OBJECTIVES: To explore the synthesis of high-quality CT (sCT) from cone-beam CT (CBCT) using PE-CycleGAN for adaptive radiotherapy (ART) for nasopharyngeal carcinoma. METHODS: A perception-enhanced CycleGAN model "PE-CycleGAN" was proposed, introducing dual-contrast discriminator loss, multi-perceptual generator loss, and improved U-Net structure. CBCT and CT data from 80 nasopharyngeal carcinoma patients were used as the training set, with 7 cases as the test set. By quantifying the mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), as well as the dose gamma pass rate and the relative dose deviations of the target area and organs at risk (OAR) between sCT and reference CT, the image quality and dose calculation accuracy of sCT were evaluated. RESULTS: The MAE of sCT generated by PE-CycleGAN compared to the reference CT was (56.89±13.84) HU, approximately 30% lower than CBCT's (81.06±15.86) HU (P<0.001). PE-CycleGAN's PSNR and SSIM were 26.69±2.41dB and 0.92±0.02 respectively, significantly higher than CBCT's 21.54±2.37dB and 0.86±0.05 (P<0.001), indicating substantial improvements in image quality and structural similarity. In gamma analysis, under the 2 mm/2% criterion, PE-CycleGAN's sCT achieved a pass rate of (90.13±3.75)%, significantly higher than CBCT's (81.65±3.92)% (P<0.001) and CycleGAN's (87.69±3.50)% (P<0.05). Under the 3 mm/3% criterion, PE-CycleGAN's sCT pass rate of (90.13±3.75)% was also significantly superior to CBCT's (86.92±3.51)% (P<0.001) and CycleGAN's (94.58±2.23)% (P<0.01). The mean relative dose deviation of the target area and OAR between sCT and planned CT was within ±3% for all regions, except for the Lens Dmax (Gy), which had a deviation of 3.38% (P=0.09). The mean relative dose deviations for PTVnx HI, PTVnd HI, PTVnd CI, PTV1 HI, PRV_SC, PRV_BS, Parotid, Larynx, Oral, Mandible, and PRV_ON were all less than ±1% (P>0.05). CONCLUSIONS PE-CycleGAN demonstrates the ability to rapidly synthesize high-quality sCT from CBCT, offering a promising approach for CBCT-guided adaptive radiotherapy in nasopharyngeal carcinoma.
Humans ; Cone-Beam Computed Tomography/methods* ; Nasopharyngeal Neoplasms/diagnostic imaging* ; Nasopharyngeal Carcinoma/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Radiotherapy Dosage ; Signal-To-Noise Ratio ; Radiotherapy, Intensity-Modulated

OBJECTIVES: To propose a new method for optimizing radiotherapy planning for lung cancer by incorporating prognostic models that take into account individual patient information and assess the feasibility of treatment planning optimization directly guided by minimizing the predicted prognostic risk. METHODS: A mixed fluence map optimization objective was constructed, incorporating the outcome-based objective and the physical dose constraints. The outcome-based objective function was constructed as an equally weighted summation of prognostic prediction models for local control failure, radiation-induced cardiac toxicity, and radiation pneumonitis considering clinical risk factors. These models were derived using Cox regression analysis or Logistic regression. The primary goal was to minimize the outcome-based objective with the physical dose constraints recommended by the clinical guidelines. The efficacy of the proposed method for optimizing treatment plans was tested in 15 cases of non-small cell lung cancer in comparison with the conventional dose-based optimization method (clinical plan), and the dosimetric indicators and predicted prognostic outcomes were compared between different plans. RESULTS: In terms of the dosemetric indicators, D_95% of the planning target volume obtained using the proposed method was basically consistent with that of the clinical plan (100.33% vs 102.57%, P=0.056), and the average dose of the heart and lungs was significantly decreased from 9.83 Gy and 9.50 Gy to 7.02 Gy (t=4.537, P<0.05) and 8.40 Gy (t=4.104, P<0.05), respectively. The predicted probability of local control failure was similar between the proposed plan and the clinical plan (60.05% vs 59.66%), while the probability of radiation-induced cardiac toxicity was reduced by 1.41% in the proposed plan. CONCLUSIONS The proposed optimization method based on a mixed objective function of outcome prediction and physical dose provides effective protection against normal tissue exposure to improve the outcomes of lung cancer patients following radiotherapy.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods* ; Prognosis ; Radiotherapy, Intensity-Modulated/methods* ; Carcinoma, Non-Small-Cell Lung/radiotherapy* ; Radiotherapy Dosage ; Female ; Male ; Middle Aged

