted of six selected features. Multivariate logistic regression analysis showed that the radiomics score, alpha-fetoprotein (AFP), and tumor size were independent predictors of MVI. The radiomics Nomogram (based on the three factors) showed better performance for MVI detection (area under the curve [AUC] 0.731[0.647, 0.815] than the clinical Nomogram (based on AFP and tumor size) (0.634 [0.543, 0.724]) (p = 0.015). Both Nomograms showed good calibration. Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics Nomogram outperformed the clinical Nomogram. The US-based radiomics score was an independent predictor of MVI in HCC. Combining the radiomics score with clinical factors improved the prediction efficacy. • Radiomics can be applied in US images. • US-based radiomics score was an independent predictor of MVI. • Radiomics Nomogram incorporated with the radiomics score showed good performance for MVI prediction.
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preoperative radiomics Nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast enhanced ct
European Radiology, 2019Co-Authors: Jingwei Wei, Yongjian Zhu, Bing Feng, Meng Liang, Shuang Wang, Xinming Zhao, Jie TianAbstract:To develop and validate a radiomics Nomogram for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). The study included 157 patients with histologically confirmed HCC with or without MVI, and 110 patients were allocated to the training dataset and 47 to the validation dataset. Baseline clinical factor (CF) data were collected from our medical records, and radiomics features were extracted from the artery phase (AP), portal venous phase (PVP) and delay phase (DP) of preoperatively acquired CT in all patients. Radiomics analysis included tumour segmentation, feature extraction, model construction and model evaluation. A final Nomogram for predicting MVI of HCC was established. Nomogram performance was assessed via both calibration and discrimination statistics. Five AP features, seven PVP features and nine DP features were effective for MVI prediction in HCC radiomics signatures. PVP radiomics signatures exhibited better performance than AP and DP radiomics signatures in the validation datasets, with the AUC 0.793. In the clinical model, age, maximum tumour diameter, alpha-fetoprotein and hepatitis B antigen were effective predictors. The final Nomogram integrated the PVP radiomics signature and four CFs. Good calibration was achieved for the Nomogram in both the training and validated datasets, with respective C-indexes of 0.827 and 0.820. Decision curve analysis suggested that the proposed Nomogram was clinically useful, with a corresponding net benefit of 0.357. The above-described radiomics Nomogram can preoperatively predict MVI in patients with HCC and may constitute a usefully clinical tool to guide subsequent personalised treatment. • No previously reported study has utilised radiomics Nomograms to preoperatively predict the MVI of HCC using 3D contrast-enhanced CT imaging. • The combined radiomics clinical factor (CF) Nomogram for predicting MVI achieved superior performance than either the radiomics signature or the CF Nomogram alone. • Nomograms combing PVP radiomics and CF may be useful as an imaging marker for predicting MVI of HCC preoperatively and could guide personalised treatment.
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radiomics features of multiparametric mri as novel prognostic factors in advanced nasopharyngeal carcinoma
Clinical Cancer Research, 2017Co-Authors: Jie Tian, Bin Zhang, Di Dong, Yuhao Dong, Lu Zhang, Zhouyang LianAbstract:Purpose: To identify MRI-based radiomics as prognostic factors in patients with advanced nasopharyngeal carcinoma (NPC).Experimental Design: One-hundred and eighteen patients (training cohort: n = 88; validation cohort: n = 30) with advanced NPC were enrolled. A total of 970 radiomics features were extracted from T2-weighted (T2-w) and contrast-enhanced T1-weighted (CET1-w) MRI. Least absolute shrinkage and selection operator (LASSO) regression was applied to select features for progression-free survival (PFS) Nomograms. Nomogram discrimination and calibration were evaluated. Associations between radiomics features and clinical data were investigated using heatmaps.Results: The radiomics signatures were significantly associated with PFS. A radiomics signature derived from joint CET1-w and T2-w images showed better prognostic performance than signatures derived from CET1-w or T2-w images alone. One radiomics Nomogram combined a radiomics signature from joint CET1-w and T2-w images with the TNM staging system. This Nomogram showed a significant improvement over the TNM staging system in terms of evaluating PFS in the training cohort (C-index, 0.761 vs. 0.514; P < 2.68 × 10-9). Another radiomics Nomogram integrated the radiomics signature with all clinical data, and thereby outperformed a Nomogram based on clinical data alone (C-index, 0.776 vs. 0.649; P < 1.60 × 10-7). Calibration curves showed good agreement. Findings were confirmed in the validation cohort. Heatmaps revealed associations between radiomics features and tumor stages.Conclusions: Multiparametric MRI-based radiomics Nomograms provided improved prognostic ability in advanced NPC. These results provide an illustrative example of precision medicine and may affect treatment strategies. Clin Cancer Res; 23(15); 4259-69. ©2017 AACR.
