MRI-based radiomics as response predictor to radiochemotherapy for metastatic cervical lymph node in nasopharyngeal carcinoma

Performance of MRI-based radiomics for prediction of residual disease status in patients with nasopharyngeal carcinoma after radical radiotherapy

The purpose of this study was to determine if habitat radiomic features extracted from pretherapy multi-sequence MRI predict residual status in patients with Nasopharyngeal Carcinoma (NPC) after radical radiotherapy. The retrospective study enrolled 179 primary NPC patients, divided into training and validation cohorts at a 7:3 ratio. K-means clustering was employed to segment T2WI, CE-T1WI and FSCE-T1WI images, creating habitats within the volume of interest. Identify relevant features that can recognize NPC residuals. In the training cohort, support vector machine (SVM) models were developed utilizing the radiomic features extracted from each habitat and the entire tumor, selecting the most predictive features for each sequence. SVM models were constructed by combining the optimal radiomic features from each sequences with clinical data. Model performance was compared and validated using receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA), and differences between models were assessed using the DeLong test. The optimal clustering results revealed 4 habitats in FSCE-T1WI, while 2 habitats in both CE-T1WI and T2WI sequences. In the training cohort, we compared the predictive accuracy of SVM models based on different habitats and total tumor characteristics from three sequences, and found that the features from T2 Hab2, CE-T1 Hab1, and FSCE-T1 Hab4 images showed higher performance. Incorporation of habitat-based radiomic features and clinical variables significantly enhanced the predictive performance. The integrated model exhibits the optimal predictive performance, with the area under the curve (AUC) values of 0.921 (SEN = 0.821, SPE = 0.830) in the training cohort and 0.811 (SEN = 0.778, SPE = 0.722) in the validation cohort. Compared to conventional radiomics, habitat imaging features that distinguish intratumoral heterogeneity have higher predictive value, making them potential non-invasive biomarkers for assessing NPC residual after radiotherapy. Integration of multi-sequence MRI habitat radiomic with clinical parameters further improved predictive accuracy.

A Stacked Multimodality Model Based on Functional MRI Features and Deep Learning Radiomics for Predicting the Early Response to Radiotherapy in Nasopharyngeal Carcinoma

BACKGROUND

This study aimed to construct and assess a comprehensive model that integrates MRI-derived deep learning radiomics, functional imaging (fMRI), and clinical indicators to predict early efficacy of radiotherapy in nasopharyngeal carcinoma (NPC).

METHODS

This retrospective study recruited NPC patients with radiotherapy from two Chinese hospitals between October 2018 and July 2022, divided into a training set (hospital I, 194 cases), an internal validation set (hospital I, 82 cases), and an external validation set (hospital II, 40 cases). We extracted 3404 radiomic features and 2048 deep learning features from multi-sequence MRI includes T1WI, CE-T1WI, T2WI and T2WI/FS. Additionally, both the Apparent diffusion coefficient (ADC), its maximum (ADCmax) and Tumor blood flow (TBF), its maximum (TBFmax) were obtained by Diffusion-weighted imaging (DWI) and Arterial spin labeling (ASL) respectively. We used four classifiers (LR, XGBoost, SVM and KNN) and stacked algorithm as model construction methods. The area under the receiver operating characteristic curve (AUC) and decision curve analysis was used to assess models.

RESULTS

The manual radiomics model based on XGBoost and the deep learning model based on KNN (the AUCs in the training set: 0.909, 0.823, respectively) showed better predictive efficacy than other machine learning algorithms. The stacked model that integrated MRI-based deep learning radiomics, fMRI, and hematological indicators, has the strongest efficacy prediction ability of AUC in the training set [0.984 (95%CI: 0.972-0.996)], the internal validation set [0.936 (95%CI: 0.885-0.987)], and the external validation set [0.959 (95%CI: 0.901-1.000)].

CONCLUSION

Our research has developed a clinical-radiomics integrated model based on MRI which can predict early radiotherapy response in NPC and provide guidance for personalized treatment.

