Rapid unpaired CBCT-based synthetic CT for CBCT-guided adaptive radiotherapy.

In this work, we demonstrate a method for rapid synthesis of high-quality CT images from unpaired, low-quality CBCT images, permitting CBCT-based adaptive radiotherapy. We adapt contrastive unpaired translation (CUT) to be used with medical images and evaluate the results on an institutional pelvic CT dataset. We compare the method against cycleGAN using mean absolute error, structural similarity index, root mean squared error, and Frèchet Inception Distance and show that CUT significantly outperforms cycleGAN while requiring less time and fewer resources. The investigated method improves the feasibility of online adaptive radiotherapy over the present state-of-the-art.

A review on medical imaging synthesis using deep learning and its clinical applications.

This paper reviewed the deep learning-based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning-based methods in inter- and intra-modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Medical imaging is widely used in the diagnosis and treatment of cancer, and artificial intelligence (AI) has achieved tremendous success in medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential applications of AI in tumor subregion analysis are discussed.

Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.

PURPOSE: Positron Emission Tomography (PET) has been a commonly used imaging modality in broad clinical applications. One of the most important tradeoffs in PET imaging is between image quality and radiation dose: high image quality comes with high radiation exposure. Improving image quality is desirable for all clinical applications while minimizing radiation exposure is needed to reduce risk to patients. METHODS: We introduce PET Consistency Model (PET-CM), an efficient diffusion-based method for generating high-quality full-dose PET images from low-dose PET images. It employs a two-step process, adding Gaussian noise to full-dose PET images in the forward diffusion, and then denoising them using a PET Shifted-window Vision Transformer (PET-VIT) network in the reverse diffusion. The PET-VIT network learns a consistency function that enables direct denoising of Gaussian noise into clean full-dose PET images. PET-CM achieves state-of-the-art image quality while requiring significantly less computation time than other methods. Evaluation with normalized mean absolute error (NMAE), peak signal-to-noise ratio (PSNR), multi-scale structure similarity index (SSIM), normalized cross-correlation (NCC), and clinical evaluation including Human Ranking Score (HRS) and Standardized Uptake Value (SUV) Error analysis shows its superiority in synthesizing full-dose PET images from low-dose inputs. RESULTS: In experiments comparing eighth-dose to full-dose images, PET-CM demonstrated impressive performance with NMAE of 1.278 ± 0.122%, PSNR of 33.783 ± 0.824 dB, SSIM of 0.964 ± 0.009, NCC of 0.968 ± 0.011, HRS of 4.543, and SUV Error of 0.255 ± 0.318%, with an average generation time of 62 s per patient. This is a significant improvement compared to the state-of-the-art diffusion-based model with PET-CM reaching this result 12× faster. Similarly, in the quarter-dose to full-dose image experiments, PET-CM delivered competitive outcomes, achieving an NMAE of 0.973 ± 0.066%, PSNR of 36.172 ± 0.801 dB, SSIM of 0.984 ± 0.004, NCC of 0.990 ± 0.005, HRS of 4.428, and SUV Error of 0.151 ± 0.192% using the same generation process, which underlining its high quantitative and clinical precision in both denoising scenario. CONCLUSIONS: We propose PET-CM, the first efficient diffusion-model-based method, for estimating full-dose PET images from low-dose images. PET-CM provides comparable quality to the state-of-the-art diffusion model with higher efficiency. By utilizing this approach, it becomes possible to maintain high-quality PET images suitable for clinical use while mitigating the risks associated with radiation. The code is availble at https://github.com/shaoyanpan/Full-dose-Whole-body-PET-Synthesis-from-Low-dose-PET-Using-Consistency-Model.

CBCT-Based synthetic CT image generation using conditional denoising diffusion probabilistic model.