Irene A Burger - One of the best experts on this subject based on the ideXlab platform.
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68ga psma 11 pet has the potential to improve patient selection for extended pelvic lymph node dissection in intermediate to high risk prostate cancer
European Journal of Nuclear Medicine and Molecular Imaging, 2020Co-Authors: Daniela A Ferraro, Urs J Muehlematter, Helena Garcia Schuler, Niels J Rupp, Martin W Huellner, Michael Messerli, Jan H Ruschoff, Edwin E G W Ter Voert, Thomas Hermanns, Irene A BurgerAbstract:Radical prostatectomy with extended pelvic lymph node dissection (ePLND) is a curative treatment option for patients with clinically significant localised prostate cancer. The decision to perform an ePLND can be challenging because the overall incidence of lymph node metastasis is relatively low and ePLND is not free of complications. Using current clinical Nomograms to identify patients with nodal involvement, approximately 75–85% of ePLNDs performed are negative. The aim of this study was to assess the added value of 68Ga-PSMA-11 PET in predicting lymph node metastasis in men with intermediate- or high-risk prostate cancer. 68Ga-PSMA-11 PET scans of 60 patients undergoing radical prostatectomy with ePLND were reviewed for qualitative (visual) assessment of suspicious nodes and assessment of quantitative parameters of the primary tumour in the prostate (SUVmax, total activity (PSMAtotal) and PSMA positive volume (PSMAvol)). Ability of quantitative PET parameters to predict nodal metastasis was assessed with receiver operating characteristics (ROC) analysis. A multivariable logistic regression model combining PSA, Gleason score, visual nodal status on PET and primary tumour PSMAtotal was built. Net benefit at each risk threshold was compared with five Nomograms: MSKCC Nomogram, Yale formula, Roach formula, Winter Nomogram and Partin tables (2016). Overall, pathology of ePLND specimens revealed 31 pelvic metastatic lymph nodes in 12 patients. 68Ga-PSMA-11 PET visual analysis correctly detected suspicious nodes in 7 patients, yielding a sensitivity of 58% and a specificity of 98%. The area under the ROC curve for primary tumour SUVmax was 0.70, for PSMAtotal 0.76 and for PSMAvol 0.75. The optimal cut-off for nodal involvement was PSMAtotal > 49.1. The PET model including PSA, Gleason score and quantitative PET parameters had a persistently higher net benefit compared with all clinical Nomograms. Our model combining PSA, Gleason score and visual lymph node analysis on 68Ga-PSMA-11 PET with PSMAtotal of the primary tumour showed a tendency to improve patient selection for ePLND over the currently used clinical Nomograms. Although this result has to be validated, 68Ga-PSMA-11 PET showed the potential to reduce unnecessary surgical procedures in patients with intermediate- or high-risk prostate cancer.
Jing Wang - One of the best experts on this subject based on the ideXlab platform.
