Pub Date : 2026-01-28DOI: 10.1186/s12885-026-15599-x
Li Wang, Yang Li, Ting Qiu, Junyi Liu, Jiawei Zhou, Han Gao, Hongliang Yu, Yinsu Zhu, Baozhou Sun, Guanyu Yang, Shengfu Huang, Lirong Wu, Li Sun, Xia He
Background: To investigate the potential of apparent diffusion coefficient (ADC) map-based deep learning and dose distribution-based dosiomics in predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC).
Methods: This retrospective study included 3578 NPC patients from Jiangsu Cancer Hospital receiving intensity-modulated radiation therapy (IMRT). Ninety-four RTLI patients were recruited based on inclusion criteria and matched 1:1 with 97 control subjects using propensity scores. Patients were randomly assigned to the training cohort (n = 135) and the validation cohort (n = 59). Deep transfer learning (DTL) features and dosiomics features were extracted from ADC map and three-dimensional dose distribution, respectively. Pearson's correlation coefficient and the least absolute shrinkage and selection operator (LASSO) regression were employed to identify predictive features. Subsequently, eight machine learning classification models were trained to establish a prediction framework, encompassing Support Vector Machine, K-Nearest Neighbor, Random Forest, Extremely Randomized Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, Adaptive Boosting and Multilayer Perceptron. The performance of clinical, DTL, dosiomics and feature fusion model was compared by the area under the curve (AUC).
Results: We constructed six pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that pre-trained WideResNet 101 exhibited superior performance with an AUC of 0.786 in the validation cohort. The clinical model based on D1cc and induction chemotherapy demonstrated an AUC of 0.794 and the dosiomics model demonstrated an AUC of 0.903. Features fusion model demonstrated the highest AUC values in both the training (0.988) and validation (0.940) cohorts.
Conclusions: The fusion model based on pretreatment ADC map and dose distribution provided a promising way to predict RTLI in NPC patients receiving IMRT, which can support clinicians in making decisions to develop individualized treatment plans and implement preventive measures.
{"title":"Improving prediction accuracy of radiation-induced temporal lobe injury in nasopharyngeal carcinoma using ADC-based deep learning and dosiomics.","authors":"Li Wang, Yang Li, Ting Qiu, Junyi Liu, Jiawei Zhou, Han Gao, Hongliang Yu, Yinsu Zhu, Baozhou Sun, Guanyu Yang, Shengfu Huang, Lirong Wu, Li Sun, Xia He","doi":"10.1186/s12885-026-15599-x","DOIUrl":"https://doi.org/10.1186/s12885-026-15599-x","url":null,"abstract":"<p><strong>Background: </strong>To investigate the potential of apparent diffusion coefficient (ADC) map-based deep learning and dose distribution-based dosiomics in predicting radiation-induced temporal lobe injury (RTLI) in nasopharyngeal carcinoma (NPC).</p><p><strong>Methods: </strong>This retrospective study included 3578 NPC patients from Jiangsu Cancer Hospital receiving intensity-modulated radiation therapy (IMRT). Ninety-four RTLI patients were recruited based on inclusion criteria and matched 1:1 with 97 control subjects using propensity scores. Patients were randomly assigned to the training cohort (n = 135) and the validation cohort (n = 59). Deep transfer learning (DTL) features and dosiomics features were extracted from ADC map and three-dimensional dose distribution, respectively. Pearson's correlation coefficient and the least absolute shrinkage and selection operator (LASSO) regression were employed to identify predictive features. Subsequently, eight machine learning classification models were trained to establish a prediction framework, encompassing Support Vector Machine, K-Nearest Neighbor, Random Forest, Extremely Randomized Trees, eXtreme Gradient Boosting, Light Gradient Boosting Machine, Adaptive Boosting and Multilayer Perceptron. The performance of clinical, DTL, dosiomics and feature fusion model was compared by the area under the curve (AUC).</p><p><strong>Results: </strong>We constructed six pre-trained transfer learning networks and extracted DTL features, respectively. The results showed that pre-trained WideResNet 101 exhibited superior performance with an AUC of 0.786 in the validation cohort. The clinical model based on D<sub>1cc</sub> and induction chemotherapy demonstrated an AUC of 0.794 and the dosiomics model demonstrated an AUC of 0.903. Features fusion model demonstrated the highest AUC values in both the training (0.988) and validation (0.940) cohorts.</p><p><strong>Conclusions: </strong>The fusion model based on pretreatment ADC map and dose distribution provided a promising way to predict RTLI in NPC patients receiving IMRT, which can support clinicians in making decisions to develop individualized treatment plans and implement preventive measures.</p>","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146131272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1186/s12885-026-15559-5
Ying Zhang, Haifang Zhu, Fujuan Yang, Lei Yang
