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Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear. Methods: ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions. Results: In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies. Conclusion: The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC. [ABSTRACT FROM AUTHOR]

Objective: To elucidate the clinicopathological characteristics and prognostic factors of poorly differentiated thyroid carcinoma. Method: A total of 24912 thyroid carcinoma patients admitted to the First Medical Center of Chinese People's Liberation Army General Hospital from 2005 to 2020 were retrospectively reviewed. A total of 94 patients (39 males and 55 females, a male-female ratio of 1:1.4) fulfilled the selection criteria. Of these, 73 patients had undergone surgery. The clinical and pathological data were collected from each enrolled patient. Univariate and multivariate Cox regression analyses were performed to determine independent prognostic factors. All analyses were performed with the SPSS version 26.0 and R version 1.2.5033 in the R Studio environment. Results: The specimens included 20 cases of poorly differentiated thyroid carcinoma complicated with papillary thyroid carcinoma, 17 cases complicated with follicular thyroid carcinoma, 34 cases complicated with other pathological types and 23 with a separate entity. The patient demonstrated a large age span, median age was 57 years (range 8-85 years, average 55.20 ± 15.74 years). The survival time of the 94 cases was calculated, and the mean Overall survival time was 33 (range, 1-170) months, and the mean Recurrence-free survival time was 14 (range, 1-90) months. Recurrence-free mortality is related to the age at diagnosis, extrathyroidal extension and Associated thyroid cancer (p<0.05). In contrast, overall mortality is related to the age at diagnosis, sex, extrathyroidal extension, T stage (AJCC 8th), surgery and radiation (p<0.05). Conclusion: Middle-aged and elderly patients are still at high risk for poorly differentiated thyroid carcinoma. The pathologic results of poorly differentiated thyroid carcinoma are varied, and reasonable treatment has an important impact on the prognosis of poorly differentiated thyroid carcinoma. [ABSTRACT FROM AUTHOR]

Background and purpose: To develop a radiomics nomogram based on contrast-enhanced computed tomography (CECT) for preoperative prediction of lymphovascular invasion (LVI) status of esophageal squamous cell carcinoma (ESCC). Materials and methods: The clinical and imaging data of 258 patients with ESCC who underwent surgical resection and were confirmed by pathology from June 2017 to December 2021 were retrospectively analyzed.The clinical imaging features and radiomic features were extracted from arterial-phase CECT. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature selection and signature construction. Multivariate logistic regression analysis was used to develop a radiomics nomogram prediction model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance and clinical effectiveness of the model in preoperative prediction of LVI status. Results: We constructed a radiomics signature based on eight radiomics features after dimensionality reduction. In the training cohort, the area under the curve (AUC) of radiomics signature was 0.805 (95% CI: 0.740-0.860), and in the validation cohort it was 0.836 (95% CI: 0.735-0.911). There were four predictive factors that made up the individualized nomogram prediction model: radiomic signatures, TNRs, tumor lengths, and tumor thicknesses. The accuracy of the nomogram for LVI prediction in the training and validation cohorts was 0.790 and 0.768, respectively, the specificity was 0.800 and 0.618, and the sensitivity was 0.786 and 0.917, respectively. The Delong test results showed that the AUC value of the nomogram model was significantly higher than that of the clinical model and radiomics model in the training and validation cohort(P<0.05). DCA results showed that the radiomics nomogram model had higher overall benefits than the clinical model and the radiomics model. Conclusions: This study proposes a radiomics nomogram based on CECT radiomics signature and clinical image features, which is helpful for preoperative individualized prediction of LVI status in ESCC. [ABSTRACT FROM AUTHOR]

Background: Current prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor’s eccentricity (shape), signal-toclutter ratio (SCR), and volume. Purpose: Conduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer. Methods: This study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor’s eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (<=3+4) were judged as “Clinically Significant” (“Insignificant”). Logistic regression combined eccentricity, SCR, volume to generate probability distribution. Nomograms, DCA used all patients plus training (13 patients) and test (12 patients) sets. Area Under the Curves for (AUC) for Receiver Operator Curves (ROC) and p-values evaluated the performance. Results: Combining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (<0.05) for logistic fit. DCA showed greater net benefit from model fit than univariate analysis, treating “all,” or “none.” Training/test sets achieved comparable AUC but with higher p-values. Conclusions: Performance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS. [ABSTRACT FROM AUTHOR]

