Ying Wu, Qun Yang, Jinguo Wang, Yunyun Xu, Tingting LiDepartment of Cardiothoracic Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tingting Li, Department of Cardiothoracic Surgery, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China, Email Ltt3389@163.comAim: To identify latent classes of symptom burden in lung cancer patients before and after pulmonary rehabilitation (PR) and to examine factors associated with transitions between classes.Design: A longitudinal study.Methods: The M. D. Anderson Symptom Inventory (Chinese version) was completed by 273 postoperative lung cancer patients before (T1) and 12 weeks after PR (T2). Latent profile analysis identified symptom burden classes, and latent transition analysis estimated the probability of moving between classes. Measurement invariance was tested, and the two-class solution was retained on the basis of fit indices, entropy and interpretability. Univariate-significant variables entered a logistic regression model.Results: Two latent classes were identified at both T1 and T2 (high and low symptom burden). The two-class solution was retained because it showed the lowest AIC, BIC and aBIC, adequate entropy (0.844 at T1; 0.902 at T2) and interpretable profiles, whereas the three-class model did not improve fit and yielded small classes. Among 262 patients (95.97% valid), the low burden class was stable (0.863), and 61.07% of patients in the high burden class at T1 moved to the low burden class at T2; these probabilities denote the proportion of patients moving between classes. Age < 45 years (OR = 2.345), no financial burden (OR = 2.841), satisfactory sleep (OR = 3.130) and family living (OR = 2.949) were associated with transition from the high to the low burden class, whereas age ≥ 60 years (OR = 0.724), unmarried (OR = 0.418) and divorced (OR = 0.405) status with lower odds of transition.Conclusion: Symptom burden in postoperative lung cancer patients is heterogeneous and follows class-specific trajectories. Younger age, better sleep and stronger family and financial resources were associated with a favourable transition. As this was a single-arm observational study, the findings indicate associations rather than causal effects of PR.Keywords: lung cancer, postoperative, symptom burden, latent profile analysis, latent transition analysis, influencing factors
Guo J, Guo M, Zheng J. Cancer Manag Res. 2021;13:773–785. https://doi.org/10.2147/CMAR.S281969 We, the Editors and Publisher of the journal Cancer Management and Research, have retracted the published article. After publication, concerns have been raised by a third party in 2026 about the integrity of the data in Figures 3 and 5 of the article. Subsequently, the authors contacted the Publisher to request a correction to their article. Further investigation conducted by the Journal and Publisher confirmed unusual similarities present in the image panels representative of different samples and treatment conditions in Figures 3D and 5C. Upon further review, the Journal and Publisher identified additional methodological concerns. When approached for an explanation, the authors have responded but have not been able to address the concerns raised and have not provided their original data. As the editorial team no longer have confidence in the reported conclusions, we are retracting the article. The corresponding author listed in this publication has been notified. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as ‘Retracted’.
Arnabjyoti Deva Sarma,1 Debanga Sarma,2 Thomas J Webster3– 61Programme of Radiology, Faculty of Allied and Healthcare Sciences, Assam Down Town University, Guwahati, Assam, India; 2Department of Urology and Renal Transplant, Gauhati Medical College and Hospital, Guwahati, Assam, India; 3Division of Pre-college and Undergraduate Studies, Brown University, Providence, RI, USA; 4School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin, 300131, People’s Republic of China; 5School of Engineering, Saveetha University, Chennai, India; 6Department of Pharmacy, The University of the Basque Country, Vitoria-Gasteiz, SpainCorrespondence: Thomas J Webster, Division of Pre-college and Undergraduate Studies, Brown University, Providence, RI, USA, Email thomas_webster@brown.edu Arnabjyoti Deva Sarma, Programme of Radiology, Faculty of Allied and Healthcare Sciences, Assam Down Town University, Sankar Madhab Path, Gandhi Nagar, Panikhaiti, Guwahati, Assam, India, Email sarma.arnab1990@gmail.comAbstract: Hepatocellular carcinoma (HCC) management has evolved rapidly with the expanding use of locoregional therapies, systemic agents, and modern immunotherapy combinations. These advances have introduced complex and variable post-treatment imaging appearances, underscoring the need for refined, standardized criteria to accurately differentiate viable tumors from treatment-related changes. The updated LI-RADS Treatment Response Algorithm (v2024– 2025) incorporates new guidance on arterial enhancement, washout, ancillary features, and modality-specific considerations to improve consistency and diagnostic clarity. This review synthesizes these updates and their implications for clinical practice. A comprehensive narrative review was conducted using updated LI-RADS guidelines and recent literature evaluating CT, MRI, CEUS, dual-energy CT, and photon-counting CT for post-treatment HCC assessment. Therapy-specific imaging appearances, algorithmic refinements, pitfalls, radiologic–pathologic correlations, and applications of artificial intelligence were analyzed. The updated LI-RADS TRA has shown potential lesion-level assessment by clarifying criteria for viable, non-viable, and equivocal responses. Multiparametric MRI and spectral CT improve the detection of subtle viable tissue, while advanced imaging helps address confounding factors such as granulation tissue, perfusional changes, and immune-related pseudo-progression. The algorithm demonstrates high accuracy after ablation and TACE, with ongoing refinements needed for radiotherapy and immunotherapy. Radiomics and machine learning models show strong potential for automated viability assessment and early prediction of recurrence. The LI-RADS TRA (v2024– 2025) strengthens standardization of post-treatment HCC imaging and supports more accurate, reproducible interpretation across modalities. As treatment options expand, integrating quantitative imaging biomarkers and AI-driven tools will be essential for personalized treatment planning and improved clinical outcomes.Keywords: hepatocellular carcinoma, LI-RADS treatment response, locoregional therapy, post-treatment imaging, radiomics and artificial intelligence
