Long term trends of breast cancer incidence according to proliferation status

Preliminary exploration of poor prognostic factor IL-33 and its involvement in perioperative immunotherapy in stage II-III lung squamous cell carcinoma: a retrospective cohort study

Background

Current knowledge about the prognostic role of interleukin-33 (IL-33) in lung squamous cell carcinoma (LUSC) remains limited, particularly in stage II-III patients. This study aimed to verify the correlation between IL-33 expression and poor prognosis in stage II-III LUSC patients at both gene and protein levels and to investigate the potential role of IL-33 blockade in combination with immune checkpoint inhibitors (ICIs) in perioperative immunotherapy.

Methods

A retrospective analysis was conducted of 103 patients with stage II-III LUSC who underwent surgical resection at Tianjin Medical University Cancer Institute & Hospital from November 1, 2004, to November 30, 2006. Of these, 83 patients were included based on complete follow-up data, and were divided into a gene expression group (38 patients) and a protein expression group (45 patients). IL-33 expression was analyzed using real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC). The correlation between IL-33 expression and overall survival (OS) was assessed using Kaplan-Meier survival analysis. Additionally, IHC results from 20 patients were used to explore the correlation between IL-33, programmed death ligand 1 (PD-L1), and Ki-67 expression levels. The total follow-up time exceeded 60 months, and the study endpoint was OS.

Results

Patients with high IL-33 expression had significantly shorter OS compared to those with low IL-33 expression, both at the gene (P=0.006) and protein expression (P=0.01). Logistic regression analysis confirmed IL-33 as an independent prognostic factor for poor survival in stage II-III LUSC (Pgene=0.04, Pprotein=0.009). Additionally, a significant positive correlation was observed between the protein expression of IL-33 (P=0.03), PD-L1 (P<0.001), and Ki-67 (P=0.01), indicating that high expression of these markers is associated with worse prognosis.

Conclusions

High IL-33 expression in cancer tissues is associated with poor prognosis in stage II-III LUSC. IL-33 blockade combined with ICIs may provide new treatment regimens and ideas for perioperative immunotherapy in stage II-III LUSC patients.

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

PURPOSE

To explore the value of deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram in predicting the Ki-67 expression in rectal cancer.

METHODS

The data of 491 patients with rectal cancer from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. They were categorized into high- and low-expression group based on postoperative pathological Ki-67 expression. Each patient's mp-MRI data were analyzed to extract and select the most relevant features of deep learning, and a deep learning model was constructed. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a nomogram for the prediction of Ki-67 expression. The performance characteristics of the DL-model, clinical model, and nomogram were assessed using ROCs, calibration curve, decision curve, and clinical impact curve analysis.

RESULTS

The strongest deep learning features were extracted and screened from mp-MRI data. Two independent predictive factors, namely Magnetic Resonance Imaging T (mrT) staging and differentiation degree, were identified through clinical feature selection. Three models were constructed: a deep learning (DL)-model, a clinical model, and a nomogram. The AUCs of clinical model in the training, internal validation, and external validation set were 0.69, 0.78, and 0.67, respectively. The AUCs of the deep model and nomogram ranged from 0.88 to 0.98. The prediction performance of the deep learning model and nomogram was significantly better than the clinical model (P < 0.001).

CONCLUSION

The nomogram based on deep learning can help clinicians accurately and conveniently predict the expression status of Ki-67 in rectal cancer.

Deep learning-based radiomic nomograms for predicting Ki67 expression in prostate cancer

BACKGROUND

To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa).

MATERIALS

The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient's prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated.

RESULTS

Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly.

CONCLUSION

The multiple easy-to-use deep learning-based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.