Background: The risk and prognosis of pancreatic cancer with lung metastasis (PCLM) are not well-defined. Thus, this study aimed to identify the risk and prognostic factors for these patients, and establish predictive nomogram models. Methods: Patients diagnosed with PCLM between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified using logistic regression and Cox regression analyses. Nomograms were constructed to predict the risk and survival of PCLM, and the area under the curve (AUC), C-index, and calibration curve were used to determine the predictive accuracy and discriminability of the established nomogram, while the decision curve analysis was used to confirm the clinical effectiveness. Results: A total of 11287 cases with complete information were included; 601 (5.3%) patients with PC had lung metastases. Multivariable logistic analysis demonstrated that primary site, histological subtype, and brain, bone, and liver metastases were independent risk factors for lung metastases. We constructed a risk prediction nomogram model for the development of lung metastases among PC patients. The c-index of the established diagnostic nomogram was 0.786 (95%CI 0.726-0.846). Multivariable Cox regression analysis demonstrated that primary site, liver metastases, surgery, and chemotherapy were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS), while bone metastases were independent prognostic factors for CSS. The C-indices for the OS and CSS prediction nomograms were 0.76 (95% CI 0.74-0.78) and 0.76 (95% CI 0.74-0.78), respectively. Based on the AUC of the receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance. Conclusions: The present study identified the risk and prognostic factors of PCLM and further established nomograms, which can help clinicians effectively identify high-risk patients and predict their clinical outcomes. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrastenhanced computed tomography (CE-CT) images. Materials and Methods: The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with highrisk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis. Results: Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram. Conclusion: We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization. [ABSTRACT FROM AUTHOR]

Background: The early diagnosis of occult peritoneal metastasis (PM) remains a challenge due to the low sensitivity on computed tomography (CT) images. Exploratory laparoscopy is the gold standard to confirm PM but should only be proposed in selected patients due to its invasiveness, high cost, and port-site metastasis risk. In this study, we aimed to develop an individualized prediction model to identify occult PM status and determine optimal candidates for exploratory laparoscopy. Method: A total of 622 colorectal cancer (CRC) patients from 2 centers were divided into training and external validation cohorts. All patients' PM status was first detected as negative on CT imaging but later confirmed by exploratory laparoscopy. Multivariate analysis was used to identify independent predictors, which were used to build a prediction model for identifying occult PM in CRC. The concordance index (C-index), calibration plot and decision curve analysis were used to evaluate its predictive accuracy and clinical utility. Results: The C-indices of the model in the development and validation groups were 0.850 (95% CI 0.815-0.885) and 0.794 (95% CI, 0.690-0.899), respectively. The calibration curve showed consistency between the observed and predicted probabilities. The decision curve analysis indicated that the prediction model has a great clinical value between thresholds of 0.10 and 0.72. At a risk threshold of 30%, a total of 40% of exploratory laparoscopies could have been prevented, while still identifying 76.7% of clinically occult PM cases. A dynamic online platform was also developed to facilitate the usage of the proposed model. Conclusions: Our individualized risk model could reduce the number of unnecessary exploratory laparoscopies while maintaining a high rate of diagnosis of clinically occult PM. These results warrant further validation in prospective studies. [ABSTRACT FROM AUTHOR]

Background: Vestibular schwannoma (VS) is the most common benign tumor of the posterior fossa. The recurrence of VS has always received widespread attention. This study aimed to develop a nomogram to predict Recurrence-free survival (RFS) following resection of VS. Methods: A total of 425 patients with VS who underwent resection at the Department of Neurosurgery in Chinese PLA General Hospital between January 2014 and December 2020 were enrolled in this retrospective study. The medical records and follow-up data were collected. Cox regression analysis was used to screen prognostic factors and construct the nomogram. The predictive accuracy and clinical benefits of the nomogram were validated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: The Cox regression analysis revealed that age (HR = 0.96; 95% CI 0.94 - 0.99; p < 0.01), EOR (HR = 4.65; 95% CI 2.22 - 9.74; p < 0.001), and Ki-67 (HR = 1.16; 95% CI 1.09 - 1.23; p < 0.001) were all significantly correlated with recurrence, and they were finally included in the nomogram model. The concordance index of the nomogram was 0.86. The areas under the curve (AUCs) of the nomogram model of 3-, 4- and 5-year were 0.912, 0.865, and 0.809, respectively. A well-fitted calibration curve was also generated for the nomogram model. The DCA curves also indicated that the nomogram model had satisfactory clinical utility compared to the single indicators. Conclusions: We developed a nomogram that has high accuracy in predicting RFS in patients after resection of VS. All of the included prognostic factors are easy to obtain. The nomogram can improve the postoperative management of patients and assist clinicians in individualized clinical treatment. Furthermore, we generated a web-based calculator to facilitate clinical application: https://abc123-123.shinyapps.io/VS-RFS/. [ABSTRACT FROM AUTHOR]

