Authorship：Lead author   Publisher：Anticancer Research  

Background/Aim: Radiomics, which links radiological image features with patient prognoses, is expected to be applied for the prediction of the clinical outcomes of radiotherapy. We investigated the clinical and radiomic factors associated with recurrence patterns after stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC). Patients and Methods: We retrospectively analyzed 125 patients with histologically confirmed NSCLC who underwent SBRT between April 2003 and June 2017 at our institution. A radiomic score was calculated from five radiomics features (histogram and texture features) selected using the LASSO Cox regression model. These features were extracted from the gross tumor volume (GTV) in three-dimensional wavelet decomposition CT images. We used univariate and multivariate analyses to determine the associations between local control (LC) time and metastasis-free survival (MFS), clinical factors (age, sex, performance status, operability, smoking, histology, and tumor diameter), and the radiomic score. Results: With a median follow-up of 37 months, the following 3-year rates were observed: overall survival, 80.9%; progression-free survival, 61.7%; LC, 75.1%, and MFS; 74.5%. In multivariate analysis, histology (squamous cell carcinoma vs. non-squamous cell carcinoma, p=0.0045), tumor diameter (>3 cm vs. ≤3 cm, p=0.039); and radiomic score (>0.043 vs. ≤0.043, p=0.042) were significantly associated with LC, and the radiomic score (>0.304 vs. ≤0.304, p<0.001) was significantly associated with MFS. Conclusion: Histology, tumor diameter, and radiomic score could be significant factors for predicting NSCLC recurrence patterns after SBRT.

DOI： 10.21873/anticanres.16699

Scopus

Language：English   Publisher：Technical Innovations and Patient Support in Radiation Oncology  

Introduction: This study evaluated dosimetric values of cone beam computed tomography (CBCT)-guided online adaptive radiotherapy (oART) in patients with gastric mucosa-associated lymphoid tissue (MALT) lymphoma, accounting for interfractional and intrafractional motion. Methods: Four patients with stage I gastric MALT lymphoma received CBCT-guided oART. For each of the 60 treatment sessions, scheduled (SCH) and adapted (ADP) plans were generated. Dosimetric evaluation focused on clinical target volume (CTV) and organs at risk (OARs). Metrics included CTV D98 % and D95 %, mean dose to the liver and left and right kidneys, maximum dose to the spinal cord, and V5Gy for bilateral kidneys. Adaptive planning CBCT-based contours were propagated to synthetic CTs of SCH and ADP plans to assess interfractional motion. Post-treatment CBCT-based contours were propagated to synthetic CTs of the ADP plan to evaluate intrafractional motion. Results: ADP plans significantly improved CTV coverage: mean D98% increased from 94.7 % in the SCH plan to 98.6 %, and D95% from 97.3 % to 99.2 % (p < 0.001). Most OAR doses were reduced in the ADP plans, including bilateral kidney V5Gy (11.3 % vs. 8.3 %, p < 0.001) and spinal cord Dmax (9.8 Gy vs. 7.9 Gy, p < 0.001). Liver Dmean was slightly higher in the ADP plan (11.4 Gy vs. 11.1 Gy, p = 0.002). No significant differences were observed in CTV and OAR dosimetric parameters between adaptive planning and post-treatment CBCTs (e.g., CTV D98%: 98.6 % vs. 98.5 %, p = 0.629). Conclusion: CBCT-guided oART improved target coverage and maintained post-treatment dosimetric stability in gastric MALT lymphoma, supporting clinical feasibility.

DOI： 10.1016/j.tipsro.2025.100321

Web of Science

Scopus

PubMed

Publisher：Health and Technology  

Purpose: Applying new treatments to real patients to verify therapeutic efficacy may induce various risks, such as critical adverse events. Additionally, there are ethical and financial issues in real-world randomized controlled trials (RCTs). This study aimed to develop mathematical models of time-variant tumor growth trajectories (TGTs) for in silico RCTs targeting patients with stage I non-small cell lung cancer (NSCLC) to optimize stereotactic body radiation therapy (SBRT). Methods: The basic idea of the in silico RCT was to evaluate the endpoint of progression-free survival (PFS) curves for the two regimens derived from TGTs for virtual patient data produced via mathematical models. TGT models with a relative number of tumor cells were proposed by integrating the Bertalanffy-Pütter (BP) model and linear quadratic model into tumor growth models. To validate the proposed models, we performed three RCTs, 30 Gy/1 fraction (Fr) versus 60 Gy/3 Fr, 48 Gy/4 Fr versus 75 Gy/25 Fr, and 34 Gy/1 Fr versus 48 Gy/4 Fr. Results: The three in silico RCTs showed no statistically significant differences in PFS curves, which was similar to the results of three previous studies. Conclusions: The proposed mathematical models could be leveraged for in silico RCTs to optimize SBRT.

DOI： 10.1007/s12553-025-01005-2

Web of Science

Scopus

Language：Japanese   Publisher：(一社)日本医用画像工学会  

体幹部定位放射線治療(SBRT)を受けた早期非小細胞肺癌(NSCLC)患者では腫瘍が進行することがある.したがって,本研究では腫瘍の進行予測を目的とし,SBRTを受けた早期NSCLC患者の腫瘍成長モデルの開発を行った.SBRTを施行したNSCLC患者17名を選択し,治療前後のCT画像から輪郭描出した肉眼的腫瘍体積(GTV)をもとに腫瘍細胞数を算出し,それらを参照値とした.一方,放射線に対して3つの異なる反応を示す細胞集団(感受性細胞,persister細胞,抵抗性細胞)が存在すると仮定し,Gompertzモデルに基づいた腫瘍成長モデルを構築した.腫瘍成長モデルに含まれる9つのパラメータを焼きなまし法によって最適化し,腫瘍細胞数の経時変化曲線を推定した.SBRTを受けた早期NSCLC患者において,腫瘍細胞数の予測可能性を示したが,進行予測のためには腫瘍成長モデルの更なる改良が必要である.(著者抄録)

Language：English   Publisher：(一社)日本医用画像工学会  

我々は深層学習(DL)を用いて体幹部定位放射線治療(SBRT)の治療計画CT画像において肺腫瘍の領域抽出法を研究している。SBRTにおいて領域抽出性能は訓練データ数に依存するが、その性能をある程度維持できる訓練データ数は研究されていない。本研究では、訓練データ数対テストデータ数比(training-to-test number ratio:TTR)の腫瘍領域抽出性能に対する影響を調査した。本研究は、非小細胞肺癌患者192名[solid:118名、part-solid:53名、ground-glass opacity(GGO):21名]を対象とした。3D U-Net、V-Net、およびDense V-Netの3つのDLモデルを5パターンのTTR(1.00、0.791、0.531、0.291および0.116)でトレーニングした。3つのモデルは、Dice係数、precision、およびrecallに基づいて評価された。V-Netは、TTR=0.116のとき、最も高い0.788のDice係数を達成し、precisionは0.826、recallは0.794であった。訓練症例が10%、検証症例が5%に削減されても、ある程度領域抽出性能を維持できる可能性を示した。(著者抄録)

Language：English  

Language：English  

Language：Japanese  

Language：Japanese  

Language：English