Mag Intro

Mag Intro Mag Intro

Responsible Institution:
Fudan University
Sponsored by:
Fudan University Shanghai Cancer Center
Edited by:
Editorial Board of Oncoradiology
Editors-in-Chief:
CHANG Cai
PENG Weijun
FAN Wei
Editorial Director: NI Ming
Published by:
Editorial Office of Oncoradiology
Address:
270 Dong’an Road, Shanghai 200032, China
Tel: (021)64188274
Website: www.zhongliuyingxiangxue.com
E-mail：
zlyxx@zhongliuyingxiangxue.com
ISSN 2096-6210　
CN 31-2087/R

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Volume 35 期 2，2026 2026年第35卷第2期

  • Specialists' Commentary

    MA Qifan, TAO Xiaofeng

    DOI：10.19732/j.cnki.2096-6210.2026.02.001
    摘要：Magnetic resonance imaging (MRI) has become a crucial tool in modern medical diagnosis and research, owing to its non-invasive nature, absence of ionizing radiation, high soft-tissue resolution, and multi-parametric imaging capabilities. This review systematically summarized recent advances in MRI technology, including breakthroughs in low-field and ultra-low-field MRI in terms of cost, portability, and accessibility; improvements in hardware performance and image quality of high-field and ultra-high-field MRI systems; and the deepening applications of functional imaging techniques—such as diffusion imaging perfusion imaging, blood oxygenation level dependent functional MRI (BOLD-fMRI), magnetic resonance spectroscopy (MRS), and chemical exchange saturation transfer imaging (CEST)—in disease mechanism research and clinical practice. Furthermore, the integration of artificial intelligence (AI) with MRI is discussed, highlighting its transformative role in accelerating imaging, enhancing diagnostic accuracy, and enabling personalized medicine.  
    关键词：Magnetic resonance imaging;Functional magnetic resonance;Imaging diagnosis;Low-field and high-field magnetic resonance;Artificial intelligence   
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    CHEN Hebing, XUE Huadan

    DOI：10.19732/j.cnki.2096-6210.2026.02.002
    摘要：Pancreatic cancer is characterized by high heterogeneity and a fibrotic microenvironment, which lead to a lag in the assessment efficacy of conventional imaging modalities. Currently, quantitative imaging markers, by extracting information on tumor function, metabolism, and microstructural characteristics, are gradually becoming a key breakthrough for early identification of treatment response and the realization of individualized treatment decisions. Therefore, this article innovatively reviewed the quantitative parameters of different imaging techniques, as well as the application value of radiomics and deep learning in the evaluation of treatment efficacy and prognosis prediction of pancreatic cancer. This review aimed to provide references for future research improvements, promote the translational application of various quantitative imaging techniques in precision diagnosis and treatment of pancreatic cancer, and ultimately improve patients' quality of life and prolong their survival.  
    关键词：Pancreatic cancer;Quantitative imaging marker;Treatment response evaluation;Prognosis prediction   
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  • Specialists' Article

    LIU Zhuang, LIU Dingxia, TANG Wei, PENG Weijun, GU Yajia, TONG Tong

    DOI：10.19732/j.cnki.2096-6210.2026.02.003
    摘要：ObjectiveTo investigate the prognostic value of a dual-marker stratification model combining response evaluation criteria in solid tumor (RECIST) v1.1 and serum carbohydrate antigen 19-9 (CA19-9) in patients with advanced pancreatic cancer receiving first-line chemotherapy.MethodsPatients with advanced pancreatic cancer who received first-line chemotherapy at Fudan University, Shanghai Cancer Center from April 2020 to July 2022 were retrospectively enrolled. Chemotherapy regimens included AG, AG combined with programmed death ligand-1 (PD-L1) inhibitor, and mFOLFIRINOX. Based on the radiological (RECIST v1.1) and biochemical (CA19-9 reduction ≥ 50%) responses after two cycles of chemotherapy, patients were classified into three groups: dual-marker responders (radiological non-progressive disease and biochemical response), single-marker responders (either radiological or biochemical response), and dual-marker non-responders (radiological progressive disease and non-biochemical response). Overall survival (OS) was compared among groups using Kaplan-Meier method and log-rank test, and subgroup analyses were performed.ResultsAmong the 69 patients included in the study, 43 patients (62.32%) were classified into the dual-marker response group, 19 (27.54%) into the single-marker response group, and 7 (10.14%) into the dual-marker non-response group. The median OS showed a significant gradient among the three groups: the dual-marker response group had a median OS of 404 days, which was significantly longer than that of the other two groups (P=0.01 and P=0.04, respectively). Subgroup analysis revealed that RECIST v1.1 had better predictive performance in the mFOLFIRINOX subgroup (P=0.01), while CA19-9 showed superior predictive value in the AG subgroup (P=0.01). However, neither single nor combined indicators effectively predicted prognosis in the AG combined with PD-L1 inhibitor subgroup.ConclusionThe dual-marker stratification model combining RECIST v1.1 and CA19-9 effectively distinguishes the prognosis of patients with advanced pancreatic cancer receiving chemotherapy, providing incremental value over single indicators. This model is simple and practical, offering reference for individualized treatment decisions.  
    关键词：Pancreatic ductal adenocarcinoma;Response evaluation criteria in solid tumor;Carbohydrate antigen 19-9;Prognostic prediction;Chemotherapy   
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    JIAN Meicheng, CHEN Chengwei, SHAO Chengwei, BIAN Yun

