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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.
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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.
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The open-shell single covalent bond composed of two electrons is unstable under normal conditions, because the closed-shell electronic configuration is generally beneficial to minimize the energy of the system. This classical rule always governs the chemical bonding of s- and p-block homonuclear diatomic molecules, such as the stable σ2 electron-pair bonds in hydrogen. In this work, surprisingly, we found that the diversified open-shell single bonds between two f-block atoms (e.g., thorium) can be stabilized within a tight "carbon-confined-space" using relativistic quantum chemical calculations. We first identified a stable dithorium endohedral metallofullerene (EMF), Th26+@Ih-C806-, with a Th-Th distance of 3.803 Å inside the Ih-C80 cage, which displays a unique spin-polarized σ1π1 2-fold single-electron Th3+-Th3+ bond that is collaboratively dominated by 5f6d7s7p orbitals. The Th3+-Th3+ bond can further evolve into a 5f6d dominated spin-polarized π2 configuration by compressing the Th-Th distance further down to 2.843 Å, within a smaller Ih-C60 cage. On the other hand, elongating the Th-Th distance to 4.063 Å by encapsulating Th2 into a long diametric D3h-C78 fullerene returns the Th3+-Th3+ bond to the normal closed-shell (6d7s7p)σ2 form. Hence, a new rule is unambiguously revealed through the carbon-confinement induced spin-polarization of a single bond. The key point of this rule is the size of the carbon cage, because the squeezed effect is conducive to the effective overlap of the Th(5f) orbitals, reducing and further reversing the original large singlet-triplet energy gap of the Th26+ unit. This discovery provides pioneering guidance for exploring new chemical bonds and thorium-based endofullerenes.