Rationale and Objectives Early and accurate staging of rectal cancer is essential for selecting optimal treatment strategies. This study aimed to evaluate the utility of a combined clinical, tumoral, and peritumoral radiomics model for predicting T1 and T2 rectal cancer staging. Materials and Methods We retrospectively enrolled patients with pathologically confirmed rectal cancer from three medical centers between August 2018 and December 2024. Radiomics features were extracted from both tumoral and peritumoral regions using preoperative magnetic resonance imaging scans. The radiomics model with the highest area under the curve (AUC) was combined with a clinical model to construct a fusion model for distinguishing T1 and T2 stages. Results A total of 392 patients were included and allocated to a training set (n = 208), an internal test set (n = 90), and an external test set (n = 94). The fusion model (clinical+Com-T2WI) demonstrated robust performance, achieving AUCs of 0.91, 0.82, and 0.88 in the training, internal, and external test sets, respectively. Tumor thickness (P =.034) and tumor length (P <.001) were identified as independent predictors, further enhancing the model’s staging accuracy. Conclusion The proposed fusion model provides a noninvasive, effective tool for preoperative differentiation of T1 and T2 rectal cancer. While the model achieved the best predictive performance in this study, prospective validation is required before clinical implementation.

To develop and validate a diagnostic framework integrating intralesional (ILN) and perilesional (PLN) radiomics derived from multiparametric MRI (mpMRI) for distinguishing IgG4-related ophthalmic disease (IgG4-ROD) from orbital mucosa-associated lymphoid tissue (MALT) lymphoma. This multicenter retrospective study analyzed 214 histopathologically confirmed cases (68 IgG4-ROD, 146 MALT lymphoma) from two institutions (2019–2024). A LASSO-SVM classifier was optimized through comparative evaluation of seven machine learning models, incorporating fused radiomic features (1,197 features) from ILN/PLN regions. Diagnostic performance was benchmarked against two subspecialty radiologists (10–20 years’ experience) using receiver operating characteristics - area under the curve (AUC), precision-recall AUC (PR-AUC), and decision curve analysis (DCA), adhering to CLEAR/METRICS guidelines. The fusion model (FR_RAD) achieved state-of-the-art performance, with an AUC of 0.927 (95

Rectal cancer (RC) is one of the most common malignant tumors of the digestive system and has an alarmingly high incidence and mortality rate globally. Compared to conventional imaging examinations, radiomics can extract quantitative features that reflect tumor heterogeneity and mine data from medical images. In this review, we discuss the potential value of multimodal MRI-based radiomics in the diagnosis and treatment of RC, with a special emphasis on the role of peritumoral tissue characteristics in clinical decision-making. Existing studies have shown that a radiomics model integrating intratumoral and peritumoral characteristics has good application prospects in RC staging evaluation, efficacy prediction, metastasis monitoring, recurrence early warning, and prognosis judgment. At the same time, this paper also objectively analyzes the existing methodological limitations in this field, including insufficient data standardization, inadequate model validation, limited sample size and poor reproducibility of results. By combining existing evidence, this review aimed to enhance the attention of clinicians and radiologists on the characteristics of peritumoral tissues and promote the translational application of radiomics technology in the individualized treatment of RC.

