American Joint Committee on Cancer Staging and Other Platforms to Assess Prognosis and Risk

Nanotechnology-Based Drug Delivery Systems in the Transdermal Treatment of Melanoma

The incidence rate of melanoma is dramatically increasing worldwide, raising it to the fifth most common cancer in men and the sixth in women currently. Resistance generally occurs to the agents used in chemotherapy; besides their high toxicity destroys the normal cells. This study reviewed a detailed summary of the structure, advantages, and disadvantages of nanotechnology-based drug delivery systems in the treatment of melanoma, as well as some nanocarrier applications in animal models or clinical studies. Respective databases were searched for the target keywords and 93 articles were reviewed and discussed. A close study of the liposomes, niosomes, transferosomes, ethosomes, transethosomes, cubosomes, dendrimers, cyclodextrins, solid lipid nanoparticles, and carbon nanotubes (CNTs) was conducted. It was found that these nanocarriers could inhibit metastasis and migration of melanoma cells and decrease cell viability. Conclusively, some nanocarriers like liposomes, niosomes, and transferosomes have been discussed as superior to conventional therapies for melanoma treatment.

Multicenter Survival Analysis and Application of an Olfactory Neuroblastoma Staging Modification Incorporating Hyams Grade

Importance

Current olfactory neuroblastoma (ONB) staging systems inadequately delineate locally advanced tumors, do not incorporate tumor grade, and poorly estimate survival and recurrence.

Objective

The primary aims of this study were to (1) examine the clinical covariates associated with survival and recurrence of ONB in a modern-era multicenter cohort and (2) incorporate Hyams tumor grade into existing staging systems to assess its ability to estimate survival and recurrence.

Design, Setting, and Participants

This retrospective, multicenter, case-control study included patients with ONB who underwent treatment between January 1, 2005, and December 31, 2021, at 9 North American academic medical centers.

Intervention

Standard-of-care ONB treatment.

Main Outcome and Measures

The main outcomes were overall survival (OS), disease-free survival (DFS), and disease-specific survival (DSS) as C statistics for model prediction.

Results

A total of 256 patients with ONB (mean [SD] age, 52.0 [15.6] years; 115 female [44.9%]; 141 male [55.1%]) were included. The 5-year rate for OS was 83.5% (95% CI, 78.3%-89.1%); for DFS, 70.8% (95% CI, 64.3%-78.0%); and for DSS, 94.1% (95% CI, 90.5%-97.8%). On multivariable analysis, age, American Joint Committee on Cancer (AJCC) stage, involvement of bilateral maxillary sinuses, and positive margins were associated with OS. Only AJCC stage was associated with DFS. Only N stage was associated with DSS. When assessing the ability of staging systems to estimate OS, the best-performing model was the novel modification of the Dulguerov system (C statistic, 0.66; 95% CI, 0.59-0.76), and the Kadish system performed most poorly (C statistic, 0.57; 95% CI, 0.50-0.63). Regarding estimation of DFS, the modified Kadish system performed most poorly (C statistic, 0.55; 95% CI, 0.51-0.66), while the novel modification of the AJCC system performed the best (C statistic, 0.70; 95% CI, 0.66-0.80). Regarding estimation of DSS, the modified Kadish system was the best-performing model (C statistic, 0.79; 95% CI, 0.70-0.94), and the unmodified Kadish performed the worst (C statistic, 0.56; 95% CI, 0.51-0.68). The ability for novel ONB staging systems to estimate disease progression across stages was also assessed. In the novel Kadish staging system, patients with stage VI disease were approximately 7 times as likely to experience disease progression as patients with stage I disease (hazard ratio [HR], 6.84; 95% CI, 1.60-29.20). Results were similar for the novel modified Kadish system (HR, 8.99; 95% CI, 1.62-49.85) and the novel Dulguerov system (HR, 6.86; 95% CI, 2.74-17.18).

Conclusions and Relevance

The study findings indicate that 5-year OS for ONB is favorable and that incorporation of Hyams grade into traditional ONB staging systems is associated with improved estimation of disease progression.

Application of machine learning algorithm in prediction of lymph node metastasis in patients with intermediate and high-risk prostate cancer

PURPOSE

This study aims to establish the best prediction model of lymph node metastasis (LNM) in patients with intermediate- and high-risk prostate cancer (PCa) through machine learning (ML), and provide the guideline of accurate clinical diagnosis and precise treatment for clinicals.

METHODS

A total of 24,470 patients with intermediate- and high-risk PCa were included in this study. Multivariate logistic regression model was used to screen the independent risk factors of LNM. At the same time, six algorithms, namely random forest (RF), naive Bayesian classifier (NBC), xgboost (XGB), gradient boosting machine (GBM), logistic regression (LR) and decision tree (DT) are used to establish risk prediction models. Based on the best prediction performance of ML algorithm, a prediction model is established, and the performance of the model is evaluated from three aspects: area under curve (AUC), sensitivity and specificity.

RESULTS

In multivariate logistic regression analysis, T stage, PSA, Gleason score and bone metastasis were independent predictors of LNM in patients with intermediate- and high-risk PCa. By comprehensively comparing the prediction model performance of training set and test set, GBM model has the best prediction performance (F1 score = 0.838, AUROC = 0.804). Finally, we developed a preliminary calculator model that can quickly and accurately calculate the regional LNM in patients with intermediate- and high-risk PCa.

CONCLUSION

T stage, PSA, Gleason and bone metastasis were independent risk factors for predicting LNM in patients with intermediate- and high-risk PCa. The prediction model established in this study performs well; however, the GBM model is the best one.

Prediction of Central Lymph Node Metastasis in cN0 Papillary Thyroid Carcinoma by CT Radiomics

RATIONALE AND OBJECTIVES

To explore the feasibility of the preoperative prediction of pathological central lymph node metastasis (CLNM) status in patients with negative clinical lymph node (cN0) papillary thyroid carcinoma (PTC) using a computed tomography (CT) radiomics signature.

MATERIALS AND METHODS

A total of 97 PTC cN0 nodules with CLNM pathology data (pN0, with CLNM, n = 59; pN1, without CLNM, n = 38) in 85 patients were divided into a training set (n = 69) and a validation set (n = 28). For each lesion, 321 radiomic features were extracted from nonenhanced, arterial and venous phase CT images. Minimum redundancy and maximum relevance and the least absolute shrinkage and selection operator were used to find the most important features with which to develop a radiomics signature in the training set. The performance of the radiomics signature was evaluated by receiver operating characteristic curves, calibration curves and decision curve analysis .

RESULTS

Three nonzero the least absolute shrinkage and selection operator coefficient features were selected for radiomics signature construction. The radiomics signature for distinguishing the pN0 and pN1 groups achieved areas under the curve of 0.79 (95% CI 0.67, 0.91) in the training set and 0.77 (95% CI 0.55, 0.99) in the validation set. The calibration curves demonstrated good agreement between the radiomics score-predicted probability and the pathological results in the two sets (p= 0.399, p = 0.191). The decision curve analysis curves showed that the model was clinically useful.

CONCLUSION

This radiomic signature could be helpful to predict CLNM status in cN0 PTC patients.

Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients

Background

Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms.

Methods

A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals.

Results

Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals.

Conclusions

T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.