Yan Li,1,* Junfei Lu,1,* Xinyi Tan,2 Jun Cai,1 Yiyuan Wang,1 Zhichao Wang,1 Qingqing Ye31Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, People’s Republic of China; 2Department of Obstetrics and Gynecology, The First Affiliated Hospital of Yangtze University, Jingzhou, People’s Republic of China; 3Department of Breast Surgery, The First Affiliated Hospital of Yangtze University, Jingzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qingqing Ye, Department of Breast Surgery, The First Affiliated Hospital of Yangtze University, No. 4, Aviation Road, Shashi District, Jingzhou, 434000, People’s Republic of China, Email zhiyin0540@163.com Zhichao Wang, Department of Oncology, The First Affiliated Hospital of Yangtze University, No. 4, Aviation Road, Shashi District, Jingzhou, 434000, People’s Republic of China, Email wzc_hrbeu@163.comAbstract: Lung cancer is a dynamically evolving disease characterized by marked temporal heterogeneity across tumor progression, treatment response, resistance development, and clinical outcomes. Nevertheless, the majority of existing artificial intelligence (AI) systems continue to rely predominantly on single time-point imaging, imposing inherent constraints on their capacity to characterize disease evolution and capture continuously shifting risk states. The growing availability of longitudinal imaging data, combined with ongoing advances in temporal deep learning, is driving a transition in lung cancer AI from static image interpretation toward dynamic risk prediction. In this narrative review, relevant literature was identified through searches of PubMed/MEDLINE, Web of Science Core Collection, and Scopus from database inception to September 2026, using terms related to lung cancer, longitudinal imaging, temporal deep learning, and dynamic risk prediction, with representative studies selected according to their methodological relevance, clinical applications, and implications for clinical translation. Longitudinal imaging tracks disease trajectories over time and offers distinct opportunities to characterize tumor growth dynamics, treatment-induced changes, and evolving recurrence risk. Temporal deep learning, in turn, enables the modelling of complex time-dependent relationships that conventional static approaches are poorly suited to represent. The field has progressed from early dual time-point analyses to long-range temporal modelling, multimodal temporal fusion, and continuous risk updating, with encouraging applications spanning pulmonary nodule management, treatment response assessment, recurrence surveillance, survival prediction, and individualized disease management. Nonetheless, substantial obstacles persist, including the scarcity of large-scale longitudinal datasets, irregular temporal sampling, limited model generalizability, insufficient causal interpretability, and a lack of robust evidence supporting real-world clinical deployment. Addressing these challenges will require coordinated efforts toward standardized longitudinal data infrastructure, continuous-time modelling frameworks, multimodal integration strategies, and closer alignment of AI systems with clinical workflows. Taken together, longitudinal imaging and temporal deep learning are steering lung cancer AI away from static image analysis and toward dynamic disease management, with the potential to support continuous risk assessment and potentially inform adaptive clinical decision-making throughout the full spectrum of lung cancer care.Keywords: lung cancer, longitudinal imaging, temporal deep learning, dynamic risk prediction, artificial intelligence, clinical decision support system
Behnam Omidi Sarajar,1 Aram Khederzadeh,1 Asghar Payoon,2 Mohammad Hadi Zarei,3 Emad Jafarzadeh21Department of Toxicology and Pharmacology, Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran; 2Health Products Safety Research Center, Qazvin University of Medical Sciences, Qazvin, Iran; 3Medical Plants Research Center, Basic Health Sciences Institute, Shahrekord University of Medical Sciences, Shahrekord, IranCorrespondence: Mohammad Hadi Zarei, Email zarei.mh@skums.ac.ir Emad Jafarzadeh, Email jafarzadeh-e@razi.tums.ac.irAbstract: Paclitaxel is a standard chemotherapeutic agent for various malignancies, but drug resistance frequently limits its clinical efficacy. Metformin, a widely used antidiabetic drug, has demonstrated anticancer properties. This systematic review evaluates the molecular mechanisms and signaling pathways underlying the synergistic interaction between metformin and paclitaxel in preclinical cancer models. A systematic search of Embase, Scopus, PubMed, Web of Science, and Google Scholar was conducted up to February 2026 following PRISMA guidelines. English-language preclinical studies examining the combined use of metformin and paclitaxel in cancer cell lines and animal models were included. Thirty-six studies met the eligibility criteria, including 28 in vitro and 8 in vivo studies. The majority demonstrated synergistic effects across endometrial, melanoma, triple-negative breast, prostate, and ovarian cancer models. Metformin enhances paclitaxel activity through several defined mechanisms. These include the activation of AMPK with subsequent suppression of the PI3K/AKT/mTOR pathway, epigenetic silencing of the oncogenic lncRNA SNHG7, induction of reactive oxygen species and mitochondrial apoptosis, and inhibition of cancer stemness via FOXO3a regulation. Additionally, metformin increases intracellular paclitaxel accumulation through OCT3 transporters. In vivo studies confirmed that metformin reduces tumor volume when combined with paclitaxel, utilizing both AMPK-dependent and AMPK-independent mechanisms such as cyclin D1 downregulation and gut microbiota modulation. Preclinical evidence indicates that metformin acts as a chemosensitizer, potentiating paclitaxel efficacy through multiple molecular pathways. This combination allows for reduced effective paclitaxel doses and may help overcome drug resistance, particularly in endometrial cancer, melanoma, and triple-negative breast cancer. Future clinical studies should optimize dosing regimens and validate predictive biomarkers, such as OCT1 and OCT3 expression, to improve clinical translation.Keywords: metformin, paclitaxel, signaling pathway, drug synergism, AMPK, chemosensitization
