Malignancy risk factors and prognostic variables of pancreatic mucinous cystic neoplasms in Chinese patients

BACKGROUND Pancreatic mucinous cystic neoplasms(MCNs)represent one of the precursor lesions of pancreatic ductal adenocarcinoma,and their detection has been facilitated by advances in preoperative imaging.Due primarily to the rarity of MCNs,however,there is limited knowledge regarding the prognostic variables and high-risk factors for malignant transformation.A more comprehensive and nuanced approach is necessary to fill this gap and provide a basis for improved treatment decisions and patient outcomes.AIM To investigate the high-risk factors associated with malignant MCNs and to explore the prognostic factors of MCN with associated invasive carcinoma(MCNAIC).METHODS All cases of resected MCNs from a single high-volume institution between January 2012 and January 2022 were retrospectively reviewed.Only cases with ovarian-type stroma verified by progesterone receptor staining were included.Preoperative features,histological findings and postoperative course were documented.Multivariate logistic regression was employed to investigate variables related to malignancy.Survival analysis was performed using the Kaplan-Meier curve,and the prognostic factors were assessed to evaluate the postoperative course of patients with MCN-AIC.RESULTS Among the 48 patients,36 had benign MCNs,and 12 had malignant MCNs(1 high-grade atypical hyperplasia and 11 MCN-AIC).Age,tumour size,presence of solid components or mural nodules and pancreatic duct dilatation were identified as independent risk factors associated with malignancy.The follow-up period ranged from 12 mo to 120 mo,with a median overall survival of 58.2 mo.Only three patients with MCN-AIC died,and the 5-year survival rate was 70.1%.All 11 cases of MCN-AIC were stage I,and extracapsular invasion was identified as a prognostic factor for poorer outcomes.CONCLUSION The risk factors independently associated with malignant transformation of MCNs included age,tumour size,presence of solid components or mural nodules,and pancreatic duct dilatation.Our study also revealed that encapsulated invasion was a favourable prognostic factor in MCN-AIC patients.

Online calculator for predicting the risk of malignancy in patients with pancreatic cystic neoplasms: A multicenter, retrospective study

BACKGROUND Efficient and practical methods for predicting the risk of malignancy in patients with pancreatic cystic neoplasms(PCNs)are lacking.AIM To establish a nomogram-based online calculator for predicting the risk of malignancy in patients with PCNs.METHODS In this study,the clinicopathological data of target patients in three medical centers were analyzed.The independent sample t-test,Mann–Whitney U test or chi-squared test were used as appropriate for statistical analysis.After univariable and multivariable logistic regression analysis,five independent factors were screened and incorporated to develop a calculator for predicting the risk of malignancy.Finally,the concordance index(C-index),calibration,area under the curve,decision curve analysis and clinical impact curves were used to evaluate the performance of the calculator.RESULTS Enhanced mural nodules[odds ratio(OR):4.314;95%confidence interval(CI):1.618–11.503,P=0.003],tumor diameter≥40 mm(OR:3.514;95%CI:1.138–10.849,P=0.029),main pancreatic duct dilatation(OR:3.267;95%CI:1.230–8.678,P=0.018),preoperative neutrophil-to-lymphocyte ratio≥2.288(OR:2.702;95%CI:1.008–7.244,P=0.048],and preoperative serum CA19-9 concentration≥34 U/mL(OR:3.267;95%CI:1.274–13.007,P=0.018)were independent risk factors for a high risk of malignancy in patients with PCNs.In the training cohort,the nomogram achieved a C-index of 0.824 for predicting the risk of malignancy.The predictive ability of the model was then validated in an external cohort(C-index:0.893).Compared with the risk factors identified in the relevant guidelines,the current model showed better predictive performance and clinical utility.CONCLUSION The calculator demonstrates optimal predictive performance for identifying the risk of malignancy,potentially yielding a personalized method for patient selection and decision-making in clinical practice.

Ultrasound Features Improve Diagnostic Performance of Ovarian Cancer Predictors in Distinguishing Benign and Malignant Ovarian Tumors

