A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Early Hepatocellular Carcinoma: A Study Based on SEER Database

Background: Primary liver cancer is a common malignant tumor primarily represented by hepatocellular carcinoma (HCC). The number of elderly patients with early HCC is increasing, and older age is related to a worse prognosis. However, an accurate predictive model for the prognosis of these patients is still lacking.Methods: Data of eligible elderly patients with early HCC in Surveillance, Epidemiology, and End Results database from 2010 to 2016 were downloaded. Patients from 2010 to 2015 were randomly assigned to the training cohort (n = 1093) and validation cohort (n = 461). Patients' data in 2016 (n = 431) was used for external validation. Independent prognostic factors were obtained using univariate and multivariate analyses. Based on these factors, a cancer-specific survival (CSS) nomogram was constructed. The predictive performance and clinical practicability of our nomogram were validated. According to the risk scores of our nomogram, patients were divided into low-, intermediate-, and high-risk groups. A survival analysis was performed using Kaplan–Meier curves and log-rank tests.Results: Age, race, T stage, histological grade, surgery, radiotherapy, and chemotherapy were independent predictors for CSS and thus were included in our nomogram. In the training cohort and validation cohort, the concordance indices (C-indices) of our nomogram were 0.739 (95% CI: 0.714–0.764) and 0.756 (95% CI: 0.719–0.793), respectively. The 1-, 3-, and 5-year areas under receiver operating characteristic curves (AUCs) showed similar results. Calibration curves revealed high consistency between observations and predictions. In external validation cohort, C-index (0.802, 95%CI: 0.778–0.826) and calibration curves also revealed high consistency between observations and predictions. Compared with the TNM stage, nomogram-related decision curve analysis (DCA) curves indicated better clinical practicability. Kaplan–Meier curves revealed that CSS significantly differed among the three different risk groups. In addition, an online prediction tool for CSS was developed.Conclusions: A web-based prediction model for CSS of elderly patients with early HCC was constructed and validated, and it may be helpful for the prognostic evaluation, therapeutic strategy selection, and follow-up management of these patients.

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An Externally Validated Dynamic Nomogram for Predicting Unfavorable Prognosis in Patients With Aneurysmal Subarachnoid Hemorrhage

Frontiers in Neurology ◽

10.3389/fneur.2021.683051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ping Hu ◽

Yang Xu ◽

Yangfan Liu ◽

Yuntao Li ◽

Liguo Ye ◽

...

Keyword(s):

Logistic Regression ◽

Prognostic Factors ◽

Subarachnoid Hemorrhage ◽

Validation Cohort ◽

Aneurysmal Subarachnoid Hemorrhage ◽

External Validation ◽

Brier Score ◽

Web Based ◽

Training Cohort ◽

Multivariable Logistic Regression

Background: Aneurysmal subarachnoid hemorrhage (aSAH) leads to severe disability and functional dependence. However, no reliable method exists to predict the clinical prognosis after aSAH. Thus, this study aimed to develop a web-based dynamic nomogram to precisely evaluate the risk of poor outcomes in patients with aSAH.Methods: Clinical patient data were retrospectively analyzed at two medical centers. One center with 126 patients was used to develop the model. Least absolute shrinkage and selection operator (LASSO) analysis was used to select the optimal variables. Multivariable logistic regression was applied to identify independent prognostic factors and construct a nomogram based on the selected variables. The C-index and Hosmer–Lemeshow p-value and Brier score was used to reflect the discrimination and calibration capacities of the model. Receiver operating characteristic curve and calibration curve (1,000 bootstrap resamples) were generated for internal validation, while another center with 84 patients was used to validate the model externally. Decision curve analysis (DCA) and clinical impact curves (CICs) were used to evaluate the clinical usefulness of the nomogram.Results: Unfavorable prognosis was observed in 46 (37%) patients in the training cohort and 24 (29%) patients in the external validation cohort. The independent prognostic factors of the nomogram, including neutrophil-to-lymphocyte ratio (NLR) (p = 0.005), World Federation of Neurosurgical Societies (WFNS) grade (p = 0.002), and delayed cerebral ischemia (DCI) (p = 0.0003), were identified using LASSO and multivariable logistic regression. A dynamic nomogram (https://hu-ping.shinyapps.io/DynNomapp/) was developed. The nomogram model demonstrated excellent discrimination, with a bias-corrected C-index of 0.85, and calibration capacities (Hosmer–Lemeshow p-value, 0.412; Brier score, 0.12) in the training cohort. Application of the model to the external validation cohort yielded a C-index of 0.84 and a Brier score of 0.13. Both DCA and CIC showed a superior overall net benefit over the entire range of threshold probabilities.Conclusion: This study identified that NLR on admission, WFNS grade, and DCI independently predicted unfavorable prognosis in patients with aSAH. These factors were used to develop a web-based dynamic nomogram application to calculate the precise probability of a poor patient outcome. This tool will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

