Risk stratification in gastric cancer lung metastasis: Utilizing an overall survival nomogram and comparing it with previous staging

BACKGROUND Gastric cancer(GC)is prevalent and aggressive,especially when patients have distant lung metastases,which often places patients into advanced stages.By identifying prognostic variables for lung metastasis in GC patients,it may be po-ssible to construct a good prediction model for both overall survival(OS)and the cumulative incidence prediction(CIP)plot of the tumour.AIM To investigate the predictors of GC with lung metastasis(GCLM)to produce nomograms for OS and generate CIP by using cancer-specific survival(CSS)data.METHODS Data from January 2000 to December 2020 involving 1652 patients with GCLM were obtained from the Surveillance,epidemiology,and end results program database.The major observational endpoint was OS;hence,patients were se-parated into training and validation groups.Correlation analysis determined va-rious connections.Univariate and multivariate Cox analyses validated the independent predictive factors.Nomogram distinction and calibration were performed with the time-dependent area under the curve(AUC)and calibration curves.To evaluate the accuracy and clinical usefulness of the nomograms,decision curve analysis(DCA)was performed.The clinical utility of the novel prognostic model was compared to that of the 7th edition of the American Joint Committee on Cancer(AJCC)staging system by utilizing Net Reclassification Improvement(NRI)and Integrated Discrimination Improvement(IDI).Finally,the OS prognostic model and Cox-AJCC risk stratification model modified for the AJCC system were compared.RESULTS For the purpose of creating the OS nomogram,a CIP plot based on CSS was generated.Cox multivariate regression analysis identified eleven significant prognostic factors(P<0.05)related to liver metastasis,bone metastasis,primary site,surgery,regional surgery,treatment sequence,chemotherapy,radiotherapy,positive lymph node count,N staging,and time from diagnosis to treatment.It was clear from the DCA(net benefit>0),time-de-pendent ROC curve(training/validation set AUC>0.7),and calibration curve(reliability slope closer to 45 degrees)results that the OS nomogram demonstrated a high level of predictive efficiency.The OS prediction model(New Model AUC=0.83)also performed much better than the old Cox-AJCC model(AUC difference between the new model and the old model greater than 0)in terms of risk stratification(P<0.0001)and verification using the IDI and NRI.CONCLUSION The OS nomogram for GCLM successfully predicts 1-and 3-year OS.Moreover,this approach can help to ap-propriately classify patients into high-risk and low-risk groups,thereby guiding treatment.

Proportion of Uterine Malignant Tumors in Patients with Laparoscopic Myomectomy： A National Multicenter Study in China

Background： The Food and Drug Administration recently announced that the use of morcellation may cause fibroids or pelvic dissemination and metastasis of uterine sarcoma; therefore, the use of morcellation is limited in the USA. A large sample study is necessary to assess the proportion of uterine malignant tumors found in patients with laparoscopic myomectomy. Methods： A national multicenter study was performed in China. From 2002 to 2014, 33,723 cases were retrospectively selected. We calculated the prevalence and recorded the clinical characteristics of the patients with malignancy after morcellation application. A total of 62 cases were finally pathologically confirmed as malignant postoperatively. Additionally, the medical records of the 62 patients were analyzed in details. Results： The proportion of postoperative malignancy after morcellation application was 0.18% （62/33,723） for patients who underwent laparoscopic myomectomy. Nearly 62.9% （39/62） of patients had demonstrated blood flow signals in the uterine fibroids before surgery. And, 23 （37.1%） patients showed rapid growth at the final preoperative ultrasound. With respect to the pathological types, 38 （61.3%） patients had detectable endometrial stromal sarcoma, 13 （21.0%） had detectable uterine leiomyosarcoma, only 3 （3.2%） had detectable carcinosarcoma, and 5 （8.1%） patients with leiomyoma had an undetermined malignant potential. Conclusions： The proportion of malignancy is low after using moreellation in patients who undergo laparoscopie myomectomy. Patients with fast-growing uterine fibroids and abnormal ultrasonic tumor blood flow should be considered for malignant potential, and morcellation should be avoided.