To determine whether ultrasound features can improve the diagnostic performance of tumor markers in distinguishing ovarian tumors,we enrolled 719 patients diagnosed as having ovarian tumors at Nanfang Hospital from September 2014 to November 2016.Age,menopausal status,histopathology,the International Federation of Gynecology and Obstetrics(FIGO)stages,tumor biomarker levels,and detailed ultrasound reports of patients were collected.The area under the curve(AUC),sensitivity,and specificity of the bellow-mentioned predictors were analyzed using the receiver operating characteristic curve.Of the 719 patients,531 had benign lesions,119 had epithelial ovarian cancers(EOC),44 had borderline ovarian tumors(BOT),and 25 had non-EOC.AUCs and the sensitivity of cancer antigen 125(CAI25),human epididymis-specific protein 4(HE4),Risk of Ovarian Malignancy Algorithm(ROMA),Risk of Malignancy Index(RMI1),HE4 model,and Rajavithi-Ovarian Cancer Predictive Score(R-OPS)in the overall population were 0.792,0.854,0.856,0.872,0.893,0.852,and 70.2%,56.9%,69.1%,60.6%,77.1%,71.3%,respectively.For distinguishing EOC from benign tumors,the AUCs and sensitivity of the above mentioned predictors were 0.888,0.946,0.947,0.949,0.967,0.966,and 84.0%,79.8%,87.4%,84.9%,90.8%,89.1%,respectively.Their specificity in predicting benign diseases was 72.9%,94.4%,87.6%,95.9%,86.3%,90.8%,respectively.Therefore,we consider biomarkers in combination with ultrasound features may improve the diagnostic performance in distinguishing malignant from benign ovarian tumors.

An improved risk of malignancy index in diagnosis of adnexal mass

Background With the advent of color Doppler flow imaging （CDFI） and technological development of detection of serum tumor markers,new opportunities are presented to the improved risk of malignancy index （RMI） based on Jacobs＇research for predicting ovarian malignancy in patients with adnexal masses.Methods One hundred and eighty women with an adnexal mass admitted for primary laparotomy were studied.Tumor specific growth factor （TSGF） adjusted ultrasound scores and the results of Doppler blood flow analysis were obtained before the operation.Based on the parameters which had been studied in Jacobs＇ research,TSGF levels and the findings of color Doppler flow imaging,the risk of malignancy model was redesigned using a binary Logistic regression model.The diagnostic efficacy of the improved risk of malignancy index （improved RMI） was compared with the Jacobs＇model RMI by receiver operator characteristic （ROC） curve.Results The ROC curve showed a higher sensitivity （Mcnamer＇s test,P 〈0.05） in the discrimination between benign and malignant adnexal masses for the improved RMI than the RMI.Compared with the RMI,the improved RMI had an advantage in prediction of ovarian germ cell tumors and granular cell tumor （28.57% vs.71.43%,P 〈0.05） and the early stage tumors and borderline tumors （33.33% vs.66.67%,P〈0.05）.Conclusion The predictability of the improved RMI is better than the classic Jacobs＇ model,especially in diagnosis of the ovarian germ cell tumors and granular cell tumor and other early stage adnexal tumors.

A risk of malignancy index in preoperative diagnosis of ovarian cancer

Objective To evaluate the ability of a risk of malignancy index (RMI), based on serum CA125 level, ultrasound findings and menopausal status, to discriminate benign from malignant pelvic mass. Methods One hundred and forty women with pelvic masses, at age 30 of years or more were admitted to the Peking Union Medical College Hospital between January 1998 and June 1999. The sensitivity, specificity and positive predictive value of serum CA125 level, ultrasound findings and the menopausal status in diagnosis of ovarian cancer were evaluated separately or combined into the RMI. Results RMI was more accurate than any individual criterion in diagnosing cancer. Using an RMI cutoff level of 200 to indicate malignancy, the RMI derived from this data set gave a sensitivity of 87.3%, a specificity of 84.4%, and a positive predictive value of 82.1%. Conclusions RMI is able to correctly discriminate malignant from benign pelvic mass. It can be introduced easily into clinical practice to facilitate the selection of patients for primary surgery.

Current diagnostic approach to patients with adnexal masses: which tools are relevant in routine praxis?

Objective： The aim of the study was to investigate which anamnestic, laboratory and ultrasound parameters used in routine practice could predict the nature of adnexal mass, thus enabling referral to relevant specialist. Methods： Study involved the women treated for adnexal tumors throughout a period of 2 years. On admission, detailed anamnestic and laboratory data were obtained, expert ultrasound scan was performed, and power Doppler index （PDI）, risk of malignancy index （RMI） and body mass index （BMI） were calculated for all patients. Obtained data were related to histopathological findings, and statistically analyzed. Results： The study included 689 women （112 malignant, 544 benignant, and 33 borderline tumors）. Malignant and borderline tumors were more frequent in postmenopausal women （P=0.000）. Women who had benignant tumors had the lowest BMI （P=0.000）. There were significant （P〈0.05） differences among tumor types regarding erythrocyte sedimentation rate, CA125 and carcinoembryonic antigen （CEA） levels. Among ultrasound findings, larger tumor diameter and ascites were more frequent in malignant tumors （P=0.000）. Women with malignant tumors had highest values of RMI and PDI （P=0.000）. Conclusions： Anamnestic data, ultrasound parameters and laboratory analyses were all found to be good discriminating factors among malignant, benignant and borderline tumors.