Preoperative nomograms incorporating magnetic resonance imaging and spectroscopy for prediction of insignificant prostate cancer

Study Type – Prognosis (case series) Level of Evidence 4 What's known on the subject? and What does the study add? Nomograms are available that combine clinical and biopsy findings to predict the probability of pathologically insignificant prostate cancer in patients with clinically low‐risk disease. Based on data from patients with Gleason score 6, clinical stage ≤ T2a and PSA <20 ng/ml, our group developed the first nomogram models for predicting insignificant prostate cancer that incorporated clinical data, detailed biopsy data and findings from MRI or MRI/MRSI (BJU Int. 2007;99(4):786–93). When tested retrospectively, these MR models performed significantly better than standard clinical models with and without detailed biopsy data. We prospectively validated the previously published MR‐based nomogram models in a population of patients with Gleason score 6, clinical stage ≤ T2a and PSA <10 ng/ml. Based on data from this same population, we also developed two new models for predicting insignificant prostate cancer that combine MR findings and clinical data without detailed biopsy data. Upon initial testing, the new MR models performed significantly better than a clinical model lacking detailed biopsy data.

OBJECTIVES

• ? To validate previously published nomograms for predicting insignificant prostate cancer (PCa) that incorporate clinical data, percentage of biopsy cores positive (%BC+) and magnetic resonance imaging (MRI) or MRI/MR spectroscopic imaging (MRSI) results.
• ? We also designed new nomogram models incorporating magnetic resonance results and clinical data without detailed biopsy data. Nomograms for predicting insignificant PCa can help physicians counsel patients with clinically low‐risk disease who are choosing between active surveillance and definitive therapy.

PATIENTS AND METHODS

• ? In total, 181 low‐risk PCa patients (clinical stage T1c–T2a, prostate‐specific antigen level <10 ng/mL, biopsy Gleason score of 6) had MRI/MRSI before surgery.
• ? For MRI and MRI/MRSI, the probability of insignificant PCa was recorded prospectively and independently by two radiologists on a scale from 0 (definitely insignificant) to 3 (definitely significant PCa).
• ? Insignificant PCa was defined on surgical pathology.
• ? There were four models incorporating MRI or MRI/MRSI and clinical data with and without %BC+ that were compared with a base clinical model without %BC and a more comprehensive clinical model with %BC+. Prediction accuracy was assessed using areas under receiver–operator characteristic curves.

RESULTS

• ? At pathology, 27% of patients had insignificant PCa, and the Gleason score was upgraded in 56.4% of patients.
• ? For both readers, all magnetic resonance models performed significantly better than the base clinical model (P ≤ 0.05 for all) and similarly to the more comprehensive clinical model.

CONCLUSIONS

• ? Existing models incorporating magnetic resonance data, clinical data and %BC+ for predicting the probability of insignificant PCa were validated.
• ? All MR‐inclusive models performed significantly better than the base clinical model.

     

Establishing and validating predictive nomograms for lateral pelvic lymph node metastasis in patients with rectal cancer based on radiologic factors and clinicopathologic characteristics

IntroductionIt is critical to accurately predict the occurrence of lateral pelvic lymph node (LPN) metastasis. Currently, verified predictive tools are unavailable. This study aims to establish nomograms for predicting LPN metastasis in patients with rectal cancer who received or did not receive neoadjuvant chemoradiotherapy (nCRT).Materials and methodsWe carried out a retrospective study of patients with rectal cancer and clinical LPN metastasis who underwent total mesorectal excision (TME) and LPN dissection (LPND) from January 2012 to December 2019 at 3 institutions. We collected and evaluated their clinicopathologic and radiologic features, and constructed nomograms based on the multivariable logistic regression models.ResultsA total of 472 eligible patients were enrolled into the non-nCRT cohort (n = 312) and the nCRT cohort (n = 160). We established nomograms using variables from the multivariable logistic regression models in both cohorts. In the non-nCRT cohort, the variables included LPN short diameter, cT stage, cN stage, histologic grade, and malignant features, and the C-index was 0.930 in the training cohort and 0.913 in the validation cohort. In the nCRT cohort, the variables included post-nCRT LPN short diameter, ycT stage, ycN stage, histologic grade, and post-nCRT malignant features, and the C-index was 0.836 in the training dataset and 0.827 in the validation dataset. The nomograms in both cohorts were moderately calibrated and well-validated.ConclusionsWe established nomograms for patients with rectal cancer that accurately predict LPN metastasis. The performance of the nomograms in both cohorts was high and well-validated.     

