Physics and Imaging in Radiation Oncology最新文献

Background and purpose

Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.

Material and methods

Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7) mm.

Results

Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11) mm with a prediction horizon of 500 ms.

Conclusion

It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.

Multi-criteria optimization (MCO) is a method that was added to treatment planning to create high-quality treatment plans. This study aimed to investigate the effectiveness of MCO in combination with knowledge-based planning (KBP) in radiotherapy for left-sided breasts, including regional nodes. Dose/volume parameters were evaluated for manual plans (MP), KBP, and KBP + MCO. Planning target volume doses of MP had better coverage while KBP + MCO plans demonstrated the lowest organ at risk doses. KBP and KBP + MCO plans had increasing complexity as expressed in the number of monitor units.

Background and Purpose

Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features.

Methods and materials

We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient $\left(\mathrm{DSC}\right)<T$ and $\mathrm{DSC}⩾T$. Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing.

Results

Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC (${\mathrm{SDSC}}_{3\mathrm{mm}}$), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16, ${\mathrm{SDSC}}_{3\mathrm{mm}}$=0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation ($M$=0) on the test cohort. Failure detection could generate precision ($P=0.88$), recall ($R=0.75$), F1-score ($F=0.81$), and accuracy ($A=0.86$) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values.

Conclusions

Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.

This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p < 0.001) and T2w MR (4.41 vs. 7.42, p < 0.001).

Background and purpose

Ion beams exhibit an increased relative biological effectiveness (RBE) with respect to photons. This study determined the RBE of oxygen ion beams as a function of linear energy transfer (LET) and dose in the rat spinal cord.

Materials and methods

The spinal cord of rats was irradiated at four different positions of a 6 cm spread-out Bragg-peak (LET: 26, 66, 98 and 141 keV/µm) using increasing levels of single and split oxygen ion doses. Dose-response curves were established for the endpoint paresis grade II and based on ED₅₀ (dose at 50 % effect probability), the RBE was determined and compared to model predictions.

Results

When LET increased from 26 to 98 keV/µm, ED₅₀ decreased from 17.2 ± 0.3 Gy to 13.5 ± 0.4 Gy for single and from 21.7 ± 0.4 Gy to 15.5 ± 0.5 Gy for split doses, however, at 141 keV/µm, ED₅₀ rose again to 15.8 ± 0.4 Gy and 17.2 ± 0.4 Gy, respectively. As a result, the RBE increased from 1.43 ± 0.05 to 1.82 ± 0.08 (single dose) and from 1.58 ± 0.04 to 2.21 ± 0.08 (split dose), respectively, before declining again to 1.56 ± 0.06 for single and 1.99 ± 0.06 for split doses at the highest LET. Deviations from RBE-predictions were model-dependent.

Conclusion

This study established first RBE data for the late reacting central nervous system after single and split doses of oxygen ions. The data was used to validate the RBE-dependence on LET and dose of three RBE-models. This study extends the existing data base for protons, helium and carbon ions and provides important information for future patient treatments with oxygen ions.

Background and purpose

Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs).

Materials and methods

Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient >0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance.

Results

Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization.

Conclusions

To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.

Background and purpose

Motion management techniques are important to spare the healthy tissue adequately. However, they are complex and need dedicated quality assurance. The aim of this study was to create a dynamic phantom designed for quality assurance and to replicate a patient’s size, anatomy, and tissue density.

Materials and methods

A computed tomography (CT) scan of a cancer patient was used to create molds for the lungs, heart, ribs, and vertebral column via additive manufacturing. A pump system and software were developed to simulate respiratory dynamics. The extent of respiratory motion was quantified using a 4DCT scan. End-to-end tests were conducted to evaluate two motion management techniques for lung stereotactic body radiotherapy (SBRT).

Results

The chest wall moved between 4 mm and 13 mm anteriorly and 2 mm to 7 mm laterally during the breathing. The diaphragm exhibited superior-inferior movement ranging from 5 mm to 16 mm in the left lung and 10 mm to 36 mm in the right lung. The left lung tumor displaced ± 7 mm superior-inferiorly and anterior-posteriorly. The CT numbers were for lung: −716 ± 108 HU (phantom) and −713 ± 70 HU (patient); bone: 460 ± 20 HU (phantom) and 458 ± 206 HU (patient); soft tissue: 92 ± 9 HU (phantom) and 60 ± 25 HU (patient). The end-to-end testing showed an excellent agreement between the measured and the calculated dose for ion chamber and film dosimetry.

Conclusions

The phantom is recommended for quality assurance, evaluating the institution’s specific planning and motion management strategies either through end-to-end testing or as an external audit phantom.

Background and purpose

Retrospective dose evaluation for organ-at-risk auto-contours has previously used small cohorts due to additional manual effort required for treatment planning on auto-contours. We aimed to do this at large scale, by a) proposing and assessing an automated plan optimization workflow that used existing clinical plan parameters and b) using it for head-and-neck auto-contour dose evaluation.

Materials and methods

Our automated workflow emulated our clinic’s treatment planning protocol and reused existing clinical plan optimization parameters. This workflow recreated the original clinical plan ($P_{\mathrm{OG}}$) with manual contours ($P_{\mathrm{MC}}$) and evaluated the dose effect ($P_{\mathrm{OG}}-P_{\mathrm{MC}}$) on 70 photon and 30 proton plans of head-and-neck patients. As a use-case, the same workflow (and parameters) created a plan using auto-contours ($P_{\mathrm{AC}}$) of eight head-and-neck organs-at-risk from a commercial tool and evaluated their dose effect ($P_{\mathrm{MC}}-P_{\mathrm{AC}}$).

Results

For plan recreation ($P_{\mathrm{OG}}-P_{\mathrm{MC}}$), our workflow had a median impact of 1.0% and 1.5% across dose metrics of auto-contours, for photon and proton respectively. Computer time of automated planning was 25% (photon) and 42% (proton) of manual planning time. For auto-contour evaluation ($P_{\mathrm{MC}}-P_{\mathrm{AC}}$), we noticed an impact of 2.0% and 2.6% for photon and proton radiotherapy. All evaluations had a median $\Delta$NTCP (Normal Tissue Complication Probability) less than 0.3%.

Conclusions

The plan replication capability of our automated program provides a blueprint for other clinics to perform auto-contour dose evaluation with large patient cohorts. Finally, despite geometric differences, auto-contours had a minimal median dose impact, hence inspiring confidence in their utility and facilitating their clinical adoption.

Background and purpose

Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices.

Materials and methods

A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI.

Results

Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician’s contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians’ contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours.

Conclusion

The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.

Background and purpose

Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment.

Materials and methods

Nineteen patients who received 30–34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05.

Results

Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9–76.8 %) and a median MDA of 5.1 mm (IQR 3.4–7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0–4.8 %). DSC was 0.97 (IQR 0.94–0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V_32.5 Gy and mean dose. The CTV V_95% changed from 99.9 % (IQR 98.8–100 %) to 99.6 % (IQR 93.9–100 %).

Conclusion

Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.