BackgroundVestibular schwannoma (VS) is the most common benign tumor of the posterior fossa. The recurrence of VS has always received widespread attention. This study aimed to develop a nomogram to predict Recurrence-free survival (RFS) following resection of VS.MethodsA total of 425 patients with VS who underwent resection at the Department of Neurosurgery in Chinese PLA General Hospital between January 2014 and December 2020 were enrolled in this retrospective study. The medical records and follow-up data were collected. Cox regression analysis was used to screen prognostic factors and construct the nomogram. The predictive accuracy and clinical benefits of the nomogram were validated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).ResultsThe Cox regression analysis revealed that age (HR = 0.96; 95% CI 0.94 - 0.99; p < 0.01), EOR (HR = 4.65; 95% CI 2.22 - 9.74; p < 0.001), and Ki-67 (HR = 1.16; 95% CI 1.09 - 1.23; p < 0.001) were all significantly correlated with recurrence, and they were finally included in the nomogram model. The concordance index of the nomogram was 0.86. The areas under the curve (AUCs) of the nomogram model of 3-, 4- and 5-year were 0.912, 0.865, and 0.809, respectively. A well-fitted calibration curve was also generated for the nomogram model. The DCA curves also indicated that the nomogram model had satisfactory clinical utility compared to the single indicators.ConclusionsWe developed a nomogram that has high accuracy in predicting RFS in patients after resection of VS. All of the included prognostic factors are easy to obtain. The nomogram can improve the postoperative management of patients and assist clinicians in individualized clinical treatment. Furthermore, we generated a web-based calculator to facilitate clinical application: https://abc123-123.shinyapps.io/VS-RFS/.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear., Methods: ARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions., Results: In our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies., Conclusion: The predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Ying, Ding, Xing, Wang, Pan, Pan, Xiang and Liu.)

BackgroundClinically relevant postoperative pancreatic fistula (CR-POPF) remains a severe and challenging complication of pancreaticoduodenectomy (PD). This study aimed to establish a novel postoperative nomogram-based diagnostic model for the early detection of CR-POPF in patients subjected to PD.MethodsConsecutive patients who underwent PD in Zhongshan Hospital, Fudan University from December 2018 to October 2020 were retrospectively enrolled. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CR-POPF. Then, a novel predictive nomogram was established accordingly.ResultsAmong the consecutive 176 patients who underwent PD, 37 (21.1%) patients developed CR-POPF. Through univariate and multivariate analyses, the drain amylase (P = 0.002), serum creatinine (P = 0.009), and serum C reactive protein (P = 0.045) at postoperative day 1 (POD1) as well as the neutrophil count (P = 0.025) and temperature (P = 0.025) at POD3 were identified as independent risk factors for CR-POPF. Based on this, a novel predictive nomogram containing these factors was constructed to predict the probability of CR-POPF after PD. The formulated nomogram showed better performance to detect CR-POPF after PD with a sensitivity of 0.784, specificity of 0.770, positive predictive value of 0.475, and negative predictive value of 0.930 when compared to other predictors. In addition, the predictive value of the nomogram was assessed by a concordance index of 0.814 (95% CI, 0.736–0.892), which was significantly higher than indicators alone. This was further validated and depicted by decision curve analysis and clinical impact curve.ConclusionThis study established a diagnostic nomogram of postoperative objective parameters that can predict the development of CR-POPF after PD with a good discriminative ability and predictive accuracy.

PurposeOur study aimed to establish and validate prognostic nomograms based on gross tumor volume (GTV) and cervical nodal volume (CNV) for nasopharyngeal carcinoma (NPC) patients treated with two cycles of concurrent chemoradiotherapy (CCRT).MethodsFrom 2012 to 2015, 620 eligible patients who received radical treatment at the Cancer Hospital of Shantou University Medical College were recruited for a nomogram study. Variables were determined in a training set of 463 patients from 2012 to 2014 by X-tile analysis, univariate and multivariate Cox proportional hazard analyses, and the least absolute shrinkage and selection operator (LASSO). Another cohort of 157 patients in 2015 was validated with bootstrap resampling. The concordance index (C-index) and calibration curves were applied to assess its predictive discriminative and accuracy ability, while decision curve analysis (DCA), X-tile analysis and Kaplan–Meier curve for clinical application.ResultsIndependent prognostic variables for overall survival (OS) were age, GTV, CNV, cranial nerve, positive cervical lymph node laterality below the caudal border of cricoid cartilage (LNBC), and were selected for the nomogram. Optimal prognostic factors including Karnofsky performance status (KPS), age, GTV, CNV, LNBC were incorporated in the nomogram for progression-free survival (PFS). In the training set, the C-index of our nomograms for OS and PFS were 0.755 (95% CI, 0.704 to 0.807) and 0.698 (95% CI, 0.652 to 0.744). The calibration curve showed good agreement between nomogram-predicted and actual survival. DCA indicated that our nomograms were of clinical benefit.ConclusionOur nomograms are capable of effective prognostic prediction for patients with NPC.

Background: Clinically relevant postoperative pancreatic fistula (CR-POPF) remains a severe and challenging complication of pancreaticoduodenectomy (PD). This study aimed to establish a novel postoperative nomogram-based diagnostic model for the early detection of CR-POPF in patients subjected to PD. Methods: Consecutive patients who underwent PD in Zhongshan Hospital, Fudan University from December 2018 to October 2020 were retrospectively enrolled. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for CR-POPF. Then, a novel predictive nomogram was established accordingly. Results: Among the consecutive 176 patients who underwent PD, 37 (21.1%) patients developed CR-POPF. Through univariate and multivariate analyses, the drain amylase (P = 0.002), serum creatinine (P = 0.009), and serum C reactive protein (P = 0.045) at postoperative day 1 (POD1) as well as the neutrophil count (P = 0.025) and temperature (P = 0.025) at POD3 were identified as independent risk factors for CR-POPF. Based on this, a novel predictive nomogram containing these factors was constructed to predict the probability of CR-POPF after PD. The formulated nomogram showed better performance to detect CR-POPF after PD with a sensitivity of 0.784, specificity of 0.770, positive predictive value of 0.475, and negative predictive value of 0.930 when compared to other predictors. In addition, the predictive value of the nomogram was assessed by a concordance index of 0.814 (95% CI, 0.736–0.892), which was significantly higher than indicators alone. This was further validated and depicted by decision curve analysis and clinical impact curve. Conclusion: This study established a diagnostic nomogram of postoperative objective parameters that can predict the development of CR-POPF after PD with a good discriminative ability and predictive accuracy. [ABSTRACT FROM AUTHOR]