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Development and validation of an early death risk score for older patients treated with chemotherapy for cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.12030 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. 12030-12030

Author(s):

Jaime Feliu Batlle ◽

Alvaro Pinto ◽

Laura Basterretxea ◽

Irene Paredero Pérez ◽

Elisenda Llabres ◽

...

Keyword(s):

Elderly Patients ◽

Risk Score ◽

Older Patients ◽

Validation Cohort ◽

External Validation ◽

Early Death ◽

Chemotherapy Treatment ◽

Training Cohort ◽

Death Risk ◽

Patients With Cancer

12030 Background: Determining life expectancy in older patients is needed to select the best treatment strategy. We aimed to develop and validate a score to predict early death risk ( < 6 months) in elderly patients with cancer that are planned to initiate chemotherapy treatment. Methods: Patients over 70 years starting new chemotherapy regimens were prospectively included in a multicenter study. A pre-chemotherapy assessment that included sociodemographics, tumor/treatment variables, and geriatric assessment variables, was performed. Association between these factors and early death was examined by using multivariate logistic regression. Score points were assigned to each risk factor based on their b coefficient. We validated the risk score with an external validation cohort of 206 patients. Results: Three hundred forty two patients were included in the training cohort. The independent predictors for early death were metastasic cancers (odds ratio [OR] 4.8, 95% confidence interval [CI], [2.4-9.6]), ECOG performance status (OR 2.3, 95% CI:1.084-5.232), ADL (OR 1.7, 95% CI:1.08-3.5), serum albumin levels (3.3, 95% CI: 1.6-6.6), BMI (OR 2.4, 95% CI:1,2-4.8), serum GGT levels (OR 1.5, 95% CI:1.05-1.8) and hemoglobin levels (OR 2.3, 95% CI:1.2-4.6). With these results, a score was to stratify patients regarding their risk of early death: low (0 to 2 points; 5%), intermediate (3 to 5 points; 19%) or high (6 to 14 points; 50%) (p < 0.001). The area under the curve of the receiver-operating characteristic (ROC) curve was 0.79 for the training cohort (95% CI, 0.74 to 0.85), and 0.70 (95% CI: 0.60-0.80) for the validation cohort (difference between cohorts not statistically different). Conclusions: We developed a highly accurate tool that uses basic clinical and analytical information to predict the probability of early death in elderly patients with cancer that are planned to initiate chemotherapy treatment. This tool can help physicians in decision making for this population of patients.

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Development and Validation of a Predictive Scoring System for Colorectal Cancer Patients With Liver Metastasis: A Population-Based Study

Frontiers in Oncology ◽

10.3389/fonc.2021.719638 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yinghao Cao ◽

Songqing Ke ◽

Shenghe Deng ◽

Lizhao Yan ◽

Junnan Gu ◽

...

Keyword(s):

Colorectal Cancer ◽

Liver Metastasis ◽

Lymph Nodes ◽

Validation Cohort ◽

Characteristic Curve ◽

External Validation ◽

Tumor Location ◽

Cancer Specific Survival ◽

Internal Validation ◽

Training Cohort

Liver metastasis in colorectal cancer (CRC) is common and has an unfavorable prognosis. This study aimed to establish a functional nomogram model to predict overall survival (OS) and cancer-specific survival (CSS) in patients with colorectal cancer liver metastasis (CRCLM). A total of 9,736 patients with CRCLM from 2010 to 2016 were randomly assigned to training, internal validation, and external validation cohorts. Univariate and multivariate Cox analyses were performed to identify independent clinicopathologic predictive factors, and a nomogram was constructed to predict CSS and OS. Multivariate analysis demonstrated age, tumor location, differentiation, gender, TNM stage, chemotherapy, number of sampled lymph nodes, number of positive lymph nodes, tumor size, and metastatic surgery as independent predictors for CRCLM. A nomogram incorporating the 10 predictors was constructed. The nomogram showed favorable sensitivity at predicting 1-, 3-, and 5-year OS, with area under the receiver operating characteristic curve (AUROC) values of 0.816, 0.782, and 0.787 in the training cohort; 0.827, 0.769, and 0.774 in the internal validation cohort; and 0.819, 0.745, and 0.767 in the external validation cohort, respectively. For CSS, the values were 0.825, 0.771, and 0.772 in the training cohort; 0.828, 0.753, and 0.758 in the internal validation cohort; and 0.828, 0.737, and 0.772 in the external validation cohort, respectively. Calibration curves and ROC curves revealed that using our models to predict the OS and CSS would add more benefit than other single methods. In summary, the novel nomogram based on significant clinicopathological characteristics can be conveniently used to facilitate the postoperative individualized prediction of OS and CSS in CRCLM patients.