Background:Non-invasive preoperative prediction of lymphovascular space invasion (LVSI) in cervical cancer (CC) is clinically important for guiding surgical planning and adjuvant therapy, while avoiding the risks associated with invasive procedures. However, current studies using amide proton transfer-weighted (APTw) MRI for LVSI prediction typically analyze only the mean values from a limited number of intratumoral regions of interest (ROIs), which fails to fully capture tumor heterogeneity. This study investigates the added value of whole-tumor APTw habitat radiomics in predicting LVSI and its advantages over conventional analysis methods. Methods:This prospective study included consecutive adult patients with suspected CC who underwent APTw MRI between December 2022 and December 2024; a portion of the cohort has been reported previously. APTw values were extracted using two methods: (1) the conventional approach, calculating the mean signal from three ROIs on a representative slice; and (2) habitat radiomics, involving whole-tumor segmentation, k-means clustering to identify functional subregions, and radiomic feature extraction. Pathological assessment of LVSI from hysterectomy specimens served as the reference standard. Multivariable logistic regression identified variables associated with LVSI and developed diagnostic models. Model robustness was evaluated by 5-fold cross-validation, with AUC and DeLong's test used for performance assessment. Results:Among 124 patients (74 LVSI-, 50 LVSI+), the APTw_h3 model achieved a higher AUC (0.796 [95% CI: 0.709-0.882]) for predicting LVSI positivity than the clinical-radiological model (AUC = 0.733, 95% CI: 0.638-0.817). The combined model integrating clinical, radiological, and APTw_h3 features achieved the highest AUC (0.903, 95% CI: 0.841-0.952), which was significantly higher than those of both the clinical-radiological and APTw_h3 models (both P < 0.001). Moreover, the addition of APTw_h3 to the clinical-radiological model improved sensitivity (88% vs. 82%) and specificity (83.8% vs. 64.9%) for determining LVSI positivity. Conclusion:Whole-tumor APTw habitat radiomics demonstrates superior performance over conventional mean-value APTw analysis for preoperative prediction of LVSI in CC. Notably, integrating habitat radiomic features with clinical and radiological parameters further improves predictive accuracy, demonstrating potential for enhanced individualized patient management.

ObjectiveTo evaluate the clinical application of the CT-based radiomics prediction model for discriminating SCC and SCH.MethodsA total of 254 clinical samples were selected from 291 patients with larynx-occupying lesions who underwent primary surgery. All lesions were validated via histopathological examination at The Second Hospital of Jilin University between June 2004 and December 2019. All patients were randomly allocated to the training (n = 177) and validation (n = 77) cohorts. After the acquisition of CT images, manual 3D tumor segmentation was performed using the CT images of the arterial, venous, and non-contrast phases via ITK-SNAP software. Subsequently, radiomics features were extracted using A.K. software. Based on the above features, three different diagnostic models (CTN, CTA+CTV, and CTN+CTA+CTV) were constructed to classify squamous cell carcinoma (SCC) and squamous cell hyperplasia (SCH). Additionally, receiver operating characteristic (ROC) and decision curve analysis (DCA) curves were measured to evaluate the diagnostic characteristics and clinical safety of the proposed three prognostic models.ResultsIn the radiomic prediction Model 1 (CTN), the area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the training cohorts in differentiating SCC and SCH were 0.883, 0.785, 0.645, 1.000, 1.000, and 0.648, while in the testing cohorts, these values were 0.852, 0.792, 0.66, 1.000, 1.000, and 0.652, respectively. In the radiomic prediction Model 2 (CTA+CTV), the AUC, accuracy, sensitivity, specificity, PPV, and NPV values of the training cohorts were 0.965, 0.91, 0.916, 0.9, 0.933, and 0.875, respectively, while in the testing cohorts, the corresponding values were 0.902, 0.805, 0.851, 0.733, 0.833, and 0.759, respectively. In the radiomic prediction Model 3(CTN+CTA+CTV), the AUC, accuracy, sensitivity, specificity, PPV, and NPV values of the training cohorts were 0.985, 0.944, 0.953, 0.929, 0.953, and 0.929, while in the testing cohorts, the corresponding values were 0.965, 0.857, 0.894, 0.8, 0.875, and 0.828, respectively.ConclusionThe radiomic prediction Model 3, based on the arterial-venous-plain combined scan phase of CT, achieved promising diagnostic performance, expected to be regarded as a preoperative imaging tool in classifying SCC and SCH to guide clinicians to develop individualized treatment programs.