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integrating manual diagnosis into radiomics for reducing the false positive rate of 18 f fdg pet ct diagnosis in patients with suspected lung cancer
European Journal of Nuclear Medicine and Molecular Imaging, 2019Co-Authors: Fei Kang, Jie Gong, Wei Qin, Shengjun Wang, Jie Tian, Jing WangAbstract:The high false positive rate (FPR) of 18F-FDG PET/CT in lung cancer screening represents a severe challenge for clinical decision-making. This study aimed to develop a clinical-translatable radiomics Nomogram for reducing the FPR of PET/CT in lung cancer diagnosis, and to determine the impact of integrating manual diagnosis to the performance of the radiomics Nomogram. Among 3,947 18F-FDG PET/CT-screened patients with lung lesion, 157 malignant and 111 benign patients were retrospectively enrolled and divided into training and test cohorts. The data of manual diagnosis were recorded. A total of 4,338 features were extracted from CT, thin-section CT, PET and PET/CT, and the four radiomics signatures (RS) were then generated by LASSO method. Radiomics prediction Nomogram integrating imaging-based RS and manual diagnosis was developed using multivariable logistic regression. The performances of RS and prediction Nomograms were independently validated through key discrimination index and clinical benefit. The FPR of manual diagnosis was found to be 30.6%. Among the four RS, PET/CT RS exhibited the best performance. By integrating manual diagnosis, the hybrid Nomogram integrating PET/CT RS and manual diagnosis demonstrated lowest FPR and highest area under curve (AUC) and Youden index (YI) in both training and test cohorts (FPR: 5.4% and 9.1%, AUC: 0.98 and 0.92, YI: 85.8% and 75.5%, respectively). This hybrid Nomogram respectively corrected 78.6% and 37.5% among FPR cases produced by PET/CT RS, without significantly sacrificing its sensitivity. The net benefit of hybrid Nomogram appeared highest at <85% threshold probability. The established hybrid Nomogram integrating PET/CT RS and manual diagnosis can significantly reduce FPR, improve diagnostic accuracy and enhance clinical benefit compared to manual diagnosis. By integrating manual diagnosis, the performance of this hybrid Nomogram is superior to PET/CT RS, indicating the importance of clinicians’ judgement as an essential information source for improving radiomics diagnostic approaches.
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radiomics technique helps to reduce the false positive rate of 18f fdg pet ct diagnosis in suspicious lung cancer importance of integrating clinical experience
The Journal of Nuclear Medicine, 2019Co-Authors: Fei Kang, Jie Gong, Weidong Yang, Wei Qin, Jing WangAbstract:55 Purpose: Due to infective lesions, high false positive rate (FPR) of 18F-FDG PET/CT in lung cancer diagnosis is a severe challenge for making accurate clinical decision. Herein, based on novel radiomics methodology, we aim to establish a hybrid Nomogram combining 18F-FDG PET/CT multimodality data and clinical judgement to reduce the FPR in the lung cancer differentiation of PET/CT, and compare its performance with the Nomograms without clinical diagnosis integration. Methods: From 3947 screened lung-lesion patients who received 18F-FDG PET/CT scan from February 2007 to March 2017, 157 malignant and 111 benign patients were ultimately enrolled and randomly divided into training and test cohort with approximately equal sample size. Manual diagnosis was retrospectively recorded, and the reconstructed data of CT, thin-section CT (TSCT) and PET were stored. Lesion delineate and feature extraction were performed in training cohort following the radiomics methodology to establish and validate the Nomograms of CT, TSCT, PET, PET/CT and PET/CT + manual diagnosis, respectively. The performance of these Nomogram was assessed with respect to their calibration, key discrimination index, and clinical benefit in test cohort. Results: The overall FPR of manual diagnosis was 30.63%. Among the established Nomograms, the C-index and YI of the PET/CT + manual diagnosis hybrid Nomogram was the highest, and FPR the lowest in both of the training and test cohort (training and test cohort: C-index = 0.982 and 0.924, YI = 85.78% and 75.53%, FPR = 5.36% and 9.09%). In training and test cohort, by combining manual diagnosis, hybrid Nomogram respectively corrected 78.57% (11/14) and 37.5% (3/8) FPR cases produced by the PET/CT RS. The net benefit of hybrid Nomogram of PET/CT + manual diagnosis was the highest when the threshold probability is