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Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study

Cancer Imaging ◽

10.1186/s40644-021-00395-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Sijia Cui ◽

Tianyu Tang ◽

Qiuming Su ◽

Yajie Wang ◽

Zhenyu Shu ◽

...

Keyword(s):

Calibration Curve ◽

Main Pancreatic Duct ◽

Validation Cohort ◽

External Validation ◽

High Grade ◽

Intraductal Papillary Mucinous Neoplasms ◽

Training Cohort ◽

Independent Validation ◽

Medical Centers ◽

Mucinous Neoplasms

Abstract Background Accurate diagnosis of high-grade branching type intraductal papillary mucinous neoplasms (BD-IPMNs) is challenging in clinical setting. We aimed to construct and validate a nomogram combining clinical characteristics and radiomic features for the preoperative prediction of low and high-grade in BD-IPMNs. Methods Two hundred and two patients from three medical centers were enrolled. The high-grade BD-IPMN group comprised patients with high-grade dysplasia and invasive carcinoma in BD-IPMN (n = 50). The training cohort comprised patients from the first medical center (n = 103), and the external independent validation cohorts comprised patients from the second and third medical centers (n = 48 and 51). Within 3 months prior to surgery, all patients were subjected to magnetic resonance examination. The volume of interest was delineated on T1-weighted (T1-w) imaging, T2-weighted (T2-w) imaging, and contrast-enhanced T1-weighted (CET1-w) imaging, respectively, on each tumor slice. Quantitative image features were extracted using MITK software (G.E.). The Mann-Whitney U test or independent-sample t-test, and LASSO regression, were applied for data dimension reduction, after which a radiomic signature was constructed for grade assessment. Based on the training cohort, we developed a combined nomogram model incorporating clinical variables and the radiomic signature. Decision curve analysis (DCA), a receiver operating characteristic curve (ROC), a calibration curve, and the area under the ROC curve (AUC) were used to evaluate the utility of the constructed model based on the external independent validation cohorts. Results To predict tumor grade, we developed a nine-feature-combined radiomic signature. For the radiomic signature, the AUC values of high-grade disease were 0.836 in the training cohort, 0.811 in external validation cohort 1, and 0.822 in external validation cohort 2. The CA19–9 level and main pancreatic duct size were identified as independent parameters of high-grade of BD-IPMNs using multivariate logistic regression analysis. The CA19–9 level and main pancreatic duct size were then used to construct the radiomic nomogram. Using the radiomic nomogram, the high-grade disease-associated AUC values were 0.903 (training cohort), 0.884 (external validation cohort 1), and 0.876 (external validation cohort 2). The clinical utility of the developed nomogram was verified using the calibration curve and DCA. Conclusions The developed radiomic nomogram model could effectively distinguish high-grade patients with BD-IPMNs preoperatively. This preoperative identification might improve treatment methods and promote personalized therapy in patients with BD-IPMNs.

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Establishment and Validation of an Integrated Model to Predict Postoperative Recurrence in Patients With Atypical Meningioma

Frontiers in Oncology ◽

10.3389/fonc.2021.754937 ◽

2021 ◽

Vol 11 ◽

Author(s):

Xiao-Yong Chen ◽

Jin-Yuan Chen ◽

Yin-Xing Huang ◽

Jia-Heng Xu ◽

Wei-Wei Sun ◽

...

