A prediction model for early systemic recurrence in breast cancer using a molecular diagnostic analysis of sentinel lymph nodes: A large-scale, multicenter cohort study

A practical recurrence risk model based on Lasso-Cox regression for gastric cancer

INTRODUCTION

Gastric cancer remains huge cancer threat worldwide. Detecting the recurrence of gastric cancer after treatment is especially important in improving the prognosis of patients. We aim to fit different risk models with different clinical variables for patients with gastric cancer, which further provides applicable guidance to clinical doctors for their patients.

METHODS

We collected the primary data from the medical record system in Lanzhou University Second Hospital and further cleaned the primary data via assessing data integrity artificially; meanwhile, detailed conclusion criteria and exclusion criteria were made. We used R software (version 4.1.3) and SPSS 25.0 to analyze data and build models, in which SPSS was used to analyze the correlation and difference of different items in the training set and testing set, and different R packages were used to run LASSO regression, Cox regression and nomogram for variable selection, model construction and model validation.

RESULT

A total of 649 patients were included in our data analysis and model building. In LASSO regression selection, seven variables, pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, intraoperative blood loss (IBL), the level of AFP and CA199, showed their correlation to the dependent variable. The multivariable Cox regression model fitted using these seven variables showed medium prediction ability, with an AUC of 0.840 in the training set and 0.756 in the testing set.

CONCLUSIONS

Pathological stage, tumor size, the number of total lymph nodes, the number of metastatic lymph nodes, IBL, the level of AFP and CA199 are significant in identifying recurrence risk for gastric cancer patients after radical gastrectomy.

Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models

PURPOSE

Selection of appropriate adjuvant therapy to ultimately reduce the risk of breast cancer (BC) recurrence is a challenge for medical oncologists. Several automated risk prediction models have been developed using retrospective clinical data and have evolved significantly over the years in terms of predictors of recurrence, data usage, and predictive techniques (statistical/machine learning [ML]).

METHODS

Following PRISMA guidelines, we performed a systematic literature review of the aforementioned statistical and ML models published between January 2008 and December 2022 through searching five digital databases-PubMed, ScienceDirect, Scopus, Cochrane, and Web of Science. The comprehensive search yielded a total of 163 papers and after a screening process focusing on papers that dealt exclusively with statistical/ML methods, only 23 papers were deemed appropriate for further analysis. We benchmarked the studies on the basis of development, evaluation metrics, and validation strategy with an added emphasis on racial diversity of patients included in the studies.

RESULTS

In total, 30.4% of the included studies use statistical techniques, while 69.6% are ML-based. Among these, traditional ML models (support vector machines, decision tree, logistic regression, and naïve Bayes) are the most frequently used (26.1%) along with deep learning (26.1%). Deep learning and ensemble learning provide the most accurate predictions (AUC = 0.94 each).

CONCLUSION

ML-based prediction models exhibit outstanding performance, yet their practical applicability might be hindered by limited interpretability and reduced generalization. Moreover, predictive models for BC recurrence often focus on limited variables related to tumor, treatment, molecular, and clinical features. Imbalanced classes and the lack of open-source data sets impede model development and validation. Furthermore, existing models predominantly overlook African and Middle Eastern populations, as they are trained and validated mainly on Caucasian and Asian patients.

Evaluation of the one-step nucleic acid amplification method for rapid detection of lymph node metastases in endometrial cancer: prospective, multicenter, comparative study

OBJECTIVE

To evaluate the diagnostic performance of the one-step nucleic acid amplification (OSNA) method for the detection of sentinel lymph node (SLN) metastases in women with apparent early-stage endometrial cancer compared with standard ultrastaging.

METHODS

Prospective, multicentric, interventional study. Patients with apparent early-stage endometrial cancer who underwent primary surgical staging with SLN mapping were included. SLNs were serially sectioned with 2 mm slices perpendicular to the longest axis of the node: the odd slices were submitted to ultrastaging, whereas the even slices were submitted to the OSNA analysis. Diagnostic performance was calculated taking ultrastaging as referral standard.

RESULTS

Three-hundred and sixteen patients with 668 SLNs were included. OSNA assay detected 22 (3.3%) positive SLNs, of which 17 (2.5%) were micrometastases and 5 (0.7%) macrometastases, whereas ultrastaging detected 24 (3.6%) positive SLNs, of which 15 (2.2%) were micrometastases and 9 (1.3%) macrometastases (p=0.48). Regarding negative SLNs, OSNA detected 646 (96.7%) negative nodes, including 8 (1.2%) isolated tumor cells, while ultrastaging detected 644 (96.4%) negative nodes with 26 (3.9%) isolated tumor cells. Specificity of OSNA was 98.4% (95% CI 97.5 to 99.4), accuracy was 96.7% (95% CI 95.4 to 98.1), sensitivity was 50% (95% CI 30.0 to 70.0), while negative predictive value was 98.1% (95% CI 97.1 to 99.2). Discordant results were found in 22 SLNs (3.3%) corresponding to 20 patients (6.3%). These were 10 (1.5%) false-positive SLNs (all micrometastases): one (0.1%) of these was a benign epithelial inclusion at ultrastaging. There were 12 (1.8%) false-negative SLNs of OSNA, of which 9 (1.3%) were micrometastases and 3 (0.5%) macrometastases. Overall, 17/668 (2.5%) benign epithelial inclusions were detected at ultrastaging.

CONCLUSION

The OSNA method had high specificity and high accuracy in detecting SLN metastasis in apparent early-stage endometrial cancer. The advantage of the OSNA method could be represented as the possibility to analyze the entire lymph node thus eliminating sampling bias.