Chemodynamic therapy (CDT), designed to trigger a tumor-specific hydrogen peroxide (H2O2) reaction generating highly toxic hydroxyl radicals (·OH), has been investigated for cancer treatment. Unfortunately, the limited Fenton or Fenton-like reaction rate and the significant impact of excessive reducing glutathione (GSH) in the tumor microenvironment (TME) have severely compromised the effectiveness of CDT. To address this issue, we designed a dual-responsive nanoplatform utilizing a metal-polyphenol network (MPN) -coated multi-caged IrOx for efficient anti-tumor therapy in response to the acidic TME and intracellular excess of GSH, in which MPN composed of Fe3+ and tannic acid (TA). Initially, the acidic TME and intracellular excess of GSH lead to the degradation of the MPN shell, resulting in the release of Fe3+ and exposure of the IrOx core, facilitating the efficient dual-pathway CDT. Subsequently, the nanoplatform can mitigate the attenuation of CDT by consuming the excessive GSH within the tumor. Finally, the multi-caged structure of IrOx is advantageous for effectively implementing photothermal therapy (PTT) in coordination with CDT, further enhancing the therapeutic efficacy of tumors. Moreover, the outstanding Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) (T1/T2) multimodal imaging capabilities of IrOx@MPN enable early diagnosis and timely treatment. This work provides a typical example of the construction of a novel multifunctional platform for dual-responsive treatment of tumors.

Background:Parametrial infiltration (PMI) is an important indicator for staging and treatment of cervical cancer (CC). The potential of amide proton transfer-weighted (APTw) parameters of peritumor tissue in predicting PMI is still uncertain. This study aims to explore whether the APTw parameters of peritumor tissue can improve diagnostic value of diffusion-weighted imaging (DWI) in magnetic resonance imaging (MRI). Methods:Eighty-one patients with pathologic analysis-confirmed CC were enrolled in this retrospective study. All patients underwent APTw MRI and DWI. The APTw values of tumor (APTw-t), APTw values in peritumor tissues (APTw-p) and apparent diffusion coefficient (ADC) values were independently reviewed by two radiologists to map the regions of interest and measure the corresponding values. Receiver operating characteristic curves were generated to evaluate the diagnostic performance of these quantitative parameters. Results:The study patients were divided into the PMI group (n=22) and non-PMI group (n=59). The APTw-t and APTw-p values (%) of PMI group were higher than those of the non-PMI group [3.71 (interquartile range, IQR, 3.60-3.98) and 2.75 (IQR, 2.68-2.77) vs. 3.33 (IQR, 3.24-3.60) and 1.98 (IQR, 1.82-2.36); P<0.001]. The ADC values of PMI group were lower than those of non-PMI group [0.88 (IQR, 0.83-0.94) ×10-3 vs. 0.95 (IQR, 0.88-1.04)×10-3 mm2/sec; P<0.001]. The area under the curve (AUC) of APTw-t, APTw-p and ADC value for PMI diagnosis were 0.810, 0.831 and 0.806 respectively. In addition, the AUC value (0.918) of APTw-p + ADC was optimal, with a sensitivity and specificity of 91.20% and 87.20% respectively. Conclusions:APTw in peritumor tissues, combined with ADC value can be used to efficiently distinguish PMI of CC.