Radiomics-based lymph nodes prognostic models from three MRI regions in nasopharyngeal carcinoma

Accurate prediction of the prognosis of nasopharyngeal carcinoma (NPC) is important for treatment. Lymph nodes metastasis is an important predictor for distant failure and regional recurrence in patients with NPC. Traditionally, subjective radiological evaluation increases concerns regarding the accuracy and consistency of predictions. Radiomics is an objective and quantitative evaluation algorithm for medical images. This retrospective analysis was conducted based on the data of 729 patients newly diagnosed with NPC without distant metastases to evaluate the performance of radiomics pretreatment using magnetic resonance imaging (MRI)-determined metastatic lymph nodes models to predict NPC prognosis with three delineation methods. Radiomics features were extracted from all lymph nodes (ALN), largest lymph node (LLN), and largest slice of the largest lymph node (LSLN) to generate three radiomics signatures. The radiomics signatures, clinical model, and radiomics-clinic merged models were developed in training cohort for predicting overall survival (OS). The results showed that LSLN signature with clinical factors predicted OS with high accuracy and robustness using pretreatment MR-determined metastatic lymph nodes (C-index [95 % confidence interval]: 0.762[0.760-0.763]), providing a new tool for treatment planning in NPC.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Revolutionizing lymph node metastasis imaging: the role of drug delivery systems and future perspectives

The deployment of imaging examinations has evolved into a robust approach for the diagnosis of lymph node metastasis (LNM). The advancement of technology, coupled with the introduction of innovative imaging drugs, has led to the incorporation of an increasingly diverse array of imaging techniques into clinical practice. Nonetheless, conventional methods of administering imaging agents persist in presenting certain drawbacks and side effects. The employment of controlled drug delivery systems (DDSs) as a conduit for transporting imaging agents offers a promising solution to ameliorate these limitations intrinsic to metastatic lymph node (LN) imaging, thereby augmenting diagnostic precision. Within the scope of this review, we elucidate the historical context of LN imaging and encapsulate the frequently employed DDSs in conjunction with a variety of imaging techniques, specifically for metastatic LN imaging. Moreover, we engage in a discourse on the conceptualization and practical application of fusing diagnosis and treatment by employing DDSs. Finally, we venture into prospective applications of DDSs in the realm of LNM imaging and share our perspective on the potential trajectory of DDS development.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Intravoxel incoherent motion radiomics nomogram for predicting tumor treatment responses in nasopharyngeal carcinoma

BACKGROUND

Intravoxel incoherent motion (IVIM) plays an important role in predicting treatment responses in patient with nasopharyngeal carcinoma (NPC). The goal of this study was to develop and validate a radiomics nomogram based on IVIM parametric maps and clinical data for the prediction of treatment responses in NPC patients.

METHODS

Eighty patients with biopsy-proven NPC were enrolled in this study. Sixty-two patients had complete responses and 18 patients had incomplete responses to treatment. Each patient received a multiple b-value diffusion-weighted imaging (DWI) examination before treatment. Radiomics features were extracted from IVIM parametric maps derived from DWI image. Feature selection was performed by the least absolute shrinkage and selection operator method. Radiomics signature was generated by support vector machine based on the selected features. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) values were used to evaluate the diagnostic performance of radiomics signature. A radiomics nomogram was established by integrating the radiomics signature and clinical data.

RESULTS

The radiomics signature showed good prognostic performance to predict treatment response in both training (AUC = 0.906, P<0.001) and testing (AUC = 0.850, P<0.001) cohorts. The radiomic nomogram established by integrating the radiomic signature with clinical data significantly outperformed clinical data alone (C-index, 0.929 vs 0.724; P<0.0001).

CONCLUSIONS

The IVIM-based radiomics nomogram provided high prognostic ability to treatment responses in patients with NPC. The IVIM-based radiomics signature has the potential to be a new biomarker in prediction of the treatment responses and may affect treatment strategies in patients with NPC.

Automatic tumor segmentation and metachronous single-organ metastasis prediction of nasopharyngeal carcinoma patients based on multi-sequence magnetic resonance imaging

Background

Distant metastases is the main failure mode of nasopharyngeal carcinoma. However, early prediction of distant metastases in NPC is extremely challenging. Deep learning has made great progress in recent years. Relying on the rich data features of radiomics and the advantages of deep learning in image representation and intelligent learning, this study intends to explore and construct the metachronous single-organ metastases (MSOM) based on multimodal magnetic resonance imaging.