Purpose: Our study aimed to establish and validate prognostic nomograms based on gross tumor volume (GTV) and cervical nodal volume (CNV) for nasopharyngeal carcinoma (NPC) patients treated with two cycles of concurrent chemoradiotherapy (CCRT). Methods: From 2012 to 2015, 620 eligible patients who received radical treatment at the Cancer Hospital of Shantou University Medical College were recruited for a nomogram study. Variables were determined in a training set of 463 patients from 2012 to 2014 by X-tile analysis, univariate and multivariate Cox proportional hazard analyses, and the least absolute shrinkage and selection operator (LASSO). Another cohort of 157 patients in 2015 was validated with bootstrap resampling. The concordance index (C-index) and calibration curves were applied to assess its predictive discriminative and accuracy ability, while decision curve analysis (DCA), X-tile analysis and Kaplan–Meier curve for clinical application. Results: Independent prognostic variables for overall survival (OS) were age, GTV, CNV, cranial nerve, positive cervical lymph node laterality below the caudal border of cricoid cartilage (LNBC), and were selected for the nomogram. Optimal prognostic factors including Karnofsky performance status (KPS), age, GTV, CNV, LNBC were incorporated in the nomogram for progression-free survival (PFS). In the training set, the C-index of our nomograms for OS and PFS were 0.755 (95% CI, 0.704 to 0.807) and 0.698 (95% CI, 0.652 to 0.744). The calibration curve showed good agreement between nomogram-predicted and actual survival. DCA indicated that our nomograms were of clinical benefit. Conclusion: Our nomograms are capable of effective prognostic prediction for patients with NPC. [ABSTRACT FROM AUTHOR]

Background/AimsPancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations.Methods1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set.ResultsPrimary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8th AJCC staging system, and achieved clinical validity.ConclusionsPHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery.

Background/Aims: Pancreatic ductal adenocarcinoma (PDAC) can occur in different parts of the pancreas. This study aimed to identify clinicopathological characteristics independently correlated with the prognosis of PDAC of the pancreatic head/uncinate (PHC) or body-tail (PBTC), and to develop novel nomograms for predicting cancer-specific survival (CSS) according to different primary cancer locations. Methods: 1160 PDAC patients were retrospectively enrolled and assigned to training and test sets with each set divided into PHC and PBTC groups. Comparative analysis of clinicopathologic characteristics, survival analysis, and multivariate analysis were performed. Independent factors were identified and used for constructing nomograms. The performance of the nomograms was validated in the test set. Results: Primary tumor location was an independent risk factor for prognosis of PDAC after surgery. Specially, gender, fasting blood glucose, and preoperative cancer antigen 19-9 were significantly associated with prognosis of PHC, whereas age, body mass index, and lymph nodes were significantly correlated with the prognosis of PBTC. A significant difference in prognosis was found between PHC and PBTC in stage Ia and stage III. Three nomograms were established for predicting the prognosis for PDAC, PHC, and PBTC. Notably, these nomograms were calibrated modestly (c-indexes of 0.690 for PDAC, 0.669 for PHC, and 0.704 for PBTC), presented better accuracy and reliability than the 8th AJCC staging system, and achieved clinical validity. Conclusions: PHC and PBTC share the differential clinical-pathological characteristics and survival. The nomograms show good performance for predicting prognosis in PHC and PBTC. Therefore, these nomograms hold potential as novel approaches for predicting survival of PHC and PBTC patients after surgery. [ABSTRACT FROM AUTHOR]

BackgroundEuropean Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa).ObjectivesTo compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC.Design, Setting, and ParticipantsSingle-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI.Outcome Measurements and Statistical AnalysisPatients’ probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models.Results and LimitationThe FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator.ConclusionsmpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators

Background: European Association of Urology (EAU) guidelines recommend using risk-calculators (RCs), imaging or additional biomarkers in asymptomatic men at risk of prostate cancer (PCa). Objectives: To compare the performance of mpMRI, a RC we recently developed and two commonly used RC not including mpMRI in predicting the risk of PCa, as well as the added value of mpMRI to each RC. Design, Setting, and Participants: Single-center retrospective study evaluating 221 biopsy-naïve patients who underwent prebiopsy mpMRI. Outcome Measurements and Statistical Analysis: Patients' probabilities of any PCa and clinically significant PCa (csPC, defined as Gleason-Score ≥3 + 4) were computed according to mpMRI, European Randomized Study of Screening for Prostate Cancer RC (ERSPC-RC), the Prostate Biopsy Collaborative Group RC (PBCG-RC) and the Foggia Prostate Cancer RC (FPC-RC). Logistic regression, AUC, and Decision curve analysis (DCA) were used to assess the accuracy of tested models. Results and Limitation: The FPC-RC outperformed mpMRI in diagnosing both any PCa (AUC 0.76 vs 0.69) and csPCa (AUC 0.80 vs 0.75). Conversely mpMRI showed a higher accuracy in predicting any PCa compared to the PBCG-RC and the ERSPC-RC but similar performances in predicting csPCa. At multivariable analysis predicting csPCa and any PCa, the addition of mpMRI findings improved the accuracy of each calculator. DCA showed that the FPC-RC provided a greater net benefit than mpMRI and the other RCs. The addition of mpMRI findings improved the net benefit provided by each calculator. Conclusions: mpMRI was outperformed by the novel FPC-RC and showed similar performances compared to the PBCG and ERSPC RCs in predicting csPCa. The addition of mpMRI findings improved the diagnostic accuracy of each of these calculators [ABSTRACT FROM AUTHOR]

Objective: Gallbladder cancer (GBC) is one of the most aggressive malignant tumors, and there is no effective and convenient method for predicting cancer-specific survival (CSS). We aim to develop a novel nomogram staging system based on the positive lymph node ratio (pLNR) for GBC patients.Methods:A total of 1,356 patients enrolled in the study. We evaluated the prognostic value of the pLNR and built a prognostic nomogram staging system based on the pLNR in the training cohort. The concordance index and calibration plots were used to evaluate model discrimination. The predictive accuracy and clinical value of the nomograms were measured by decision curve analysis (DCA). The CSS nomogram was further validated in an internal validation cohort.Results:The pLNR was an independent prognostic factor for CSS based on Cox regression analyses. A prognostic nomogram that combined T classification, pLNR, M classification, histologic grade, live metastasis, and tumor size was formulated with a c-index of 0.763 (95% CI, 0.728–0.798), while the c-indexes for the staging system of AJCC 8th, 7th, and 6th for CSS prediction were 0.718, 0.718, and 0.717, respectively. The calibration curves showed perfect agreement. The DCA showed that the nomogram provided substantial clinical value. The nomogram (the AUCs for 1, 3, and 5 years were 0.693, 0.716, and 0.726, respectively,) showed high prognostic accuracy.Conclusion:We have developed a formulated nomogram staging system based on the pLNR that allows more accurate individualized predictions of CSS for resected GBC patients than the AJCC staging systems.

Objective: Gallbladder cancer (GBC) is one of the most aggressive malignant tumors, and there is no effective and convenient method for predicting cancer-specific survival (CSS). We aim to develop a novel nomogram staging system based on the positive lymph node ratio (pLNR) for GBC patients. Methods: A total of 1,356 patients enrolled in the study. We evaluated the prognostic value of the pLNR and built a prognostic nomogram staging system based on the pLNR in the training cohort. The concordance index and calibration plots were used to evaluate model discrimination. The predictive accuracy and clinical value of the nomograms were measured by decision curve analysis (DCA). The CSS nomogram was further validated in an internal validation cohort. Results: The pLNR was an independent prognostic factor for CSS based on Cox regression analyses. A prognostic nomogram that combined T classification, pLNR, M classification, histologic grade, live metastasis, and tumor size was formulated with a c-index of 0.763 (95% CI, 0.728–0.798), while the c-indexes for the staging system of AJCC 8th, 7th, and 6th for CSS prediction were 0.718, 0.718, and 0.717, respectively. The calibration curves showed perfect agreement. The DCA showed that the nomogram provided substantial clinical value. The nomogram (the AUCs for 1, 3, and 5 years were 0.693, 0.716, and 0.726, respectively,) showed high prognostic accuracy. Conclusion: We have developed a formulated nomogram staging system based on the pLNR that allows more accurate individualized predictions of CSS for resected GBC patients than the AJCC staging systems. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) frequently occurs in cirrhosis and closely relates to poor prognosis of cirrhotic patients. Alpha-fetoprotein (AFP) is the most widely used biomarker in HCC diagnosis but not sensitive and specific to detect HCC at low AFP levels. In order to enhance the ability of AFP to detect HCC developed on cirrhosis, we attempted to combine AFP with conventional clinical metrics to develop a simple and effective method for identifying cirrhotic patients with complicating HCC at various AFP levels. Methods: Cirrhotic patients with or without HCC hospitalized to receive therapy for the first time were recruited and their clinical data were retrospectively collected. A model for diagnosing HCC was developed with routine clinical metrics and AFP by binary logistic regression analysis and internally validated. The goodness of fit, diagnostic accuracy and clinical usefulness of the model were evaluated using a calibration curve, the area under the receiver operating characteristic curve (AUROC) and a decision curve analysis, respectively. Results: A total of 574 patients with cirrhosis mainly caused by hepatitis B were recruited in this study, including 286 cases of simple cirrhosis (LC) and 288 cases of cirrhosis with HCC (LCC) (124 AFP-negative), with an average age of 53.2 ± 12.1 years and 81.4% males. Twelve of the 19 clinical metrics (age, gender, AFP, liver function tests, serum electrolytes, and coagulation tests) significantly differed between the LC and LCC groups. A model was successfully developed with age, AFP, Na+, Cl−, alkaline phosphatase, and activated partial thromboplastin time, which exhibited good performance in diagnosing LCC, with an AUROC of 0.918 (95%CI 0.895–0.940), 82.3% sensitivity, 89.5% specificity, and 85.9% accuracy for all patients, which were much higher values than those for AFP [0.846 (95%CI 0.815–0.878), 72.9, 81.5, and 77.2%, respectively]. For cirrhotic patients complicated with AFP-negative HCC, the model showed an AUROC of 0.854 (95%CI 0.812–0.896), 68.5% sensitivity, 86.6% specificity, and 80.0% accuracy. A high net benefit could be obtained in clinical decision making according to the model. Conclusion: A diagnostic model combining simple clinical metrics with AFP is valuable for the identification of cirrhotic patients complicating HCC with various AFP levels. [ABSTRACT FROM AUTHOR]