Objective:To investigate the value of the 8-channel eye surface phased array coil in improving image quality and demonstrating ocular masses on 3.0 T MR scanner.Methods:From July 2018 to January 2020, the data of orbital MRI in 692 patients with ocular masses on 6 medical centers were prospectively collected. The patients were simple randomly assigned into 8-channel eye surface phased array coil group (413 patients) or 8-channel head phased array coil group (279 patients), with the same MRI sequences. The signal to noise ratio (SNR) and contrast to noise ratio (CNR) were calculated in orbital anatomy structures and masses (eyelid mass, intraocular mass, lacrimal mass and orbital mass). The image quality scores including motion artifact, mass margin, the relationship between the mass and adjacent structures, and overall image quality were recorded. The differences of image quality between the two groups were compared by two independent sample t-test or Wilcoxon rank test. Results:The SNR and CNR were higher in eye surface coil group than those in head coil group ( P<0.05). The scores of ocular movement artifacts were higher in head coil group than those in surface coil group ( P<0.05). The scores of intraocular mass margin, the relationship between the mass and adjacent structures, and overall image quality were higher in surface coil group than those in head coil group ( P<0.001). There were no significant differences in mass margin, the relationship between the mass and adjacent structures, and overall image quality scores of eyelid, lacrimal gland, and orbital mass between the two groups ( P>0.05). Conclusion:3.0 T MR scanner combined with the 8-channel eye surface phased array coil can improve the SNR and CNR of orbital MR images, the demonstration of the intraocular mass margin and the relationship between the mass and adjacent structures.

Objective:To analyze the imaging features of meningitis involved by otogenic lesions to improve the imaging diagnostic efficiency of ear lesions.Investigate the risk factors of meningitis caused by ear cholesteatoma.Methods:Imaging data of patients who underwent ear magnetic resonance plain scan plus diffusion and enhancement examination and CT plain scan of temporal bone in our hospital from January 2020 to January 2023 were collected.Imaging and clinical information of patients with otogenetic diseases with meningeal involvement and postoperative pathology were collected and retrospectively an-alyzed,and imaging characteristics of meningeal involvement were retrospectively analyzed.At the same time,the imaging and clinical information of patients with middle ear cholesteatoma with destruction of the tympanic roof on CT but no meningeal involvement on MRI were collected,and the risk factors of meningitis caused by middle ear cholesteatoma were retrospectively analyzed.Results:The neoplastic lesions of the ear involving the meninges are mainly squamous cell carcinomas.There were statistical differences in coronal diameter of thickened and enhanced meninges∕maximum coronal diameter of bone destruction(Rc),sagittal diameter of thickened and enhanced meninges∕maximum sagittal diameter of bone destruction(Rs)and maximum thickness between non-tumor group and malignant tumor group,with Z values of 2.05,2.15,3.09,P<0.05,respectively.As for ear cholesteatoma patients,there were no significant differences in gender and smoking history between the meningeal en-hancement group and non-meningeal enhancement group,χ2 values were 0.50 and 0.42,P>0.05,respectively.There was no significant difference in the mean diameter of cholesteatoma(Rm)and the mean diameterof tympanical roof destruction(Dm)(Z= 1.13,0.89 respectively,P>0.05).The granulation tissue thickness of the damaged tympanic roof was significantly different be-tween the two groups(Z=5.94,P<0.001).Conclusion:The imaging manifestations of meningeal thickening and enhancement caused by malignant tumors and non-neoplastic ear lesions are different.The former has greater membrane thickening,but the range of membrane enhancement relative to the extent of bone destruction was smaller than the latter.The presence of granu-lation tissue around the cholesteatoma in the destruction place of the tympanic roof is a risk factor for meningeal involvement in patients with middle ear cholestatoma.

目的 探讨利用Revolution CT对心律不齐患者行胸痛三联征CT血管成像检查的图像质量及可诊断性.资料与方法回顾性收集2020年1—9月在吉林大学第二医院因急性胸痛伴心律不齐行胸痛三联征CT血管成像检查的50例患者设为A组,选取同时期、相同对比剂剂量下心律规整且心率<80次/min的50例患者设为B组,两组均采用Revolution CT扫描,分别评价两组的图像质量,并计算辐射剂量.结果 A组冠状动脉、肺动脉及主动脉的对比噪声比分别为34.98±11.11、39.08±11.93、37.82±12.08;B组分别为33.08±8.75、37.17±11.18、38.08±9.58,两组差异均无统计学意义(t=0.95、0.82、0.46,P均>0.05);两组患者冠状动脉、肺动脉及主动脉图像质量主观评分比较,差异无统计学意义(t=1.15、0.99、0.25,P均>0.05);两组有效剂量差异无统计学意义[(14.75±3.08)mSv比(13.77±2.05)mSv;t=1.84,P=0.07].结论 胸痛三联征CT血管成像中,对于心律不齐的患者采用Revolution CT扫描可以保证获得的图像满足诊断需求.