Keyword(s):

Cox Regression ◽

Validation Cohort ◽

Clinical Laboratory ◽

External Validation ◽

Postoperative Recurrence ◽

Integrated Model ◽

Atypical Meningioma ◽

Tumor Diameter ◽

Roc Curve Analysis ◽

Training Cohort

BackgroundThis study aims to establish an integrated model based on clinical, laboratory, radiological, and pathological factors to predict the postoperative recurrence of atypical meningioma (AM).Materials and MethodsA retrospective study of 183 patients with AM was conducted. Patients were randomly divided into a training cohort (n = 128) and an external validation cohort (n = 55). Univariable and multivariable Cox regression analyses, the least absolute shrinkage and selection operator (LASSO) regression analysis, time-dependent receiver operating characteristic (ROC) curve analysis, and evaluation of clinical usage were used to select variables for the final nomogram model.ResultsAfter multivariable Cox analysis, serum fibrinogen >2.95 g/L (hazard ratio (HR), 2.43; 95% confidence interval (CI), 1.05–5.63; p = 0.039), tumor located in skull base (HR, 6.59; 95% CI, 2.46-17.68; p < 0.001), Simpson grades III–IV (HR, 2.73; 95% CI, 1.01–7.34; p = 0.047), tumor diameter >4.91 cm (HR, 7.10; 95% CI, 2.52–19.95; p < 0.001), and mitotic level ≥4/high power field (HR, 2.80; 95% CI, 1.16–6.74; p = 0.021) were independently associated with AM recurrence. Mitotic level was excluded after LASSO analysis, and it did not improve the predictive performance and clinical usage of the model. Therefore, the other four factors were integrated into the nomogram model, which showed good discrimination abilities in training cohort (C-index, 0.822; 95% CI, 0.759–0.885) and validation cohort (C-index, 0.817; 95% CI, 0.716–0.918) and good match between the predicted and observed probability of recurrence-free survival.ConclusionOur study established an integrated model to predict the postoperative recurrence of AM.

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A novel nomogram to predict the overall survival in esthesinoeroblastoma

10.21203/rs.3.rs-22163/v2 ◽

2020 ◽

Author(s):

Lijie Jiang ◽

Tengjiao Lin ◽

Yu Zhang ◽

Wenxiang Gao ◽

Jie Deng ◽

...

Keyword(s):

Overall Survival ◽

Proportional Hazards ◽

Proportional Hazards Model ◽

Validation Cohort ◽

Risk Groups ◽

Cox Proportional Hazards ◽

Cox Proportional Hazards Model ◽

Cancer Center ◽

P Value ◽

Training Cohort

Abstract Background Increasing evidence indicates that the pathology and the modified Kadish system have some influence on the prognosis of esthesioneuroblastoma (ENB). However, an accurate system to combine pathology with a modified Kadish system has not been established. Methods This study aimed to set up and evaluate a model to predict overall survival (OS) accurately in ENB, including clinical characteristics, treatment and pathological variables. We screened the information of patients with ENB between January 1, 1976, and December 30, 2016 from the National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) program as a training cohort. The validation cohort consisted of patients with ENB at Sun Yat-sen University Cancer Center and The First Affiliated Hospital of Sun Yat-sen University in the same period, and 87 patients were identified. The Pearson’s chi-squared test was used to assess significance of clinicopathological and demographic characteristics. We used the Cox proportional hazards model to examine univariate and multivariate analyses. The model coefficients were used to calculate the Hazard ratios (HR) with 95% confidence intervals (CI). Prognostic factors with a p- value < 0.05 in multivariate analysis were included in the nomogram. The concordance index (c-index) and calibration curve were used to evaluate the predictive power of the nomogram. Results The c-index of training cohort and validation cohort are 0.737 (95% CI, 0.709 to 0.765) and 0.791 (95% CI, 0.767 to 0.815) respectively. The calibration curves revealed a good agreement between the nomogram prediction and actual observation regarding the probability of 3-year and 5-year survival. We used a nomogram to calculate the 3-year and 5-year growth probability and stratified patients into three risk groups. Conclusions The nomogram provided the risk group information and identified mortality risk and can serve as a reference for designing a reasonable follow-up plan.