Definitive treatment of lung cancer with radiotherapy is challenging, as respiratory motion and anatomical changes can increase the risk of severe off-target effects during radiotherapy. Online adaptive radiotherapy (ART) is an evolving approach that enables timely modification of a treatment plan during the interfraction of radiotherapy, in response to physiologic or anatomic variations, aiming to improve the dose distribution for precise targeting and delivery in lung cancer patients. The effectiveness of online ART depends on the seamless integration of multiple components: sufficient quality of linear accelerator-integrated imaging guidance, deformable image registration, automatic recontouring, and efficient quality assurance and workflow. This review summarizes the present status of online ART for lung cancer, including key technologies, as well as the challenges and areas of active research in this field.
Humans ; Lung Neoplasms/radiotherapy* ; Radiotherapy Planning, Computer-Assisted/methods*

Cancer represents a major worldwide disease burden marked by escalating incidence and mortality. While therapeutic advances persist, developing safer and precisely targeted modalities remains imperative. Nanomedicines emerges as a transformative paradigm leveraging distinctive physicochemical properties to achieve tumor-specific drug delivery, controlled release, and tumor microenvironment modulation. By synergizing passive enhanced permeation and retention effect-driven accumulation and active ligand-mediated targeting, nanoplatforms enhance pharmacokinetics, promote tumor microenvironment enrichment, and improve cellular internalization while mitigating systemic toxicity. Despite revolutionizing cancer therapy through enhanced treatment efficacy and reduced adverse effects, translational challenges persist in manufacturing scalability, longterm biosafety, and cost-efficiency. This review systematically analyzes cutting-edge nanoplatforms, including polymeric, lipidic, biomimetic, albumin-based, peptide engineered, DNA origami, and inorganic nanocarriers, while evaluating their strategic advantages and technical limitations across three therapeutic domains: immunotherapy, chemotherapy, and radiotherapy. By assessing structure-function correlations and clinical translation barriers, this work establishes mechanistic and translational references to advance oncological nanomedicine development.
Humans ; Neoplasms/radiotherapy* ; Immunotherapy/methods* ; Nanoparticles/chemistry* ; Animals ; Nanomedicine/methods* ; Drug Delivery Systems/methods* ; Drug Carriers/chemistry* ; Radiotherapy/methods*

Immunotherapy has been widely used in cancer treatment in recent years and functions by stimulating the immune system to kill tumor cells. Radiation therapy (RT) uses radiation to induce DNA damage and kill tumor cells. However, this activates the body's immune system, promoting the release of tumor-related antigens from inactive dendritic cells, which stimulates the recurrence and metastasis of tumors in immune system tissues. The combination of RT and immunotherapy has been increasingly evaluated in recent years, with studies confirming the synergistic effect of the two antitumor therapies. Particularly, the combination of RT by dose adjustment with different immunotherapies has positive implications on antitumor immunity as well as disease prognosis compared with respective monotherapies. This review summarizes the current research status, progress, and prospects of RT combined with immunotherapy in cancer treatment. It additionally discusses the prevalent concerns regarding the dose, time window, and toxicity of this combination therapy.
Humans ; Neoplasms/radiotherapy* ; Immunotherapy/methods* ; Combined Modality Therapy ; Radiotherapy/methods*

This paper aims to propose a noninvasive radiotherapy patient positioning system based on structured light surface imaging, and evaluate its clinical feasibility. First, structured light sensors were used to obtain the panoramic point clouds during radiotherapy positioning in real time. The fusion of different point clouds and coordinate transformation were realized based on optical calibration and pose estimation, and the body surface was segmented referring to the preset region of interest (ROI). Then, the global-local registration of cross-source point cloud was achieved based on algorithms such as random sample consensus (RANSAC) and iterative closest point (ICP), to calculate 6 degrees of freedom (DoF) positioning deviation and provide guidance for the correction of couch shifts. The evaluation of the system was carried out based on a rigid adult phantom and volunteers' body, which included positioning error, correlation analysis, and receiver operating characteristic (ROC) analysis. Using Cone Beam CT (CBCT) as the gold standard, the maximum translation and rotation errors of this system were (1.5 ± 0.9) mm along Vrt direction (chest) and (0.7 ± 0.3) ° along Pitch direction (head and neck). The Pearson correlation coefficient between results of system outputs and CBCT verification distributed in an interval of [0.80, 0.84]. Results of ROC analysis showed that the translational and rotational AUC values were 0.82 and 0.85, respectively. In the 4D freedom accuracy test on the human body of volunteers, the maximum translation and rotation errors were (2.6 ± 1.1) mm (Vrt direction, chest and abdomen) and (0.8 ± 0.4)° (Rtn direction, chest and abdomen) respectively. In summary, the positioning system based on structured light body surface imaging proposed in this article can ensure positioning accuracy without surface markers and additional doses, and is feasible for clinical application.
Humans ; Patient Positioning/methods* ; Phantoms, Imaging ; Cone-Beam Computed Tomography ; Algorithms ; Radiotherapy, Image-Guided/methods* ; Radiotherapy Planning, Computer-Assisted/methods*