Background and purposeInverted papilloma (IP) and nasal polyp (NP), as two benign lesions, are difficult to distinguish on MRI imaging and clinically, especially in predicting whether the olfactory nerve is damaged, which is an important aspect of treatment and prognosis. We plan to establish a new biomarker to distinguish IP and NP that may invade the olfactory nerve, and to analyze its diagnostic efficacy.Materials and methodsA total of 74 cases of IP and 55 cases of NP were collected. A total of 80% of 129 patients were used as the training set (59 IP and 44 NP); the remaining were used as the testing set. As a multimodal study (two MRI sequences and clinical indicators), preoperative MR images including T2-weighted magnetic resonance imaging (T2-WI) and contrast-enhanced T1-weighted magnetic resonance imaging (CE-T1WI) were collected. Radiomic features were extracted from MR images. Then, the least absolute shrinkage and selection operator (LASSO) regression method was used to decrease the high degree of redundancy and irrelevance. Subsequently, the radiomics model is constructed by the rad scoring formula. The area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model have been calculated. Finally, the decision curve analysis (DCA) is used to evaluate the clinical practicability of the model.ResultsThere were significant differences in age, nasal bleeding, and hyposmia between the two lesions (p < 0.05). In total, 1,906 radiomic features were extracted from T2-WI and CE-T1WI images. After feature selection, using 12 key features to bulid model. AUC, sensitivity, specificity, and accuracy on the testing cohort of the optimal model were, respectively, 0.9121, 0.828, 0.9091, and 0.899. AUC on the testing cohort of the optimal model was 0.9121; in addition, sensitivity, specificity, and accuracy were, respectively, 0.828, 0.9091, and 0.899.ConclusionA new biomarker combining multimodal MRI radiomics and clinical indicators can effectively distinguish between IP and NP that may invade the olfactory nerve, which can provide a valuable decision basis for individualized treatment.

目的 探讨任务驱动教学法结合在线教学在医学影像学本科教学中的教学效果.方法 将2022年3月中枢神经系统影像学理论课学习的2019级临床医学五年制学生设为A组,采用任务驱动教学法结合在线教学授课,在线教学参与情况计入过程性考核;将2021年3月中枢神经系统影像学理论课及实习课学习的2018级临床医学五年制学生设置为B组,采用传统授课法,两组学生性别、年龄无统计学差异.A组授课学时8学时,B组12学时.课后测试其阅片能力,并对教学满意度进行调查.结果 A组测试成绩(94.66±8.91)高于B组(93.00±11.54),差异无统计学意义(P>0.05).结论 任务驱动教学法结合在线教学与传统理论课结合实习课的教学方法相比,课堂效率更高;将在线教学参与度纳入过程性考核,学生参与度高,满意度高,自主学习能力有所提高.

影像检查在卒中的诊断、鉴别诊断和疗效评估中都具有重要的临床价值.在影像学技术中,MRI既有多序列优势,又可同时进行功能成像,对卒中患者可同时实现从形态到功能的多维度评估,为制订正确的治疗方案和评估预后提供重要的影像学依据.本文对1例青年卒中患者的多模态影像评估进行总结,与大家分享.

患者 女,60 岁,因记忆力下降6 个月,加重伴头痛、情绪低落1 个月,一过性行走不稳1 周入院.既往糖尿病10 年、甲状腺功能减退20 年.MRI检查示T2 WI及T2 液体衰减反转恢复双侧丘脑呈高信号,右侧为甚(图 1 ,2 ),扩散加权成像(diffusion weighted imaging,DWI)呈等信号(图 3 ),磁敏感加权成像(susceptibility weighted imaging,SWI)内部见低信号(图4),增强左侧病灶内部见斑片状强化(图 5).