BACKGROUND: Daily or weekly cone-beam computed tomography (CBCT) scans are commonly used for accurate patient positioning during the image-guided radiotherapy (IGRT) process, making it an ideal option for adaptive radiotherapy (ART) replanning. However, the presence of severe artifacts and inaccurate Hounsfield unit (HU) values prevent its use for quantitative applications such as organ segmentation and dose calculation. To enable the clinical practice of online ART, it is crucial to obtain CBCT scans with a quality comparable to that of a CT scan. PURPOSE: This work aims to develop a conditional diffusion model to perform image translation from the CBCT to the CT distribution for the image quality improvement of CBCT. METHODS: The proposed method is a conditional denoising diffusion probabilistic model (DDPM) that utilizes a time-embedded U-net architecture with residual and attention blocks to gradually transform the white Gaussian noise sample to the target CT distribution conditioned on the CBCT. The model was trained on deformed planning CT (dpCT) and CBCT image pairs, and its feasibility was verified in brain patient study and head-and-neck (H&N) patient study. The performance of the proposed algorithm was evaluated using mean absolute error (MAE), peak signal-to-noise ratio (PSNR) and normalized cross-correlation (NCC) metrics on generated synthetic CT (sCT) samples. The proposed method was also compared to four other diffusion model-based sCT generation methods. RESULTS: In the brain patient study, the MAE, PSNR, and NCC of the generated sCT were 25.99 HU, 30.49 dB, and 0.99, respectively, compared to 40.63 HU, 27.87 dB, and 0.98 of the CBCT images. In the H&N patient study, the metrics were 32.56 HU, 27.65 dB, 0.98 and 38.99 HU, 27.00, 0.98 for sCT and CBCT, respectively. Compared to the other four diffusion models and one Cycle generative adversarial network (Cycle GAN), the proposed method showed superior results in both visual quality and quantitative analysis. CONCLUSIONS: The proposed conditional DDPM method can generate sCT from CBCT with accurate HU numbers and reduced artifacts, enabling accurate CBCT-based organ segmentation and dose calculation for online ART.

Landmark tracking in liver US images using cascade convolutional neural networks with long short-term memory.

Accurate tracking of anatomic landmarks is critical for motion management in liver radiation therapy. Ultrasound (US) is a safe, low-cost technology that is broadly available and offer real-time imaging capability. This study proposed a deep learning-based tracking method for the US image-guided radiation therapy. The proposed cascade deep learning model is composed of an attention network, a mask region-based convolutional neural network (mask R-CNN), and a long short-term memory (LSTM) network. The attention network learns a mapping from an US image to a suspected area of landmark motion in order to reduce the search region. The mask R-CNN then produces multiple region-of-interest proposals in the reduced region and identifies the proposed landmark via three network heads: bounding box regression, proposal classification, and landmark segmentation. The LSTM network models the temporal relationship among the successive image frames for bounding box regression and proposal classification. To consolidate the final proposal, a selection method is designed according to the similarities between sequential frames. The proposed method was tested on the liver US tracking datasets used in the medical image computing and computer assisted interventions 2015 challenges, where the landmarks were annotated by three experienced observers to obtain their mean positions. Five-fold cross validation on the 24 given US sequences with ground truths shows that the mean tracking error for all landmarks is 0.65 ± 0.56 mm, and the errors of all landmarks are within 2 mm. We further tested the proposed model on 69 landmarks from the testing dataset that have the similar image pattern with the training pattern, resulting in a mean tracking error of 0.94 ± 0.83 mm. The proposed deep-learning model was implemented on a graphics processing unit (GPU), tracking 47-81 frames s-1. Our experimental results have demonstrated the feasibility and accuracy of our proposed method in tracking liver anatomic landmarks using US images, providing a potential solution for real-time liver tracking for active motion management during radiation therapy.

Impact of Age on Overall Survival Among Children With Wilms Tumor: A Population-based Registry Analysis.

OBJECTIVES: International trials have reported conflicting findings on whether the association between age and worse overall survival (OS) among children with Wilms tumor (WT) is due to age as an independent prognostic factor or the observation of more advanced disease at older ages. We sought to further elucidate this relationship using a population-based registry analysis. METHODS: The Surveillance, Epidemiology, and End Results database was queried for all patients diagnosed with WT under the age of 20. The association between age and OS was assessed using multivariable Cox proportional hazards regression. RESULTS: In this study, 3463 patients (54% female) were diagnosed with WT between 1975 and 2016. More advanced stage, larger primary tumor size, lymph node involvement, disease requiring radiotherapy, and omission of surgery were associated with worse OS ( P <0.05). More advanced stage, larger primary tumor size, and disease requiring radiotherapy were also associated with older age, whereas bilateral disease was associated with younger age ( P <0.001). On average, each year of age conferred an incremental hazard ratio (HR) of 1.07 (95% CI, 1.01 to 1.12, P =0.018) independent of relevant covariates. The rise in adjusted OS HR was most pronounced after the transitions in diagnosis age from 2 to 3 (HR age 3-15 vs. 0-2 1.77, 95% CI, 1.11 to 2.82, P =0.016) and from 15 to 16 (HR age 16-19 vs. 3-15 2.58, 95% CI, 1.06 to 6.25, P =0.036). CONCLUSIONS: Diagnosis of pediatric WT at an older age was found to be independently associated with worse OS. Although additional prospective studies are warranted to examine tumor biology and other potential correlates, more aggressive treatment of older children based on age, especially as they approach early adulthood, may be considered in the multidisciplinary management of WT.