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Integrated immune-related gene signature to predict clinical outcome for patients with luminal B breast cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e12526 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e12526-e12526

Author(s):

Xiaying Kuang ◽

Du Cai ◽

Ying Lin ◽

Feng Gao

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Validation Cohort ◽

Risk Groups ◽

Gene Signature ◽

Breast Cancer Patients ◽

Training Cohort ◽

Luminal B ◽

Immune Related Gene ◽

Immune Related Genes

e12526 Background: Luminal B breast cancer is always routinely treated with chemotherapy and endocrine therapy but heterogeneous with respect to sensitivity to treatment, identification of patients who may most benefit remains a matter of controversy. Immune-related genes (IRGs) was found to be associated with the prognosis of breast cancer. The aim of this study is to evaluate the impact of IRGs in predicting the outcome of luminal B breast cancer patients. Methods: According to the Metabric microarray dataset also as a training cohort, 488 luminal B breast cancer patients were selected for generation of immune-related gene signature (IRGS). Another independent dataset (n=250) of patients with complete prognostic information was analyzed as a validation cohort. Prognostic analysis was assessed to test the predictive value of IRGS. Results: A model of prognostic IRGS containing 12 immune-related genes was developed. In both training and validation cohorts, IRGS significantly stratified luminal B breast cancer patients into immune low- and high-risk groups in terms of disease free survival (DFS, HR=4.95, 95% CI=3.22-7.62, P<0.001 in training cohort, HR=2.47, 95% CI=1.29-4.75, P<0.001 in validation cohort). Multivariate analysis revealed IRGS as an independent prognostic factor (HR=4.96, 95% CI=3.00-8.18, P<0.001 in training cohort, HR=2.56, 95% CI=1.28-5.09, P=0.007 in validation cohort). Furthermore, those 12 genes mostly related with response to chemical, and the expression levels of them were completely opposite in patients of immune low- and high-risk groups. Conclusions: The proposed IRGS is a satisfactory prognostic model for estimating DFS of luminal B breast cancer patients. Further studies are needed to assess the clinical effectiveness of this system in predicting prognosis and treatment options for luminal B breast cancer patients. This work was supported by National Natural Science Foundation of China (No. 81602520), Natural Science Foundation of Guangdong Province (No. 2017A030313596).

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Development and validation of a radiomics nomogram to discriminate advanced pancreatic cancer patients with liver metastases or other metastatic patterns.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.3_suppl.435 ◽

2021 ◽

Vol 39 (3_suppl) ◽

pp. 435-435

Author(s):

Junjie Hang ◽

Lixia Wu

Keyword(s):

Pancreatic Cancer ◽

Liver Metastases ◽

Cancer Patients ◽

Validation Cohort ◽

Advanced Pancreatic Cancer ◽

Region Of Interest ◽

Calibration Plot ◽

Training Cohort ◽

Kaplan Meier ◽

Metastatic Patterns

435 Background: Pancreatic cancer patients with liver metastases had much poorer prognosis than those with other metastatic patterns. This study aimed to develop and validate a radiomics model to discriminate pancreatic cancer patients with liver metastases from patients with other metastatic patterns. Methods: We evaluated 77 patients advanced pancreatic cancer (APC) with different metastatic patterns and performed texture analysis on the region of interest (ROI). 58 patients and 19 patients were allocated randomly into the training cohort and the validation cohort with almost the same proportion of patients with liver metastases. An independent samples t-test was used for initial feature selection in the training cohort. Random Forest Classifier (RFC) was used to construct models based on these features in both cohorts and a radiomics signature (RS) was derived from the model. Then a nomogram was constructed based on RS and CA19-9, and validated with calibration plot and decision curve. The prognostic value of RS was evaluated by Kaplan-Meier methods. Results: A nomogram based on the RS and CA19-9 was constructed and it demonstrated good discrimination in the training cohort (AUC = 0.93) and validation cohort (AUC = 0.81). Kaplan-meier methods showed that patients with RS>0.61 had much poorer OS than patients with RS < 0.61 in both cohorts. Conclusions:This study presents a radiomics nomogram incorporating both RS and CA19-9, which can be used to discriminate advanced pancreatic cancer patients with liver metastases from patients with other metastatic patterns.

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Development and validation of Nomograms for predicting overall survival and Cancer-specific survival in patients with ovarian clear cell carcinoma

Journal of Ovarian Research ◽

10.1186/s13048-020-00727-3 ◽

2020 ◽

Vol 13 (1) ◽

Author(s):

Qian Chen ◽

Shu Wang ◽

Jing-He Lang

Keyword(s):