Purpose: To evaluate the capability of non-enhanced computed tomography (CT) images for distinguishing between orbital cavernous venous malformations (OCVM) and non-OCVM, and to identify the optimal model from radiomics-based machine learning (ML) algorithms. Methods: A total of 215 cases of OCVM and 120 cases of non- OCVM were retrospectively analyzed in this study. A stratified random sample of 268 patients (80%) was used as the training set (172 OCVM and 96 non-OCVM); the remaining data were used as the testing set. Six feature selection techniques and thirteen ML models were evaluated to construct an optimal classification model. Results: There were statistically significant differences between the OCVM and non-OCVM groups in the density and tumor location (P < 0.05), whereas other indicators were comparable (age, gender, sharp, P > 0.05). Linear regression (area under the curve [AUC] = 0.9351; accuracy = 0.8657) and Stochastic Gradient Descent (AUC = 0.9448; accuracy = 0.8806) classifiers, both of which coupled with the f test and L1-based feature selection method, achieved optimal performance. The support vector machine (AUC = 0.9186; accuracy = 0.8806), Random Forest (AUC = 0.9288; accuracy = 0.8507) and eXtreme Gradient Boosting (AUC = 0.9147; accuracy = 0.8507) classifier combined with f test method showed excellent average performance among our study, respectively. Conclusions: The effect of non-enhanced CT images in OCVM not only can help ophthalmologist to find and locate lesion, but also bring great help for the qualitative diagnosis value using radiomic- based ML algorithms.

Objective:To investigate the value of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) in improving ability of demonstrating ocular masses on 3.0 T MR scanner.Methods:This study was a multi-center prospective study involving 6 centers. From July 2018 to January 2020, totally 413 patients with ocular masses from 6 centers were prospectively enrolled, and all of them underwent T 1WI and T 2WI, PROPELLER T 1 FLAIR and T 2WI, and contrast-enhanced scans. The signal intensity of eyelid, vitreous body, lacrimal gland, intraorbital segment of optic nerve, and orbital masses of eyelid, intraocular, lacrimal gland and retrobulbar were measured by two radiologists, and the signal to noise ratio (SNR) and contrast noise ratio (CNR) were calculated. The 5-point scoring method was used to evaluate the motion artefacts, tumor edges and the relationship between the tumor and adjacent structures, and the overall score of image quality was calculated. Paired t-test or Wilcoxon signed rank test was used to compare the image quality between PROPELLER and non-PROPELLER images. Results:The SNR and CNR of PROPELLER T 2WI were higher than those of non-PROPELLER T 2WI (all P<0.001). The SNR and CNR of PROPELLER T 1 FLAIR were lower than those of non-PROPELLER T 1WI (all P<0.05). The scores of artefacts and overall image quality in PROPELLER images were higher than those in non-PROPELLER images (all P<0.001). The tumor edge and the relationship between the tumor and adjacent structures scores of eyelid, intraocular, and lacrimal gland masses in PROPELLER images were higher than those in non-PROPELLER images (all P<0.001),while compared to non-PROPELLER images, retro-global masses in PROPELLER images showed no significant differences (all P>0.05). Conclusion:PROPELLER can reduce ocular motion artefacts, effectively improve image quality and ability of demonstrating anterior (eyelid, intraocular, and lacrimal gland) masses.

