MRI-based radiomic models to predict surgical margin status and infer tumor immune microenvironment in breast cancer patients with breast-conserving surgery: a multicenter validation study

ConvXGB: A novel deep learning model to predict recurrence risk of early-stage cervical cancer following surgery using multiparametric MRI images

BACKGROUND

Accurate estimation of recurrence risk for cervical cancer plays a pivot role in making individualized treatment plans. We aimed to develop and externally validate an end-to-end deep learning model for predicting recurrence risk in cervical cancer patients following surgery by using multiparametric MRI images.

METHODS

The clinicopathologic data and multiparametric MRI images of 406 cervical cancer patients from three institutions were collected. We designed a novel deep learning model called "ConvXGB" for predicting recurrence risk by combining the convolutional neural network (CNN) and eXtreme Gradient Boost (XGBoost). The predictive performance of the ConvXGB model was evaluated using time-dependent area under curve (AUC), compared with the deep learning radio-clinical model, clinical model, conventional radiomics nomogram and an existing histology-specific tool. The potential of the ConvXGB model in predicting the recurrence-free survival (RFS) and overall survival (OS) was assessed.

RESULTS

The ConvXGB model outperformed other models in predicting recurrence risk, with AUCs for 1 and 3 year-RFS of 0.872(95% CI, 0.857-0.906) and 0.882(95% CI, 0.860-0.904) respectively in the test cohort. This model showed better discrimination, calibration and clinical utility. Grad-CAM analysis was adopted to help clinicians better understand the predictive results. Moreover, Kaplan-Meier survival analysis revealed that patients who were stratified into high-risk group by the ConvXGB model were significantly susceptible to higher cumulative recurrence risk rates and worse outcome.

CONCLUSION

The ConvXGB model allowed for predicting postoperative recurrence risk in cervical cancer patients and for stratifying the risk of RFS and OS.

Predicting functional outcome after open lumbar fusion surgery: A retrospective multicenter cohort study

PURPOSE

We aimed to develop and externally validate a tool for predicting short-term functional outcome after lumbar fusion surgery.

METHODS

Data of 1520 patients underwent lumbar fusion from three institutions was analyzed. A total of 855 and 1251 radiomics features from paraspinal muscles were extracted from preoperative CT and MRI scans, respectively. Multivariable logistic regression was used to identify independent risk factors of poor functional status after surgery. We developed and externally validated a combined model by integrating radiomics score and clinical features. We evaluated the clinical utility and stability of the model using decision curve and calibration curve analysis. SHAP plot was used for interpretation of predictive results.

RESULTS

At multivariable analysis, radiomics score and 4 clinical features were identified as independent risk factors of poor functional outcome, and then a combined model was generated. This model had excellent performance, with AUCs of 0.85(95 %CI, 0.81-0.88), 0.82(95 %CI, 0.77-0.84), 0.79(95 %CI, 0.73-0.84) and 0.80(95 %CI, 0.76-0.83) in the derivation dataset and three independent test datasets, respectively. Moreover, this model showed great calibration and utility, outperforming the clinical model and radiomics score alone (both p < 0.05).

CONCLUSION

The combined model allows for accurate prediction of functional outcome after lumbar fusion surgery. The model could guide clinical decisions about the necessity of surgery for potential functional recovery.

Intratumoral and peritumoral radiomics of MRIs predicts pathologic complete response to neoadjuvant chemoimmunotherapy in patients with head and neck squamous cell carcinoma

BACKGROUND

For patients with locally advanced head and neck squamous cell carcinoma (HNSCC), combined programmed death receptor-1 inhibitor and chemotherapy improved response rate to neoadjuvant therapy. However, treatment response varies among patients. There is no tool to predict pathologic complete response (pCR) with high accuracy for now. To develop a tool based on radiomics features of MRI to predict pCR to neoadjuvant chemoimmunotherapy (NACI) may provide valuable assistance in treatment regimen determination for HNSCC.

METHODS

From January 2021 to April 2024, a total of 172 patients with HNSCC from three medical center, who received NACI followed by surgery, were included and allocated into a training set (n=84), an internal validation set (n=37) and an external validation set (n=51). Radiomics features were extracted from intratumoral and different peritumoral areas, and radiomics signature (Rad-score) for each area was constructed. A radiomics-clinical nomogram was developed based on Rad-scores and clinicopathological characteristics, tested in the validation sets, and compared with clinical nomogram and combined positive score (CPS) in predicting pCR.

RESULTS

The radiomics-clinical nomogram, incorporating peritumoral Rad-score, intratumoral Rad-score and CPS, achieved the highest accuracy with areas under the receiver operating characteristic curve of 0.904 (95% CI, 0.835 to 0.972) in the training cohort, 0.860 (95% CI, 0.722 to 0.998) in the internal validation cohort, and 0.849 (95% CI, 0.739 to 0.959) in the external validation cohort, respectively, which outperformed the clinical nomogram and CPS in predict pCR to NACI for HNSCC.

CONCLUSION

A nomogram developed based on intratumoral and peritumoral MRI radiomics features outperformed CPS, a widely employed biomarker, in predict pCR to NACI for HNSCC, which would provide incremental value in treatment regimen determination.

Radiomics in breast cancer: Current advances and future directions

Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.

Imaging classification of prostate cancer with extracapsular extension and its impact on positive surgical margins after laparoscopic radical prostatectomy

Abstract

To explore the impact of different imaging classifications of prostate cancer (PCa) with extracapsular extension (EPE) on positive surgical margins (PSM) after laparoscopic radical prostatectomy.

Methods

Clinical data were collected for 114 patients with stage PT3a PCa admitted to Ningbo Yinzhou No. 2 Hospital from September 2019 to August 2023. Radiologists classified the EPE imaging of PCa into Type I, Type II, and Type III. A chi-square test or t-test was employed to analyze the factors related to PSM. Multivariate regression analysis was conducted to determine the factors associated with PSM. Receiver operating characteristic curve analysis was used to calculate the area under the curve and evaluate the diagnostic performance of our model. Clinical decision curve analysis was performed to assess the clinical net benefit of EPE imaging classification, biopsy grade group (GG), and combined model.

Results

Among the 114 patients, 58 had PSM, and 56 had negative surgical margins. Multivariate analysis showed that EPE imaging classification and biopsy GG were risk factors for PSM after laparoscopic radical prostatectomy. The areas under the curve for EPE imaging classification and biopsy GG were 0.677 and 0.712, respectively. The difference in predicting PSM between EPE imaging classification and biopsy GG was not statistically significant (P>0.05). However, when used in combination, the diagnostic efficiency significantly improved, with an increase in the area under the curve to 0.795 (P<0.05). The clinical decision curve analysis revealed that the clinical net benefit of the combined model was significantly higher than that of EPE imaging classification and biopsy GG.

Conclusions

EPE imaging classification and biopsy GG were associated with PSM after laparoscopic radical prostatectomy, and their combination can significantly improve the accuracy of predicting PSM.