To address the issues of difficulty in preserving anatomical structures, low realism of generated images, and loss of high-frequency image information in medical image cross-modal translation, this paper proposes a medical image cross-modal translation method based on diffusion generative adversarial networks. First, an unsupervised translation module is used to convert magnetic resonance imaging (MRI) into pseudo-computed tomography (CT) images. Subsequently, a nonlinear frequency decomposition module is used to extract high-frequency CT images. Finally, the pseudo-CT image is input into the forward process, while the high-frequency CT image as a conditional input is used to guide the reverse process to generate the final CT image. The proposed model is evaluated on the SynthRAD2023 dataset, which is used for CT image generation for radiotherapy planning. The generated brain CT images achieve a Fréchet Inception Distance (FID) score of 33.159 7, a structure similarity index measure (SSIM) of 89.84%, a peak signal-to-noise ratio (PSNR) of 35.596 5 dB, and a mean squared error (MSE) of 17.873 9. The generated pelvic CT images yield an FID score of 33.951 6, a structural similarity index of 91.30%, a PSNR of 34.870 7 dB, and an MSE of 17.465 8. Experimental results show that the proposed model generates highly realistic CT images while preserving anatomical accuracy as much as possible. The transformed CT images can be effectively used in radiotherapy planning, further enhancing diagnostic efficiency.
Humans ; Tomography, X-Ray Computed/methods* ; Magnetic Resonance Imaging/methods* ; Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Brain/diagnostic imaging* ; Algorithms ; Radiotherapy Planning, Computer-Assisted ; Generative Adversarial Networks

The standard treatment for locally advanced esophageal squamous cell carcinoma (ESCC) is neoadjuvant chemoradiotherapy, followed by surgery or definitive radiotherapy, but clinical results are unsatisfactory. In recent years, relevant studies have shown that immunotherapy combined with chemoradiotherapy has become a new treatment option for locally advanced ESCC. This article summarizes the current progress of chemoradiotherapy combined with immunotherapy in the treatment of locally advanced ESCC, and provides necessary theoretical basis for the comprehensive understanding and optimization of chemoradiotherapy combined with immunotherapy regimens for ESCC.
Humans ; Esophageal Squamous Cell Carcinoma/therapy* ; Esophageal Neoplasms/radiotherapy* ; Immunotherapy/methods* ; Chemoradiotherapy/methods* ; Combined Modality Therapy

Small cell lung cancer (SCLC) is the thoracic malignant tumor and accounts for about 15% of lung malignancies and transfer often occurs by the time of diagnosis. Extensive stage-small cell lung cancer (ES-SCLC) accounts for about 2/3 of all SCLC. For many years, radiotherapy has occupied an important position in the treatment of SCLC, especially in the treatment of ES-SCLC, because SCLC is more sensitive to radiotherapy. However, in recent years, immune checkpoint inhibitor has shown more excellent antitumor activity in the treatment of ES-SCLC and become the mainstream argument for the treatment of ES-SCLC. However, will radiotherapy be buried by the times among the therapeutic approaches for ES-SCLC? In this article, we will review the clinical progress of radiotherapy, immunotherapy and combination therapy for ES-SCLC.
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Humans ; Small Cell Lung Carcinoma/therapy* ; Lung Neoplasms/therapy* ; Immunotherapy ; Neoplasm Staging ; Radiotherapy/methods* ; Combined Modality Therapy

Tianhe procedure^® is a functional sphincter-preserving surgical approach developed for rectal cancer patients following radiotherapy. This technique involves extended proximal resection of the colon beyond the pelvic cavity, followed by anastomosis of the non-irradiated proximal colon to the distal rectum or anal canal. This strategy aims to reduce the incidence of anastomotic complications and postoperative bowel dysfunction. However, there is currently a lack of standardized practice guideline for implementing Tianhe procedure^® in China. Therefore, the Chinese Radiation Intestinal Injury Research Group, the Colorectal Surgery Group of Surgery Branch of the Chinese Medical Association, the Anorectal Branch of Chinese Medical Doctor Association, the Colorectal Cancer Committee of the Chinese Medical Doctor Association, and the Colorectal Cancer Committee of China Anti-cancer Association, and the Gastrointestinal Surgical Branch of Guangdong Medical Doctor Association, have jointly convened a panel of national experts to discuss and establish this standardized surgical procedure. This standard, based on the latest evidence from literature, research advancements, and expert experience, focuses on key aspects of the Tianhe procedure^®, including its precise definition, indications, critical procedural steps, postoperative complications, and functional rehabilitation strategies. It aims to promote standardized implementation and broader clinical adoption of this innovative surgical technique.
Humans ; Rectal Neoplasms/radiotherapy* ; Anal Canal/surgery* ; Anastomosis, Surgical/methods* ; Organ Sparing Treatments/methods* ; Rectum/surgery* ; Postoperative Complications/prevention & control* ; Digestive System Surgical Procedures/methods*