Background: Metastatic cervical cancer (mCEC) is the end stage of cervical cancer. This study aimed to establish and validate a nomogram to predict the overall survival (OS) of mCEC patients.Methods: We investigated the Surveillance, Epidemiology, and End Results (SEER) database for mCEC patients diagnosed between 2010 and 2014. Univariate and multivariable Cox analyses was performed to select the clinically important predictors of OS when developing the nomogram. The performance of nomogram was validated with Harrell's concordance index (C-index), calibration curves, receiver operating characteristic curve (ROC), and decision curve analysis (DCA).Results: One thousand two hundred and fifty-two mCEC patients were included and were divided into training (n = 880) and independent validation (n = 372) cohorts. Age, race, pathological type, histology grade, radiotherapy, and chemotherapy were independent predictors of OS and used to develop the nomogram for predicting 1- and 3-year OS. This nomogram had a C-index of 0.753 (95% confidence interval [CI]: 0.780–0.726) and 0.751 (95% CI: 0.794–0.708) in the training and the validation cohorts, respectively. Internal and external calibration curves indicated satisfactory agreement between nomogram prediction and actual survival, and DCA indicated its clinical usefulness. Furthermore, a risk stratification system was established that was able to accurately stratify mCEC patients into three risk subgroups with significantly different prognosis.Conclusions: We constructed the first nomogram and corresponding risk classification system to predict the OS of mCEC patients. These tools showed satisfactory accuracy, and clinical utility, and could aid in patient counseling and individualized clinical decision-making.

Background: Metastatic cervical cancer (mCEC) is the end stage of cervical cancer. This study aimed to establish and validate a nomogram to predict the overall survival (OS) of mCEC patients. Methods: We investigated the Surveillance, Epidemiology, and End Results (SEER) database for mCEC patients diagnosed between 2010 and 2014. Univariate and multivariable Cox analyses was performed to select the clinically important predictors of OS when developing the nomogram. The performance of nomogram was validated with Harrell's concordance index (C-index), calibration curves, receiver operating characteristic curve (ROC), and decision curve analysis (DCA). Results: One thousand two hundred and fifty-two mCEC patients were included and were divided into training (n = 880) and independent validation (n = 372) cohorts. Age, race, pathological type, histology grade, radiotherapy, and chemotherapy were independent predictors of OS and used to develop the nomogram for predicting 1- and 3-year OS. This nomogram had a C-index of 0.753 (95% confidence interval [CI]: 0.780–0.726) and 0.751 (95% CI: 0.794–0.708) in the training and the validation cohorts, respectively. Internal and external calibration curves indicated satisfactory agreement between nomogram prediction and actual survival, and DCA indicated its clinical usefulness. Furthermore, a risk stratification system was established that was able to accurately stratify mCEC patients into three risk subgroups with significantly different prognosis. Conclusions: We constructed the first nomogram and corresponding risk classification system to predict the OS of mCEC patients. These tools showed satisfactory accuracy, and clinical utility, and could aid in patient counseling and individualized clinical decision-making. [ABSTRACT FROM AUTHOR]