Stereotactic ablative radiotherapy (SABR) for treatment of central and ultra-central lung tumors.

OBJECTIVES: Treatment of central and ultra-central lung tumors with stereotactic ablative radiotherapy (SABR) remains controversial due to risks of treatment-related toxicities compared with peripheral tumors. Here we report our institution's experience in treating central and ultra-central lung tumor patients with SABR. MATERIALS AND METHODS: We retrospectively reviewed outcomes in 68 patients with single lung tumors, 34 central and 34 peripheral, all treated with SABR consisting of 50 Gy in 4-5 fractions. Tumor centrality was defined per the RTOG 0813 protocol. We defined "ultra-central" tumors as those with GTV directly abutting the central airway. RESULTS: Median follow-up time was 18.4 months and median overall survival was 38.1 months. Two-year overall survival was similar between ultra-central, central, and peripheral NSCLC (80.0% vs. 63.2% vs. 86.6%, P=0.62), as was 2-year local failure (0% vs. 10.0% vs. 16.3%, P=0.64). Toxicity rates were low and comparable between the three groups, with only two cases of grade 3 toxicity (chest wall pain), and one case of grade 4 toxicity (pneumonitis) observed. Patients with ultra-central tumors experienced no symptomatic toxicities over a median follow-up time of 23.6 months. Dosimetric analysis revealed that RTOG 0813 central airway dose constraints were frequently not achieved in central tumor treatment plans, but this did not correlate with increased toxicity rate. CONCLUSION: Patients with central and ultra-central lung tumors treated with SABR (50 Gy in 4-5 fractions) experienced few toxicities and good outcomes, similar to patients with peripheral lung tumors.

An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage.

Circulating tumor DNA (ctDNA) is a promising biomarker for noninvasive assessment of cancer burden, but existing ctDNA detection methods have insufficient sensitivity or patient coverage for broad clinical applicability. Here we introduce cancer personalized profiling by deep sequencing (CAPP-Seq), an economical and ultrasensitive method for quantifying ctDNA. We implemented CAPP-Seq for non-small-cell lung cancer (NSCLC) with a design covering multiple classes of somatic alterations that identified mutations in >95% of tumors. We detected ctDNA in 100% of patients with stage II-IV NSCLC and in 50% of patients with stage I, with 96% specificity for mutant allele fractions down to ∼0.02%. Levels of ctDNA were highly correlated with tumor volume and distinguished between residual disease and treatment-related imaging changes, and measurement of ctDNA levels allowed for earlier response assessment than radiographic approaches. Finally, we evaluated biopsy-free tumor screening and genotyping with CAPP-Seq. We envision that CAPP-Seq could be routinely applied clinically to detect and monitor diverse malignancies, thus facilitating personalized cancer therapy.

Feasibility and potential utility of multicomponent exhaled breath analysis for predicting development of radiation pneumonitis after stereotactic ablative radiotherapy.

INTRODUCTION: In this prospective pilot study, we evaluated the feasibility and potential utility of measuring multiple exhaled gases as biomarkers of radiation pneumonitis (RP) in patients receiving stereotactic ablative radiotherapy (SABR) for lung tumors. METHODS: Breath analysis was performed for 26 patients receiving SABR for lung tumors. Concentrations of exhaled nitric oxide (eNO), carbon monoxide (eCO), nitrous oxide (eN2O), and carbon dioxide (eCO2) were measured before and immediately after each fraction using real-time, infrared laser spectroscopy. RP development (CTCAE grade ≥2) was correlated with baseline gas concentrations, acute changes in gas concentrations after each SABR fraction, and dosimetric parameters. RESULTS: Exhaled breath analysis was successfully completed in 77% of patients. Five of 20 evaluable patients developed RP at a mean of 5.4 months after SABR. Acute changes in eNO and eCO concentrations, defined as percent changes between each pre-fraction and post-fraction measurement, were significantly smaller in RP versus non-RP cases (p = 0.022 and 0.015, respectively). In an exploratory analysis, a combined predictor of baseline eNO greater than 24 parts per billion and acute decrease in eCO less than 5.5% strongly correlated with RP incidence (p =0.0099). Neither eN2O nor eCO2 concentrations were significantly associated with RP development. Although generally higher in patients destined to develop RP, dosimetric parameters were not significantly associated with RP development. CONCLUSIONS: The majority of SABR patients in this pilot study were able to complete exhaled breath analysis. Baseline concentrations and acute changes in concentrations of exhaled breath components were associated with RP development after SABR. If our findings are validated, exhaled breath analysis may become a useful approach for noninvasive identification of patients at highest risk for developing RP after SABR.