{"title":"Construction and validation of a prediction model for postoperative complications of elderly patients with locally advanced esophageal squamous cell carcinoma based on POSSUM system and inflammatory factors.","authors":"Ying Zhang, Haifang Zhu, Fujuan Yang, Lei Yang","doi":"10.1186/s12885-026-15559-5","DOIUrl":"https://doi.org/10.1186/s12885-026-15559-5","url":null,"abstract":"","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146060132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1186/s12885-026-15600-7
Noah-David Hirsch, Markus Perl, Simon Holzinger, Christoph Barz, Stefan Enßle, Widya Johannes, Anja Conrad, Jonas J Unterholzner, Viktoria Obermeier, Markus Tschurtschenthaler, Ludger Johannes, Klaus-Peter Janssen
{"title":"The Gb3-synthase A4GALT is an epigenetically regulated driver of tumor invasiveness in gastrointestinal cancer.","authors":"Noah-David Hirsch, Markus Perl, Simon Holzinger, Christoph Barz, Stefan Enßle, Widya Johannes, Anja Conrad, Jonas J Unterholzner, Viktoria Obermeier, Markus Tschurtschenthaler, Ludger Johannes, Klaus-Peter Janssen","doi":"10.1186/s12885-026-15600-7","DOIUrl":"https://doi.org/10.1186/s12885-026-15600-7","url":null,"abstract":"","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146060058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-27DOI: 10.1186/s12885-025-15515-9
Yasaman Moghaddasi, Abbas Dehghanian, Armaghan Vafafar, Mirza Ali Mofazzal Jahromi, Vahid Rahmanian
{"title":"Associations between whole blood donation and cancer incidence: a systematic review.","authors":"Yasaman Moghaddasi, Abbas Dehghanian, Armaghan Vafafar, Mirza Ali Mofazzal Jahromi, Vahid Rahmanian","doi":"10.1186/s12885-025-15515-9","DOIUrl":"https://doi.org/10.1186/s12885-025-15515-9","url":null,"abstract":"","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1186/s12885-026-15624-z
Olaf Schoffer, Max Kemper, Michael Gerken, Veronika Bierbaum, Christoph Bobeth, Martin Rößler, Patrik Dröge, Thomas Ruhnke, Christian Günster, Kees Kleihues-van Tol, Chia-Jung Busch, Monika Klinkhammer-Schalke, Jochen Schmitt
{"title":"The effects of certification of head and neck cancer centers on the survival of patients with a head and neck cancer.","authors":"Olaf Schoffer, Max Kemper, Michael Gerken, Veronika Bierbaum, Christoph Bobeth, Martin Rößler, Patrik Dröge, Thomas Ruhnke, Christian Günster, Kees Kleihues-van Tol, Chia-Jung Busch, Monika Klinkhammer-Schalke, Jochen Schmitt","doi":"10.1186/s12885-026-15624-z","DOIUrl":"10.1186/s12885-026-15624-z","url":null,"abstract":"","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":" ","pages":"132"},"PeriodicalIF":3.4,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12837409/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Paclitaxel has been a cornerstone of ovarian cancer chemotherapy for over two decades. However, its clinical application is constrained by poor solubility and non-specific delivery, resulting in systemic toxicity and inconsistent therapeutic outcomes. Nanotechnology-based drug delivery systems have emerged as a promising strategy to address these limitations. In this study, we employed elastin-like polypeptide (ELP) nanocarriers, precisely modified with the tumor-targeting AP1 peptide, to deliver paclitaxel in ovarian cancer. ELPs are biologically inspired, genetically engineered polymers that can form nano-sized structures with controlled physicochemical properties, facilitating passive tumor targeting. The integration of the AP1 peptide, which specifically binds to the IL-4 receptor overexpressed in numerous cancers, enables active targeting of these nanocarriers, complementing the passive delivery approach. This investigation focused on the synthesis and characterization of paclitaxel delivery vehicles based on modified (A60) and unmodified (E60) ELPs. Paclitaxel (PTX) was conjugated to ELPs via a thiol-maleimide Michael-addition strategy. Both ELP-PTX formulations formed stable, monodisperse micelles, with A60-PTX nanoparticles measuring 28 ± 2.8 nm and E60-PTX nanoparticles measuring 46.8 ± 6.6 nm, as determined by TEM. DLS analysis further confirmed the narrow size distribution, evidenced by a single, narrow peak in the size distribution profile, indicating near homogeneity of the micellar population. In vitro binding analysis in SKOV-3 and OVCAR-3 ovarian cancer cells demonstrated significantly enhanced targeting capability with A60, exhibiting ~ 8.6-fold and ~ 2.7-fold higher cell binding than E60, respectively. Consistently, A60-PTX demonstrated superior cytotoxicity, with ~ 2.6-fold and ~ 1.4-fold lower IC50 values than E60-PTX in SKOV-3 (47 nM vs. 120 nM) and OVCAR-3 (45 nM vs. 62 nM), respectively. The relevance of the active targeting was further validated in agarose-based 3D spheroid models of the two cell lines with A60-PTX demonstrating approximately ~ 3-fold (SKOV-3) and ~ 2.5-fold (OVCAR-3) higher cytotoxicity compared to E60-PTX. Overall, this study highlights the potential of AP1-functionalized ELP nanocarriers to enhance the precision and therapeutic efficacy of paclitaxel delivery, offering a promising strategy for targeted ovarian cancer therapy.