Objective: To elucidate the clinicopathological characteristics and prognostic factors of poorly differentiated thyroid carcinoma., Method: A total of 24912 thyroid carcinoma patients admitted to the First Medical Center of Chinese People's Liberation Army General Hospital from 2005 to 2020 were retrospectively reviewed. A total of 94 patients (39 males and 55 females, a male-female ratio of 1:1.4) fulfilled the selection criteria. Of these, 73 patients had undergone surgery. The clinical and pathological data were collected from each enrolled patient. Univariate and multivariate Cox regression analyses were performed to determine independent prognostic factors. All analyses were performed with the SPSS version 26.0 and R version 1.2.5033 in the R Studio environment., Results: The specimens included 20 cases of poorly differentiated thyroid carcinoma complicated with papillary thyroid carcinoma, 17 cases complicated with follicular thyroid carcinoma, 34 cases complicated with other pathological types and 23 with a separate entity. The patient demonstrated a large age span, median age was 57 years (range 8-85 years, average 55.20 ± 15.74 years). The survival time of the 94 cases was calculated, and the mean Overall survival time was 33 (range, 1-170) months, and the mean Recurrence-free survival time was 14 (range, 1-90) months. Recurrence-free mortality is related to the age at diagnosis, extrathyroidal extension and Associated thyroid cancer (p<0.05). In contrast, overall mortality is related to the age at diagnosis, sex, extrathyroidal extension, T stage (AJCC 8th), surgery and radiation (p<0.05)., Conclusion: Middle-aged and elderly patients are still at high risk for poorly differentiated thyroid carcinoma. The pathologic results of poorly differentiated thyroid carcinoma are varied, and reasonable treatment has an important impact on the prognosis of poorly differentiated thyroid carcinoma., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Wan, Wang, Yao, Li, Miao, Jian, Huang, Lai, Li and Tian.)

Background and Purpose: To develop a radiomics nomogram based on contrast-enhanced computed tomography (CECT) for preoperative prediction of lymphovascular invasion (LVI) status of esophageal squamous cell carcinoma (ESCC)., Materials and Methods: The clinical and imaging data of 258 patients with ESCC who underwent surgical resection and were confirmed by pathology from June 2017 to December 2021 were retrospectively analyzed.The clinical imaging features and radiomic features were extracted from arterial-phase CECT. The least absolute shrinkage and selection operator (LASSO) regression model was used for radiomics feature selection and signature construction. Multivariate logistic regression analysis was used to develop a radiomics nomogram prediction model. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the performance and clinical effectiveness of the model in preoperative prediction of LVI status., Results: We constructed a radiomics signature based on eight radiomics features after dimensionality reduction. In the training cohort, the area under the curve (AUC) of radiomics signature was 0.805 (95% CI: 0.740-0.860), and in the validation cohort it was 0.836 (95% CI: 0.735-0.911). There were four predictive factors that made up the individualized nomogram prediction model: radiomic signatures, TNRs, tumor lengths, and tumor thicknesses.The accuracy of the nomogram for LVI prediction in the training and validation cohorts was 0.790 and 0.768, respectively, the specificity was 0.800 and 0.618, and the sensitivity was 0.786 and 0.917, respectively. The Delong test results showed that the AUC value of the nomogram model was significantly higher than that of the clinical model and radiomics model in the training and validation cohort(P<0.05). DCA results showed that the radiomics nomogram model had higher overall benefits than the clinical model and the radiomics model., Conclusions: This study proposes a radiomics nomogram based on CECT radiomics signature and clinical image features, which is helpful for preoperative individualized prediction of LVI status in ESCC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest, (Copyright © 2023 Wang, Bai, Huang, Zhang and Chen.)

Background: Current prostate cancer evaluation can be inaccurate and burdensome. To help non-invasive prostate tumor assessment, recent algorithms applied to spatially registered multi-parametric (SRMP) MRI extracted novel clinically relevant metrics, namely the tumor's eccentricity (shape), signal-to-clutter ratio (SCR), and volume., Purpose: Conduct a pilot study to predict the risk of developing clinically significant prostate cancer using nomograms and employing Decision Curves Analysis (DCA) from the SRMP MRI-based features to help clinicians non-invasively manage prostate cancer., Methods: This study retrospectively analyzed 25 prostate cancer patients. MP-MRI (T1, T2, diffusion, dynamic contrast-enhanced) were resized, translated, and stitched to form SRMP MRI. Target detection algorithm [adaptive cosine estimator (ACE)] applied to SRMP MRI determines tumor's eccentricity, noise reduced SCR (by regularizing or eliminating principal components (PC) from the covariance matrix), and volume. Pathology assessed wholemount prostatectomy for Gleason score (GS). Tumors with GS >=4+3 (<=3+4) were judged as "Clinically Significant" ("Insignificant"). Logistic regression combined eccentricity, SCR, volume to generate probability distribution. Nomograms, DCA used all patients plus training (13 patients) and test (12 patients) sets. Area Under the Curves for (AUC) for Receiver Operator Curves (ROC) and p-values evaluated the performance., Results: Combining eccentricity (0.45 ACE threshold), SCR (3, 4 PCs), SCR (regularized, modified regularization) with tumor volume (0.65 ACE threshold) improved AUC (>0.70) for ROC curves and p-values (<0.05) for logistic fit. DCA showed greater net benefit from model fit than univariate analysis, treating "all," or "none." Training/test sets achieved comparable AUC but with higher p-values., Conclusions: Performance of nomograms and DCA based on metrics derived from SRMP-MRI in this pilot study were comparable to those using prostate serum antigen, age, and PI-RADS., Competing Interests: RM works for Oncoscore. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Mayer, Turkbey, Choyke and Simone.)