A Nomogram for Predicting Progression-free Survival in Patients with Endometrial Cancer

AimsEndometrial cancer is one of the most widely known gynaecological malignancies that lacks a prognostic prediction model. This study aimed to develop a nomogram to predict progression-free survival (PFS) in patients with endometrial cancer.Materials and methodsInformation for endometrial cancer patients diagnosed and treated from 1 January 2005 to 30 June 2018 was collected. The Kaplan–Meier survival analysis and multivariate Cox regression analysis were carried out to determine the independent risk factors and a nomogram was constructed by R based on analytical factors. Internal and external validation were then carried out to predict the probability of 3- and 5-year PFS.ResultsIn total, 1020 patients with endometrial cancer were included in the study and the relationship between 25 factors and prognosis was analysed. Postmenopause (hazard ratio = 2.476, 95% confidence interval 1.023–5.994), lymph node metastasis (hazard ratio = 6.242, 95% confidence interval 2.815–13.843), lymphovascular space invasion (hazard ratio = 4.263, 95% confidence interval 1.802–10.087), histological type (hazard ratio = 2.713, 95% confidence interval 1.374–5.356), histological differentiation (hazard ratio = 2.601, 95% confidence interval 1.141–5.927) and parametrial involvement (hazard ratio = 3.596, 95% confidence interval 1.622–7.973) were found to be independent prognostic risk factors; these factors were selected to establish a nomogram. The consistency index for 3-year PFS were 0.88 (95% confidence interval 0.81–0.95) in the training cohort and 0.93 (95% confidence interval 0.87–0.99) in the verification set. The areas under the receiver operating characteristic curve for the 3- and 5-year PFS predictions are 0.891 and 0.842 in the training set; the same conclusion also appeared in the verification set [0.835 (3-year), 0.803(5-year)].ConclusionsThis study established a prognostic nomogram for endometrial cancer that provides a more individualised and accurate estimation of PFS for patients, which will help physicians make follow-up strategies and risk stratification.     

Nomogram to diagnosis of obstructive sleep apnoea-hypopnoea syndrome in high-risk Chinese adult patients

Introduction

Many scales are designed to screen for obstructive sleep apnoea-hypopnoea syndrome (OSAHS); however, there is a lack of an efficiently and easily diagnostic tool, especially for Chinese. Therefore, we conduct a cross-sectional study in China to develop and validate an efficient and simple clinical diagnostic model to help screen patients at risk of OSAHS.

Methods

This study based on 782 high-risk patients (aged >18 years) admitted to the Sleep Medicine department of the Sixth Affiliated Hospital, Sun Yat-sen University from 2015 to 2021. Totally 34 potential predictors were evaluated. We divided all patients into training and validation dataset to develop diagnostic model. The univariable and multivariable logistic regression model were used to build model and nomogram was finally built.

Results

Among 602 high-risk patients with median age of 46 (37, 56) years, 23.26% were women. After selecting using the univariate logistic model, 15 factors were identified. We further used the stepwise method to build the final model with five factors: age, BMI, total bilirubin levels, high Berlin score, and symptom of morning dry mouth or mouth breathing. The AUC was 0.780 (0.711, 0.848), with sensitivity of 0.848 (0.811, 0.885), specificity of 0.629 (0.509, 0.749), accuracy of 0.816 (0.779, 0.853). The discrimination ability had been verified in the validation dataset. Finally, we established a nomogram model base on the above final model.

Conclusion

We developed and validated a predictive model with five easily acquire factors to diagnose OSAHS patient in high-risk population with well discriminant ability. Accordingly, we finally build the nomogram model.