二十多年来,紫杉醇一直是卵巢癌化疗的基石。然而,其临床应用受到溶解度差和非特异性递送的限制,导致全身毒性和治疗结果不一致。基于纳米技术的药物输送系统已经成为解决这些限制的一种有希望的策略。在这项研究中,我们使用弹性蛋白样多肽(ELP)纳米载体,精确修饰肿瘤靶向AP1肽,在卵巢癌中递送紫杉醇。elp是一种受生物学启发的基因工程聚合物,可以形成具有可控物理化学性质的纳米级结构,促进被动靶向肿瘤。在许多癌症中特异性结合IL-4受体的AP1肽的整合,使这些纳米载体能够主动靶向,补充了被动递送方法。本文主要研究了改性(A60)和未改性(E60) elp载体紫杉醇的合成与表征。紫杉醇(PTX)通过巯基-马来酰亚胺迈克尔加成策略与elp偶联。两种ELP-PTX配方均形成稳定的单分散胶束,通过透射电镜测定,A60-PTX纳米颗粒的粒径为28±2.8 nm, E60-PTX纳米颗粒的粒径为46.8±6.6 nm。DLS分析进一步证实了粒径分布的窄性,在粒径分布曲线上有一个单一的窄峰,表明胶束群体的均匀性。体外结合分析表明,A60对SKOV-3和OVCAR-3卵巢癌细胞的靶向能力显著增强,分别比E60的细胞结合能力高约8.6倍和约2.7倍。与此同时,A60-PTX在SKOV-3 (47 nM vs. 120 nM)和OVCAR-3 (45 nM vs. 62 nM)中的IC50值分别比E60-PTX低2.6倍和1.4倍,表现出更强的细胞毒性。在琼脂糖为基础的两种细胞系的三维球体模型中进一步验证了活性靶向的相关性,A60-PTX显示出比E60-PTX高约3倍(SKOV-3)和2.5倍(OVCAR-3)的细胞毒性。总之,本研究强调了ap1功能化ELP纳米载体在提高紫杉醇给药精度和治疗效果方面的潜力,为卵巢癌靶向治疗提供了一种有前景的策略。
{"title":"Targeted paclitaxel delivery in ovarian cancer via AP1-functionalized elastin-like polypeptide nanocarriers: development and characterization.","authors":"Ridhima Goel, Shakeel Alvi, Rashid Ali, Pradeep Sharma, Jayanta Bhattacharyya, Vijaya Sarangthem, Thoudam Debraj Singh","doi":"10.1186/s12885-026-15615-0","DOIUrl":"https://doi.org/10.1186/s12885-026-15615-0","url":null,"abstract":"<p><p>Paclitaxel has been a cornerstone of ovarian cancer chemotherapy for over two decades. However, its clinical application is constrained by poor solubility and non-specific delivery, resulting in systemic toxicity and inconsistent therapeutic outcomes. Nanotechnology-based drug delivery systems have emerged as a promising strategy to address these limitations. In this study, we employed elastin-like polypeptide (ELP) nanocarriers, precisely modified with the tumor-targeting AP1 peptide, to deliver paclitaxel in ovarian cancer. ELPs are biologically inspired, genetically engineered polymers that can form nano-sized structures with controlled physicochemical properties, facilitating passive tumor targeting. The integration of the AP1 peptide, which specifically binds to the IL-4 receptor overexpressed in numerous cancers, enables active targeting of these nanocarriers, complementing the passive delivery approach. This investigation focused on the synthesis and characterization of paclitaxel delivery vehicles based on modified (A60) and unmodified (E60) ELPs. Paclitaxel (PTX) was conjugated to ELPs via a thiol-maleimide Michael-addition strategy. Both ELP-PTX formulations formed stable, monodisperse micelles, with A60-PTX nanoparticles measuring 28 ± 2.8 nm and E60-PTX nanoparticles measuring 46.8 ± 6.6 nm, as determined by TEM. DLS analysis further confirmed the narrow size distribution, evidenced by a single, narrow peak in the size distribution profile, indicating near homogeneity of the micellar population. In vitro binding analysis in SKOV-3 and OVCAR-3 ovarian cancer cells demonstrated significantly enhanced targeting capability with A60, exhibiting ~ 8.6-fold and ~ 2.7-fold higher cell binding than E60, respectively. Consistently, A60-PTX demonstrated superior cytotoxicity, with ~ 2.6-fold and ~ 1.4-fold lower IC50 values than E60-PTX in SKOV-3 (47 nM vs. 120 nM) and OVCAR-3 (45 nM vs. 62 nM), respectively. The relevance of the active targeting was further validated in agarose-based 3D spheroid models of the two cell lines with A60-PTX demonstrating approximately ~ 3-fold (SKOV-3) and ~ 2.5-fold (OVCAR-3) higher cytotoxicity compared to E60-PTX. Overall, this study highlights the potential of AP1-functionalized ELP nanocarriers to enhance the precision and therapeutic efficacy of paclitaxel delivery, offering a promising strategy for targeted ovarian cancer therapy.</p>","PeriodicalId":9131,"journal":{"name":"BMC Cancer","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146050407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}