Cancer Research and Treatment

Original Articles

Lung and Thoracic cancer

Clinical Relevance of Starting Alectinib at a Reduced Dose in Patients with ALK-Positive Non–Small Cell Lung Cancer: Junkyu Kim, Min-Ji Kim, Jinyong Kim, Sehhoon Park, Hyun Ae Jung, Se-Hoon Lee, Jin Seok Ahn, Myung-Ju Ahn, Jong-Mu Sun; Cancer Res Treat. 2026;58(2):434-442. Published online April 24, 2025; DOI: https://doi.org/10.4143/crt.2024.1209

Abstract PDF Supplementary Material PubReader ePub: Purpose
Alectinib has been approved for anaplastic lymphoma kinase (ALK)–positive non–small cell lung cancer (NSCLC) at 300 mg twice daily in Japan, lower than global standard of 600 mg twice daily. This study evaluated the clinical relevance of the reduced dose by comparing outcomes between the two doses.
Materials and Methods
This study included patients with advanced ALK-positive NSCLC who received alectinib at Samsung Medical Center, Korea. The progression-free survival (PFS), overall survival, cumulative incidence of central nervous system (CNS) progression, and safety profiles were retrospectively reviewed and compared.
Results
Among 306 patients, 32 and 274 received alectinib at either 300 or 600 mg twice daily, respectively. The 300 mg group showed a slight but not significant advantage in PFS (hazard ratio [HR], 0.82; 95% confidence interval [CI], 0.44 to 1.51; p=0.51) and overall survival (HR, 0.51; 95% CI, 0.20 to 1.21; p=0.13). Superior outcome with 300 mg was remarkable in patients with lower body weight (≤ 60 kg), but diminished in patients with higher body weights. Patients with baseline brain metastasis in the 300 mg group exhibited a slight increase in incidence of CNS failure (HR, 1.76; 95% CI, 0.53 to 5.8; p=0.36). Although the safety profiles were mostly mild, adverse events were more frequent in the 600 mg group, 50% of which requiring dose reduction.
Conclusion
Alectinib at 300 mg twice daily seems an acceptable dose in East Asians with ALK-positive NSCLC. Notably, our data favor 300 mg twice daily in patients with lower body weight and no baseline brain metastasis, considering the more tolerable safety profiles and the potential to reduce medical costs.

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Enhancing Identification of High-Risk cN0 Lung Adenocarcinoma Patients Using MRI-Based Radiomic Features: Harim Kim, Jonghoon Kim, Soohyun Hwang, You Jin Oh, Joong Hyun Ahn, Min-Ji Kim, Tae Hee Hong, Sung Goo Park, Joon Young Choi, Hong Kwan Kim, Jhingook Kim, Sumin Shin, Ho Yun Lee; Cancer Res Treat. 2025;57(1):57-69. Published online June 26, 2024; DOI: https://doi.org/10.4143/crt.2024.251

Abstract PDF Supplementary Material PubReader ePub

Purpose
This study aimed to develop a magnetic resonance imaging (MRI)–based radiomics model to predict high-risk pathologic features for lung adenocarcinoma: micropapillary and solid pattern (MPsol), spread through air space, and poorly differentiated patterns.
Materials and Methods
As a prospective study, we screened clinical N0 lung cancer patients who were surgical candidates and had undergone both ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography–computed tomography (PET/CT) and chest CT from August 2018 to January 2020. We recruited patients meeting our proposed imaging criteria indicating high-risk, that is, poorer prognosis of lung adenocarcinoma, using CT and FDG PET/CT. If possible, these patients underwent an MRI examination from which we extracted 77 radiomics features from T1-contrast-enhanced and T2-weighted images. Additionally, patient demographics, maximum standardized uptake value on FDG PET/CT, and the mean apparent diffusion coefficient value on diffusion-weighted image, were considered together to build prediction models for high-risk pathologic features.
Results
Among 616 patients, 72 patients met the imaging criteria for high-risk lung cancer and underwent lung MRI. The magnetic resonance (MR)–eligible group showed a higher prevalence of nodal upstaging (29.2% vs. 4.2%, p < 0.001), vascular invasion (6.5% vs. 2.1%, p=0.011), high-grade pathologic features (p < 0.001), worse 4-year disease-free survival (p < 0.001) compared with non-MR-eligible group. The prediction power for MR-based radiomics model predicting high-risk pathologic features was good, with mean area under the receiver operating curve (AUC) value measuring 0.751-0.886 in test sets. Adding clinical variables increased the predictive performance for MPsol and the poorly differentiated pattern using the 2021 grading system (AUC, 0.860 and 0.907, respectively).
Conclusion
Our imaging criteria can effectively screen high-risk lung cancer patients and predict high-risk pathologic features by our MR-based prediction model using radiomics.

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Advances in Radiological Imaging for Thoracic Oncology: A 20-Year Perspective
Jeremy J. Erasmus, Ioannis Vlahos
Journal of Thoracic Oncology.2026; 21(5): 103563. CrossRef
Equilibrium Contrastive Learning for Imbalanced Image Classification
Sumin Roh, Harim Kim, Ho Yun Lee, Il Yong Chun
IEEE Transactions on Emerging Topics in Computational Intelligence.2026; 10(3): 2429. CrossRef
Integrating CT Radiomics and Machine Learning for Preoperative Assessment of STAS Grading and Prognostic Stratification in Lung Adenocarcinoma
Qiuxing Chen, Siqi Qu, Fang Chen, Shulin Li, Zhihe Yang, Qijia Han, Zhu Ai, Peng Wang, Kun Ma, Zhiming Xiang
Academic Radiology.2026;[Epub] CrossRef

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