Background: The risk and prognosis of pancreatic cancer with lung metastasis (PCLM) are not well-defined. Thus, this study aimed to identify the risk and prognostic factors for these patients, and establish predictive nomogram models., Methods: Patients diagnosed with PCLM between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified using logistic regression and Cox regression analyses. Nomograms were constructed to predict the risk and survival of PCLM, and the area under the curve (AUC), C-index, and calibration curve were used to determine the predictive accuracy and discriminability of the established nomogram, while the decision curve analysis was used to confirm the clinical effectiveness., Results: A total of 11287 cases with complete information were included; 601 (5.3%) patients with PC had lung metastases. Multivariable logistic analysis demonstrated that primary site, histological subtype, and brain, bone, and liver metastases were independent risk factors for lung metastases. We constructed a risk prediction nomogram model for the development of lung metastases among PC patients. The c-index of the established diagnostic nomogram was 0.786 (95%CI 0.726-0.846). Multivariable Cox regression analysis demonstrated that primary site, liver metastases, surgery, and chemotherapy were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS), while bone metastases were independent prognostic factors for CSS. The C-indices for the OS and CSS prediction nomograms were 0.76 (95% CI 0.74-0.78) and 0.76 (95% CI 0.74-0.78), respectively. Based on the AUC of the receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance., Conclusions: The present study identified the risk and prognostic factors of PCLM and further established nomograms, which can help clinicians effectively identify high-risk patients and predict their clinical outcomes., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Yao, Tu, Zhou, Huang, Liu and Xue.)

Objective: This study aimed to establish a combined radiomics nomogram to preoperatively predict the risk categorization of thymomas by using contrast-enhanced computed tomography (CE-CT) images., Materials and Methods: The clinical, pathological, and CT data of 110 patients with thymoma (50 patients with low-risk thymomas and 60 patients with high-risk thymomas) collected in our Hospital from July 2017 to March 2022 were retrospectively analyzed. The study subjects were randomly divided into the training set (n = 77) and validation set (n = 33) in a 7:3 ratio. Radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) algorithm was performed to select 13 representative features. Five models, including logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and gradient boosting decision tree (GBDT) were constructed to predict thymoma risks based on these features. A combined radiomics nomogram was further established based on the clinical factors and radiomics scores. The performance of the models was evaluated using receiver operating characteristic (ROC) curve, DeLong tests, and decision curve analysis., Results: Maximum tumor diameter and boundary were selected to build the clinical factors model. Thirteen features were acquired by LASSO algorithm screening as the optimal features for machine learning model construction. The LR model exhibited the highest AUC value (0.819) among the five machine learning models in the validation set. Furthermore, the radiomics nomogram combining the selected clinical variables and radiomics signature predicted the categorization of thymomas at different risks more effectively (the training set, AUC = 0.923; the validation set, AUC = 0.870). Finally, the calibration curve and DCA were utilized to confirm the clinical value of this combined radiomics nomogram., Conclusion: We demonstrated the clinical diagnostic value of machine learning models based on CT semantic features and the selected clinical variables, providing a non-invasive, appropriate, and accurate method for preoperative prediction of thymomas risk categorization., Competing Interests: Author HL was employed by company Yizhun Medical AI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dong, Xiong, Lei, Wang, Liu, Liu, Zou, Fan and Qiu.)

Background: The early diagnosis of occult peritoneal metastasis (PM) remains a challenge due to the low sensitivity on computed tomography (CT) images. Exploratory laparoscopy is the gold standard to confirm PM but should only be proposed in selected patients due to its invasiveness, high cost, and port-site metastasis risk. In this study, we aimed to develop an individualized prediction model to identify occult PM status and determine optimal candidates for exploratory laparoscopy., Method: A total of 622 colorectal cancer (CRC) patients from 2 centers were divided into training and external validation cohorts. All patients' PM status was first detected as negative on CT imaging but later confirmed by exploratory laparoscopy. Multivariate analysis was used to identify independent predictors, which were used to build a prediction model for identifying occult PM in CRC. The concordance index (C-index), calibration plot and decision curve analysis were used to evaluate its predictive accuracy and clinical utility., Results: The C-indices of the model in the development and validation groups were 0.850 (95% CI 0.815-0.885) and 0.794 (95% CI, 0.690-0.899), respectively. The calibration curve showed consistency between the observed and predicted probabilities. The decision curve analysis indicated that the prediction model has a great clinical value between thresholds of 0.10 and 0.72. At a risk threshold of 30%, a total of 40% of exploratory laparoscopies could have been prevented, while still identifying 76.7% of clinically occult PM cases. A dynamic online platform was also developed to facilitate the usage of the proposed model., Conclusions: Our individualized risk model could reduce the number of unnecessary exploratory laparoscopies while maintaining a high rate of diagnosis of clinically occult PM. These results warrant further validation in prospective studies., Clinical Trial Registration: https://www.isrctn.com, identifier ISRCTN76852032., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zhang, Qin, Li, Zhang, Luo, Wu, Li, Han, Wang and Wang.)