Comparative analysis of dose calculation algorithms for CyberKnife-based stereotactic radiotherapy in lung cancer.

Purpose: The accuracy of dose calculation is the prerequisite for CyberKnife (CK) to implement precise stereotactic body radiotherapy (SBRT). In this study, CK-MLC treatment planning for early-stage non-small cell lung cancer (NSCLC) were compared using finite-size pencil beam (FSPB) algorithm, FSPB with lateral scaling option (FSPB_LS) and Monte Carlo (MC) algorithms, respectively. We concentrated on the enhancement of accuracy with the FSPB_LS algorithm over the conventional FSPB algorithm and the dose consistency with the MC algorithm. Methods: In this study, 54 cases of NSCLC were subdivided into central lung cancer (CLC, n=26) and ultra-central lung cancer (UCLC, n=28). For each patient, we used the FSPB algorithm to generate a treatment plan. Then the dose was recalculated using FSPB_LS and MC dose algorithms based on the plans computed using the FSPB algorithm. The resultant plans were assessed by calculating the mean value of pertinent comparative parameters, including PTV prescription isodose, conformity index (CI), homogeneity index (HI), and dose-volume statistics of organs at risk (OARs). Results: In this study, most dose parameters of PTV and OARs demonstrated a trend of MC > FSPB_LS > FSPB. The FSPB_LS algorithm aligns better with the dose parameters of the target compared to the MC algorithm, which is particularly evident in UCLC. However, the FSPB algorithm significantly underestimated the does of the target. Regarding the OARs in CLC, differences in dose parameters were observed between FSPB and FSPB_LS for V10 of the contralateral lung, as well as between FSPB and MC for mean dose (Dmean) of the contralateral lung and maximum dose (Dmax) of the aorta, exhibiting statistical differences. There were no statistically significant differences observed between FSPB_LS and MC for the OARs. However, the average dose deviation between FSPB_LS and MC algorithms for OARs ranged from 2.79% to 11.93%. No significant dose differences were observed among the three algorithms in UCLC. Conclusion: For CLC, the FSPB_LS algorithm exhibited good consistency with the MC algorithm in PTV and demonstrated a significant improvement in accuracy when compared to the traditional FSPB algorithm. However, the FSPB_LS algorithm and the MC algorithm showed a significant dose deviation in OARs of CLC. In the case of UCLC, FSPB_LS showed better consistency with the MC algorithm than observed in CLC. Notwithstanding, UCLC's OARs were highly sensitive to radiation dose and could result in potentially serious adverse reactions. Consequently, it is advisable to use the MC algorithm for dose calculation in both CLC and UCLC, while the application of FSPB_LS algorithm should be carefully considered.

Influence and optimization strategy of the magnetic field in 1.5 T MR-linac liver stereotactic radiotherapy.

OBJECTIVE: To compare intensity reduction plans for liver cancer with or without a magnetic field and optimize field and subfield numbers in the intensity-modulated radiotherapy (IMRT) plans designed for liver masses in different regions. METHODS: This retrospective study included 62 patients who received radiotherapy for liver cancer at Shandong Cancer Hospital. Based on each patient's original individualized intensity-modulated plan (plan1.5 T), a magnetic field-free plan (plan0 T) and static intensity-modulated plan with four different optimization schemes were redesigned for each patient. The differences in dosimetric parameters among plans were compared. RESULTS: In the absence of a magnetic field in the first quadrant, PTV Dmin increased (97.75 ± 17.55 vs. 100.96 ± 22.78)%, Dmax decreased (121.48 ± 29.68 vs. 119.06 ± 28.52)%, D98 increased (101.35 ± 7.42 vs. 109.35 ± 26.52)% and HI decreased (1.14 ± 0.14 vs. 1.05 ± 0.01). In the absence of a magnetic field in the second quadrant, PTV Dmin increased (84.33 ± 19.74 vs. 89.96 ± 21.23)%, Dmax decreased (105 ± 25.08 vs. 104.05 ± 24.86)%, and HI decreased (1.04 ± 0.25 vs. 0.99 ± 0.24). In the absence of a magnetic field in the third quadrant, PTV Dmax decreased (110.21 ± 2.22 vs. 102.31 ± 26)%, L-P V30 decreased (10.66 ± 9.19 vs. 5.81 ± 3.22)%, HI decreased (1.09 ± 0.02 vs. 0.98 ± 0.25), and PTV Dmin decreased (92.12 ± 4.92 vs. 89.1 ± 22.35)%. In the absence of a magnetic field in the fourth quadrant, PTV Dmin increased (89.78 ± 6.72 vs. 93.04 ± 4.86)%, HI decreased (1.09 ± 0.01 vs. 1.05 ± 0.01) and D98 increased (99.82 ± 0.82 vs. 100.54 ± 0.84)%. These were all significant differences. In designing plans for tumors in each liver region, a total number of subfields in the first area of 60, total subfields in the second zone of 80, and total subfields in the third and fourth zones of 60 or 80 can achieve the dose effect without a magnetic field. CONCLUSION: In patients with liver cancer, the effect of a magnetic field on the target dose is more significant than that on doses to organs at risk. By controlling the max total number of subfields in different quadrants, the effect of the magnetic field can be greatly reduced or even eliminated.