Overall Survival ◽

Cell Carcinoma ◽

Cox Regression ◽

Validation Cohort ◽

Clear Cell ◽

Clear Cell Carcinoma ◽

Ovarian Clear Cell Carcinoma ◽

Cancer Specific Survival ◽

Training Cohort ◽

Organ Metastasis

Abstract Background Ovarian clear cell carcinoma (OCCC) is a rare histologic type of ovarian cancer. There is a lack of an efficient prognostic predictive tool for OCCC in clinical work. This study aimed to construct and validate nomograms for predicting the overall survival (OS) and cancer-specific survival (CSS) in patients with OCCC. Methods Data of patients with primary diagnosed OCCC in the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2016 was extracted. Prognostic factors were evaluated with LASSO Cox regression and multivariate Cox regression analysis, which were applied to construct nomograms. The performance of the nomogram models was assessed by the concordance index (C-index), calibration plots, decision curve analysis (DCA) and risk subgroup classification. The Kaplan-Meier curves were plotted to compare survival outcomes between subgroups. Results A total of 1541 patients from SEER registries were randomly divided into a training cohort (n = 1079) and a validation cohort (n = 462). Age, laterality, stage, lymph node (LN) dissected, organ metastasis and chemotherapy were independently and significantly associated with OS, while laterality, stage, LN dissected, organ metastasis and chemotherapy were independent risk factors for CSS. Nomograms were developed for the prediction of 3- and 5-year OS and CSS. The C-indexes for OS and CSS were 0.802[95% confidence interval (CI) 0.773–0.831] and 0.802 (0.769–0.835), respectively, in the training cohort, while 0.746 (0.691–0.801) and 0.770 (0.721–0.819), respectively, in the validation cohort. Calibration plots illustrated favorable consistency between the nomogram predicted and actual survival. C-index and DCA curves also indicated better performance of nomogram than the AJCC staging system. Significant differences were observed in the survival curves of different risk subgroups. Conclusions We have constructed predictive nomograms and a risk classification system to evaluate the OS and CSS of OCCC patients. They were validated to be of satisfactory predictive value, and could aid in future clinical practice.

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Uncovering the invisible—prevalence, characteristics, and radiomics feature–based detection of visually undetectable intraprostatic tumor lesions in 68GaPSMA-11 PET images of patients with primary prostate cancer

European Journal of Nuclear Medicine and Molecular Imaging ◽

10.1007/s00259-020-05111-3 ◽

2020 ◽

Author(s):

Constantinos Zamboglou ◽

Alisa S. Bettermann ◽

Christian Gratzke ◽

Michael Mix ◽

Juri Ruf ◽

...

Keyword(s):

Prostate Cancer ◽

Validation Cohort ◽

External Validation ◽

Image Interpretation ◽

Receiver Operating Curve ◽

Training Cohort ◽

Primary Prostate Cancer ◽

Psma Pet ◽

Number Of Patients ◽

Clinically Significant

Abstract Introduction Primary prostate cancer (PCa) can be visualized on prostate-specific membrane antigen positron emission tomography (PSMA-PET) with high accuracy. However, intraprostatic lesions may be missed by visual PSMA-PET interpretation. In this work, we quantified and characterized the intraprostatic lesions which have been missed by visual PSMA-PET image interpretation. In addition, we investigated whether PSMA-PET-derived radiomics features (RFs) could detect these lesions. Methodology This study consists of two cohorts of primary PCa patients: a prospective training cohort (n = 20) and an external validation cohort (n = 52). All patients underwent 68Ga-PSMA-11 PET/CT and histology sections were obtained after surgery. PCa lesions missed by visual PET image interpretation were counted and their International Society of Urological Pathology score (ISUP) was obtained. Finally, 154 RFs were derived from the PET images and the discriminative power to differentiate between prostates with or without visually undetectable lesions was assessed and areas under the receiver-operating curve (ROC-AUC) as well as sensitivities/specificities were calculated. Results In the training cohort, visual PET image interpretation missed 134 tumor lesions in 60% (12/20) of the patients, and of these patients, 75% had clinically significant (ISUP > 1) PCa. The median diameter of the missed lesions was 2.2 mm (range: 1–6). Standard clinical parameters like the NCCN risk group were equally distributed between patients with and without visually missed lesions (p < 0.05). Two RFs (local binary pattern (LBP) size-zone non-uniformality normalized and LBP small-area emphasis) were found to perform excellently in visually unknown PCa detection (Mann-Whitney U: p < 0.01, ROC-AUC: ≥ 0.93). In the validation cohort, PCa was missed in 50% (26/52) of the patients and 77% of these patients possessed clinically significant PCa. The sensitivities of both RFs in the validation cohort were ≥ 0.8. Conclusion Visual PSMA-PET image interpretation may miss small but clinically significant PCa in a relevant number of patients and RFs can be implemented to uncover them. This could be used for guiding personalized treatments.

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