目的 探讨混合式教学方法(课程思政结合具体案例为价值引领,课堂教学为主渠道,在线教学为辅助)在医学影像学实习课中的教学效果.方法 采用实验对照法,将2020年12月在吉林大学第二医院进行医学影像学实习课的2017级临床医学生("5+3"一体化)按照学生学号分为两组,每组31人,学号的顺序与学生的年龄、性别及学习成绩无关.两组学生性别及年龄差异均无统计学意义,具有可比性.所有学生理论课学习背景完全相同,实习课教学学时数均为16学时.实习一组采用传统授课方法,老师采用多媒体、PPT进行授课,实习二组采用混合式教学方法,课后对两组学生的临床阅片能力进行测试,进而对教学效果进行评价.结果 实习二组阅片能力测试成绩(92.78±9.68)高于实习一组(75.23±20.57),差异具有统计学意义(P<0.05).结论 在医学影像学实习课中,采用混合式教学方法有助于提高学生的临床阅片水平及临床实践能力.

Bi-based nanomaterials, such as Bi 2 Se 3 , play an important part in biomedicine, such as photothermal therapy (PTT) and computed tomography (CT) imaging. Polyethylenimine (PEI)-modified ultrasmall Bi 2 Se 3 nanodots were prepared using an ultrafast synthetic method at room temperature (25°C). Bi 2 Se 3 nanodots exhibited superior CT imaging performance, and could be used as effective photothermal reagents owing to their broad absorption in the ultraviolet–visible–near infrared region. Under irradiation at 808 nm, PEI-Bi 2 Se 3 nanodots exhibited excellent photothermal-conversion efficiency of up to 41.3%. Good biocompatibility and significant tumor-ablation capabilities were demonstrated in vitro and in vivo . These results revealed that PEI-Bi 2 Se 3 nanodots are safe and a good nanotheranostic platform for CT imaging-guided PTT of cancer.

Objectives: To differentiate squamous cell hyperplasia (SCH) (benign) from squamous cell carcinoma (SCC) malignant) using textural features extracted from CT images and thereby, facilitate the preoperative medical diagnosis and treatment of throat cancers without the need for sample biopsies. Methods: In total, 100 throat cancer patients were selected for this retrospective study. The cases were collected from the Second Hospital of Jilin University, Changchun, China, from June 2017 to January 2019. The patients were separated into a training and validation cohort consisting of 70 and 30 cases, respectively. The Artificial Intelligence Kit software (A.K. software) was used to extract the radiomics features from the CT images. These features were further processed using the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) methods to obtain a subset of optimal features. The radiomics model was validated based on area-under-the-curve (AUC) values, accuracy, specificity, and sensitivity using the R-studio software. Results: The diagnostic accuracy, specificity, PPV, NPV, and AUC values obtained for the training cohort was 0.91, 0.9, 0.93, 0.9, and 0.96 CT angiography (CTA), 0.93, 0.93, 0.95, 0.90, and 0.96 computed tomography normal (CTN), and 0.92, 0.87, 0.91, 0.96, and 0.96 CT venogram (CTV). These values were subsequently confirmed in the validation cohort. Conclusion: The radiomics-based prediction model proposed in this study successfully differentiated between SCH and SCC throat cancers using CT imaging, thereby facilitating the development of accurate preoperative diagnosis based on specific biomarkers and cancer phenotypes.

The rapid development of medical imaging has boosted the abilities of modern medicine. As single modality imaging limits complex cancer diagnostics, dual-modal imaging has come into the spotlight in clinical settings. The rare earth element Holmium (Ho) has intrinsic paramagnetism and great X-ray attenuation due to its high atomic number. These features endow Ho with good potential to be a nanoprobe in combined x-ray computed tomography (CT) and T 2 -weighted magnetic resonance imaging (MRI). Herein, we present a facile strategy for preparing HoF 3 nanoparticles (HoF 3 NPs) with modification by PEG 4000. The functional PEG-HoF 3 NPs have good water solubility, low cytotoxicity, and biocompatibility as a dual-modal contrast agent. Currently, there is limited systematic and intensive investigation of Ho-based nanomaterials for dual-modal imaging. Our PEG-HoF 3 NPs provide a new direction to realize in vitro and vivo CT/MRI imaging, as well as validation of Ho-based nanomaterials will verify their potential for biomedical applications.