Yimei Liu

Objectives To develop a multimodal AI model integrating CT imaging, radiotherapy dose, and clinical features for predicting nasopharyngeal necrosis risk after re-irradiation in recurrent nasopharyngeal carcinoma (NPC). Methods This retrospective study included 126 patients with recurrent NPC (44 with necrosis and 82 without necrosis). Clinical baseline characteristics, radiomic features from planning CT images, and three-dimensional radiotherapy dose-derived radiomic features were collected and extracted. After two-stage feature selection, three single-modality and four multimodal fusion models were developed and evaluated on internal and external test sets using AUC, accuracy, sensitivity, and specificity. Results The multimodal fusion model demonstrated overall superior performance compared to all single-modality models. The CT-dose fusion model achieved AUCs of 0.869 and 0.972 in the internal and external test sets, respectively, outperforming the single CT, single-dose, and single clinical models. Conclusion The CT-dose fusion model demonstrated robust performance with strong generalizability for predicting nasopharyngeal necrosis, providing a valuable tool for early identification of high-risk patients and showing promise for clinical application.

Background and purpose: Patients with recurrent nasopharyngeal carcinoma (rNPC) undergoing re-irradiation have a high risk of lethal nasopharyngeal necrosis (NN), which may lead to massive nasopharyngeal hemorrhage or death. Predicting NN is crucial to improve the prognosis of these patients. We aimed to utilize deep learning techniques in combination with multi-sequence magnetic resonance imaging (MRI) radiomics and dosiomics to predict the risk of nasopharyngeal necrosis in patients with recurrent nasopharyngeal carcinoma undergoing re-irradiation therapy.Materials and methods: 117 patients with rNPC were included, comprising pre-treatment multi-sequence MR images (including T1, T1C, and T2 sequences) and a planned re-irradiation therapy dose distribution. A three-dimensional (3D) convolutional neural network (CNN) deep learning network model was utilized to integrate the selected MRI radiomics and dosiomics features. Eight prediction deep learning models were developed for training, 97 cases were used as the training set and 20 as the test set. The performance and prediction accuracy of each deep learning network model were then evaluated.Results: Thirty-two features correlated with necrosis of rNPC. The model based on multi-sequence MRI radiomics could better predict necrosis. The models combining radiomics and dosiomics features were more accurate for the prediction of NN, especially the model of multi-sequence MRI radiomics plus dosiomics, which showed the best performance in the test set, with an AUC, ACC, and F1-Score of 0.81, 0.75, and 0.74, respectively.Conclusion: The deep learning model leveraging pre-treatment multi-sequence MRI radiomics and dosiomics of re-irradiation therapy can serve as a potential predictor of NN in patients with recurrent nasopharyngeal carcinoma, thereby improving clinical decision-making processes.

Background and purpose: This study aimed to analyze the impact of interobserver variability (IOV) on clinical dosimetry and prognosis, specifically investigating the correlation between IOV and clinical prognosis in the context of intensity-modulated radiation therapy (IMRT) for nasopharyngeal carcinoma (NPC).Materials and methods: Twelve NPC patients who underwent IMRT were selected. Four radiotherapy physicians from two different-tier cancer centers independently delineated target volumes and organs at risk (OARs) for each patient. These delineations were compared against gold standard structures from a regional cancer center. The IOV among physicians and its effect on clinical and prognosis were analyzed. The relationships between the IOV, dosimetry, and prognosis were investigated using spearman’s correlation analysis.Results: The target volume and OARs delineation differed significantly among physicians. This variability led to reduced prescription dose coverage (PDC) of the planning target volume (PTV) and increased doses to OARs, impacting tumor control probability (TCP) and normal tissue complication probability (NTCP). Compared to standard delineations, all four physicians showed decreased TCPs (average decrease in ΔTCP >1%) and a significant increase in NTCPs of OARs. The relative volume difference (ΔV) of target volumes correlated strongly with ΔPDC (R=0.686) and ΔTCP (R=0.703). Moreover, in the validation set, ΔV also strongly correlated with ΔTCP (R = 0.778).Conclusion: Substantial IOV in delineating NPC target volumes and OARs for IMRT was observed. This variability affects plan optimization, dose distribution, and clinical prognosis. ΔV can serve as a risk predictor for assessing delineation variability in NPC radiotherapy treatment planning.

Purpose: This study aimed to assess the effects of bladder filling during cervical cancer radiotherapy on target volume and organs at risk (OARs) dose based on daily computed tomography (daily‐CT) images and provide bladder‐volume‐based dose prediction models.Methods: Nineteen patients (475 daily‐CTs) comprised the study group, and five patients comprised the validation set (25 daily‐CTs). Target volumes and OARs were delineated on daily‐CT images and the treatment plan was recalculated accordingly. The deviation from the planning bladder volume (DVB), the correlation between DVB and clinical (CTV)/planning (PTV) target volume in terms of prescribed dose coverage, and the relationship of small bowel volume and bladder dose with the ratio of bladder volume (RVB) were analyzed.Results: In all cases, the prescribed dose coverage in the CTV was >95% when DVB was 95% when RVB was 45 Gy to the planning bladder V_{45 Gy} (RBV₄₅) exhibited a negative linear relationship with RVB (RBV₄₅ = −0.18*RVB + 120.8; R² = 0.80). Moreover, the ratio of small bowel volume to planning small bowel volume (RVS) exhibited a negative linear relationship with RVB (RVS = −1.06*RVB +217.59; R² = 0.41). The validation set results showed that the linear model predicted well the effects of bladder volume changes on target volume coverage and bladder dose.Conclusions: This study assessed dosimetry and volume effects of bladder filling on target and OARs based on daily‐CT images. We established a quantitative relationship between these parameters, providing dose prediction models for cervical cancer radiotherapy.