    DOI：10.19732/j.cnki.2096-6210.2026.02.004
    摘要：ObjectiveTo develop a cross-scale risk assessment framework for pancreatic adenosquamous carcinoma (PASC). At the postoperative pathology level, a squamous phenotype pathology index (SPPI) was constructed from whole slide images (WSIs) and its prognostic stratification value was evaluated. At the preoperative imaging level, the ability of contrast-enhanced computed tomography (CT) to noninvasively predict SPPI-defined high versus low risk was investigated.MethodsA total of 158 patients with surgically resected, pathologically confirmed PASC were retrospectively enrolled from The First Affiliated Hospital of Naval Medical University between June 2014 and June 2024. The cohort was split chronologically into a training set (June 2014 to June 2021, n=102) and a validation set (July 2021 to June 2024, n=56). In the digital pathology stage, 100 representative WSIs were randomly sampled from the training set; pathologists annotated squamous and adenocarcinoma regions pixel by pixel with immunohistochemical guidance, yielding approximately 10 000 patches (256×256 pixels) that were used to train a DeepLab-v3+ segmentation model. Five patient-level histological phenotype categories—component proportion (C), dispersion/fragmentation (D), boundary complexity (B), spatial interface relationship (S), and tumor burden (V)—were extracted from the segmentation output, standardized, and fed into a Ridge-penalized Cox model to produce the continuous risk index SPPI; the training-set median served as the stratification threshold. In the preoperative imaging stage, 129 of the 158 patients who had undergone contrast-enhanced CT before surgery (training 85, validation 44) formed an imaging subcohort. Portal-phase images were segmented with nnMamba and registered to the remaining phases; 213 candidate radiomic features were extracted from intratumoral and peritumoral regions of interest, subjected to least absolute shrinkage and selection operator (LASSO) selection, and combined with maximum tumor diameter in a logistic model targeting the frozen SPPI risk labels.ResultsEach one-standard-deviation increase in SPPI was associated with a 60.9% rise in the hazard of death in the training set (HR 1.609, 95% CI 1.294–2.002) and a 90.3% rise in the validation set (HR 1.903, 95% CI 1.381–2.622), with corresponding C-indexes of 0.632 and 0.709. After threshold-based stratification, median overall survival (OS) in the high-risk group was below 10 months in both cohorts (9.53 and 8.68 months), whereas the low-risk group reached 18.77 and 34.21 months, respectively. On the imaging side, LASSO retained five features, and the combined model achieved area under curves (AUCs) of 0.831 and 0.865 in the training and validation sets. The model-predicted risk groups likewise showed significant survival separation (both P<0.01) and remained independently prognostic after multivariable Cox adjustment.ConclusionBy integrating multidimensional spatial information on the squamous component, SPPI outperformed the conventional single-proportion metric for prognostic stratification of PASC. The contrast-enhanced CT-based combined model provided reasonably reliable preoperative discrimination of SPPI risk categories, offering preliminary support for a two-stage "imaging-pathology-prognosis" risk assessment paradigm.  
    关键词：Pancreatic adenosquamous carcinoma;Digital pathology;Whole slide image;Computed tomography;Radiomics;Prognosis   
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