Patients and methods

The magnetic resonance imaging data of 186 patients with nasopharyngeal carcinoma before treatment were collected, and the gross tumor volume (GTV) and metastatic lymph nodes (GTVln) prior to treatment were defined on T1WI, T2WI, and CE-T1WI. After image normalization, the deep learning platform Python (version 3.9.12) was used in Ubuntu 20.04.1 LTS to construct automatic tumor detection and the MSOM prediction model.

Results

There were 85 of 186 patients who had MSOM (including 32 liver metastases, 25 lung metastases, and 28 bone metastases). The median time to MSOM was 13 months after treatment (7–36 months). The patients were randomly assigned to the training set (N = 140) and validation set (N = 46). By comparison, we found that the overall performance of the automatic tumor detection model based on CE-T1WI was the best (6). The performance of automatic detection for primary tumor (GTV) and lymph node gross tumor volume (GTVln) based on the CE-T1WI model was better than that of models based on T1WI and T2WI (AP@0.5 is 59.6 and 55.6). The prediction model based on CE-T1WI for MSOM prediction achieved the best overall performance, and it obtained the largest AUC value (AUC = 0.733) in the validation set. The precision, recall, precision, and AUC of the prediction model based on CE-T1WI are 0.727, 0.533, 0.730, and 0.733 (95% CI 0.557–0.909), respectively. When clinical data were added to the deep learning prediction model, a better performance of the model could be obtained; the AUC of the integrated model based on T2WI, T1WI, and CE-T1WI were 0.719, 0.738, and 0.775, respectively. By comparing the 3-year survival of high-risk and low-risk patients based on the fusion model, we found that the 3-year DMFS of low and high MSOM risk patients were 95% and 11.4%, respectively (p &lt; 0.001).

Conclusion

The intelligent prediction model based on magnetic resonance imaging alone or combined with clinical data achieves excellent performance in automatic tumor detection and MSOM prediction for NPC patients and is worthy of clinical application.

Using ultrasound radiomics analysis to diagnose cervical lymph node metastasis in patients with nasopharyngeal carcinoma

OBJECTIVE

This study aimed to explore the clinical value of ultrasound radiomics analysis in the diagnosis of cervical lymph node metastasis (CLNM) in patients with nasopharyngeal carcinoma (NPC).

METHODS

A total of 205 cases of NPC CLNM and 284 cases of benign lymphadenopathy with pathologic diagnosis were retrospectively included. Grayscale ultrasound (US) images of the largest section of every lymph node underwent feature extraction. Feature selection was done by maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression. Logistic regression models were developed based on clinical features, radiomics features, and the combination of those features. The AUCs of models were analyzed by DeLong's test.

RESULTS

In the clinical model, lymph nodes in the upper neck, larger long axis, and unclear hilus were significant factors for CLNM (p < 0.001). MRMR and LASSO regression selected 7 significant features for the radiomics model from the 386 radiomics features extracted. In the validation dataset, the AUC value was 0.838 (0.776-0.901) in the clinical model, 0.810 (0.739-0.881) in the radiomics model, and 0.880 (0.826-0.933) in the combined model. There was not a significant difference between the AUCs of clinical models and radiomics models in both datasets. DeLong's test revealed a significantly larger AUC in the combined model than in the clinical model in both training (p = 0.049) and validation datasets (p = 0.027).

CONCLUSION

Ultrasound radiomics analysis has potential value in screening meaningful ultrasound features and improving the diagnostic efficiency of ultrasound in CLNM of patients with NPC.

KEY POINTS

• Radiomics analysis of gray-scale ultrasound images can be used to develop an effective radiomics model for the diagnosis of cervical lymph node metastasis in nasopharyngeal carcinoma patients. • Radiomics model combined with general ultrasound features performed better than the clinical model in differentiating cervical lymph node metastases from benign lymphadenopathy.