This study aimed to develop a nomogram to predict the overall survival (OS) of stage IV breast cancer patients. We searched the National Cancer Database (NCDB) for stage IV breast cancer patients diagnosed between 2010 and 2013. Predictors of OS were identified and a nomogram was developed and validated using concordance index (C‐index), calibration plots, and risk group stratifications. A total of 7199 patients from the NCDB were included in the study. With a median follow‐up of 25.7 months, the 1‐year and 3‐year OS rates were 80.6% and 52.5%, respectively. Race, age, comorbidity status, T‐stage, grade, ER/PR/Her2 status, the presence of lung/liver/brain metastasis, surgery, radiotherapy, and chemotherapy were significantly associated with OS. The developed nomogram had a C‐index of 0.722 (95% CI 0.710–0.734) and 0.725 (95% CI 0.713–0.736) in the training and the validation cohorts, respectively. The predicted survival using the nomogram is well correlated with actual OS. The nomogram was able to stratify patients into different risk groups, among which the survival benefit of local therapy varied. We developed a nomogram to predict the overall survival of stage IV breast cancer patients. Prospectively designed studies with international collaborations are needed to further validate our nomogram.

Background: Percutaneous endoscopic interlaminar discectomy (PEID), which poses advantages for certain types of herniated disc, is gaining wider acceptance in clinical practice. We retrospectively analyzed the efficacy of the PEID technique in treatment of calcified lumbar disc herniation. Study Design: A retrospective case-control study. Setting: University hospital in China. Objective: To evaluate the efficacy of the PEID technique in treatment of calcified lumbar disc herniation, and a comparison between calcified and noncalcified disc herniation was drawn to analyze the causes of herniated disc calcification. Methods: Data from patients who underwent full-endoscopic lumbar discectomy in our department between March 2011 and May 2013 were collected. Thirty cases with calcified lumbar disc herniation were included in the study group, and 30 age-, gender-, and body mass index (BMI)-matched cases with noncalcified lumbar disc herniation served as controls. Perioperative data, preoperative and postoperative Visual Analog Scale (VAS) scores, Oswestry Disability Index (ODI) values, MacNab scores, and postoperative low-extremity dysesthesia among patients in the 2 groups were collected. Results: The values of computed tomography (CT) in the calcified group were significantly higher than those in the noncalcified group (P < 0.01). The preoperative disease courses in the 2 groups were similar. However, there was a statistically significant difference in the duration of traditional Chinese medicines (TCM) administration (P < 0.01). VAS and ODI scores improved significantly after surgery, but there were no significant differences between the 2 groups (P > 0.05). Three months after surgery, the rate of low-extremity dysesthesia in the calcified group was significantly higher than that in the control group (P = 0.03) but became similar at 6 months. By applying MacNab criteria the proportions of good and excellent were greater than 90% in both groups, and there was no difference between groups (P > 0.05). Limitations: The sample size was small in this retrospective study. Conclusion: The PEID technique is an effective method in the treatment of calcified lumber disc herniation, although the rate of postoperative dysesthesia is higher in this group during the early postoperative period. Long-term TCM administration may be related to the calcification of herniated lumbar discs. Key words: Lumbar disc herniation, percutaneous endoscopic lumbar discectomy, interlaminar approach, calcification

BackgroundRecent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.ResultsWe propose a method based on statistical moments to reduce feature dimensions. After refining and t-testing, SELDI-TOF data are divided into several intervals. Four statistical moments (mean, variance, skewness and kurtosis) are calculated for each interval and are used as representative variables. The high dimensionality of the data can thus be rapidly reduced. To improve efficiency and classification performance, the data are further used in kernel PLS models. The method achieved average sensitivity of 0.9950, specificity of 0.9916, accuracy of 0.9935 and a correlation coefficient of 0.9869 for 100 five-fold cross validations. Furthermore, only one control was misclassified in leave-one-out cross validation.ConclusionThe proposed method is suitable for analyzing high-throughput proteomics data.