Automatic Detection of Brain Metastases in T1-Weighted Construct-Enhanced MRI Using Deep Learning Model.

As a complication of malignant tumors, brain metastasis (BM) seriously threatens patients' survival and quality of life. Accurate detection of BM before determining radiation therapy plans is a paramount task. Due to the small size and heterogeneous number of BMs, their manual diagnosis faces enormous challenges. Thus, MRI-based artificial intelligence-assisted BM diagnosis is significant. Most of the existing deep learning (DL) methods for automatic BM detection try to ensure a good trade-off between precision and recall. However, due to the objective factors of the models, higher recall is often accompanied by higher number of false positive results. In real clinical auxiliary diagnosis, radiation oncologists are required to spend much effort to review these false positive results. In order to reduce false positive results while retaining high accuracy, a modified YOLOv5 algorithm is proposed in this paper. First, in order to focus on the important channels of the feature map, we add a convolutional block attention model to the neck structure. Furthermore, an additional prediction head is introduced for detecting small-size BMs. Finally, to distinguish between cerebral vessels and small-size BMs, a Swin transformer block is embedded into the smallest prediction head. With the introduction of the F2-score index to determine the most appropriate confidence threshold, the proposed method achieves a precision of 0.612 and recall of 0.904. Compared with existing methods, our proposed method shows superior performance with fewer false positive results. It is anticipated that the proposed method could reduce the workload of radiation oncologists in real clinical auxiliary diagnosis.

A Machine Learning-Based Predictive Model of Epidermal Growth Factor Mutations in Lung Adenocarcinomas.

Data from 758 patients with lung adenocarcinoma were retrospectively collected. All patients had undergone computed tomography imaging and EGFR gene testing. Radiomic features were extracted using the medical imaging tool 3D-Slicer and were combined with the clinical features to build a machine learning prediction model. The high-dimensional feature set was screened for optimal feature subsets using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO). Model prediction of EGFR mutation status in the validation group was evaluated using multiple classifiers. We showed that six clinical features and 622 radiomic features were initially collected. Thirty-one radiomic features with non-zero correlation coefficients were obtained by LASSO regression, and 24 features correlated with label values were obtained by PCA. The shared radiomic features determined by these two methods were selected and combined with the clinical features of the respective patient to form a subset of features related to EGFR mutations. The full dataset was partitioned into training and test sets at a ratio of 7:3 using 10-fold cross-validation. The area under the curve (AUC) of the four classifiers with cross-validations was: (1) K-nearest neighbor (AUCmean = 0.83, Acc = 81%); (2) random forest (AUCmean = 0.91, Acc = 83%); (3) LGBM (AUCmean = 0.94, Acc = 88%); and (4) support vector machine (AUCmean = 0.79, Acc = 83%). In summary, the subset of radiographic and clinical features selected by feature engineering effectively predicted the EGFR mutation status of this NSCLC patient cohort.