Prognostic prediction plays a pivotal role in guiding personalized treatment for patients with locoregionally advanced nasopharyngeal carcinoma (LANPC). However, few studies have investigated the incremental value of functional MRI to the conventional MRI-based radiomic models. Here, we aimed to develop a radiomic model including functional MRI to predict the prognosis of LANPC patients. One hundred and twenty-six patients (training dataset, n = 88; validation dataset, n = 38) with LANPC were retrospectively included. Radiomic features were extracted from T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), contrast-enhanced T1WI (cT1WI), and diffusion-weighted imaging (DWI). Pearson correlation analysis and recursive feature elimination or Relief were used for identifying features associated with progression-free survival (PFS). Five machine learning algorithms with cross-validation were compared to develop the optimal single-layer and fusion radiomic models. Clinical and combined models were developed via multivariate Cox regression model. The clinical model based on TNM stage achieved a C-index of 0.544 in the validation dataset. The fusion radiomic model, incorporating DWI-, T1WI-, and cT1WI-derived imaging features, yielded the highest C-index of 0.788, outperforming DWI-based (C-index = 0.739), T1WI-based (C-index = 0.734), cT1WI-based (C-index = 0.722), and T1WI plus cT1WI-based models (C-index = 0.747) in predicting PFS. The fusion radiomic model yielded the C-index of 0.786 and 0.690 in predicting distant metastasis-free survival and overall survival, respectively. However, the addition of TNM stage to the fusion radiomic model could not improve the predictive power. The fusion radiomic model demonstrates favorable performance in predicting survival outcomes in LANPC patients, surpassing TNM staging alone. Integration of DWI-derived features into conventional MRI radiomic models could enhance predictive accuracy.

Although stage I gastric cancer (GC) presents a favorable survival rate, outcomes for patients experiencing recurrence remain poor. This research focuses on assessing the prognosis and identifying risk factors for stage I GC patients, further assessing the necessity of adjuvant chemotherapy (AC). The study involved a retrospective analysis of 902 patients with stage I GC who received curative resection from November 2010 to December 2020. Independent prognostic factors were identified using multivariate Cox regression analysis. Kaplan-Meier analysis was employed to compare recurrence-free survival (RFS) and disease-specific survival (DSS) across different groups. During follow-up, 47 patients (5.2

Prognostic models play a crucial role in providing personalised risk assessment, guiding treatment decisions, and facilitating the counselling of patients with cancer. However, previous imaging-based artificial intelligence models of epithelial ovarian cancer lacked interpretability. In this study, we aimed to develop an interpretable machine-learning model to predict progression-free survival in patients with epithelial ovarian cancer using clinical variables and radiomics features. A total of 102 patients with epithelial ovarian cancer who underwent contrast-enhanced computed tomography scans were enrolled in this retrospective study. Pre-surgery clinical data, including age, performance status, body mass index, tumour stage, venous blood cancer antigen-125 (CA125) level, white blood cell count, neutrophil count, red blood cell count, haemoglobin level, and platelet count, were obtained from medical records. The volume of interest for each tumour was manually delineated slice-by-slice along the boundary. A total of 2074 radiomic features were extracted from the pre- and post-contrast computed tomography images. Optimal radiomic features were selected using the Least Absolute Shrinkage and Selection Operator logistic regression. Multivariate Cox analysis was performed to identify independent predictors of three-year progression-free survival. The random forest algorithm developed radiomic and combined models using four-fold cross-validation. Finally, the Shapley additive explanation algorithm was applied to interpret the predictions of the combined model. Multivariate Cox analysis identified CA-125 levels (P=0.015), tumour stage (P=0.019), and Radscore (P<0.001) as independent predictors of progression-free survival. The combined model based on these factors achieved an area under the curve of 0.812 (95% confidence interval: 0.802-0.822) in the training cohort and 0.772 (95% confidence interval: 0.727-0.817) in the validation cohort. The most impactful features on the model output were Radscore, followed by tumour stage and CA-125. In conclusion, the Shapley additive explanation-based interpretation of the prognostic model enables clinicians to understand the reasoning behind predictions better.

Individual prognosis assessment is of paramount importance for treatment decision-making and active surveillance in cancer patients. We aimed to propose a radiomic model based on pre- and post-therapy MRI features for predicting disease-free survival (DFS) in locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (nCRT) and subsequent surgical resection. This retrospective study included a total of 126 LARC patients, which were randomly assigned to a training set (n=84) and a validation set (n=42). All patients underwent pre- and post-nCRT MRI scans. Radiomic features were extracted from higher resolution T2 -weighted images. Pearson correlation analysis and ANOVA or Relief were utilized for identifying radiomic features associated with DFS. Pre-treatment, post-treatment, and delta radscores were constructed by machine learning algorithms. An individualized nomogram was developed based on significant radscores and clinical variables using multivariate Cox regression analysis. Predictive performance was evaluated by the C-index, calibration curve, and decision curve analysis. The results demonstrated that in the validation set, the clinical model including pre-surgery Carcinoembryonic Antigen (CEA), chemotherapy after radiotherapy, and pathological stage yielded a C-index of 0.755 (95% confidence interval [CI]: 0.630-0.880). While the optimal pre-, post-, and delta-radscores achieved C-indices of 0.724 (95%CI: 0.585-0.863), 0.701 (95%CI: 0.544-0.858), and 0.625 (95%CI: 0.439-0.811), respectively. The nomogram integrating pre-surgery CEA, pathological stage, alongside pre- and post-nCRT radscore, obtained the highest C-index of 0.833 (95%CI: 0.694-0.972). The calibration curve and decision curves exhibited good calibration and clinical usefulness of the nomogram. Furthermore, the nomogram categorized patients into high- and low-risk groups exhibiting distinct DFS (both P<0.0001). In summary, the nomogram incorporating pre- and post-therapy radscores and clinical factors could predict DFS in patients with LARC, which warrants further external validations.

Rationale and Objectives: The 15%-27% of patients with locally advanced rectal cancer (LARC) achieved pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) and could avoid proctectomy. We aimed to investigate the effectiveness of treatment response prediction using MRI-based pre-, post-, and delta-radiomic features for LARC patients treated with nCRT and to compare these radiomic models with radiologists' visual assessment. Materials and Methods: A total of 126 patients with LARC who received nCRT before surgery were included and randomly divided into a training set (n = 84) and a validation set (n = 42). 250 radiomic features were extracted from T2-weighted images from pre- and post-nCRT MRI. Pearson correlation analysis and AONVA or Relief were used to identify radiomic descriptors associated with pCR. Five machinelearning classifiers were compared to construct radiomic models. The radiomic nomogram was built via multivariate logistic regression analysis. Two senior radiologists independently rated tumor regression grades and compared with radiomic models. Area under the curve (AUC) of the models and pooled observers were compared by using the DeLong test. Results: The optimal pre-, post-, and delta-radiomic models yielded an AUC of 0.717 (95% CI: 0.639-0.795), 0.805 (95%CI: 0.736-0.874), and 0.724 (95%CI: 0.648-0.800), respectively. The radiomic nomogram based on pre-nCRT cN stage, pre-nCRT radscore, and pos-tnCRT radscore achieved an AUC of 0.852 (95%CI: 0.774-0.930), which was higher than the single radiomic models and pooled readers (all p < 0.05). Conclusions: The radiomic nomogram is an effective and invasive tool to predict pCR in LARC patients after nCRT, which outperforms radiologists.

Objective To establish and validate a radiomics nomogram based on the features of the primary tumor for predicting preoperative pathological extramural venous invasion (EMVI) in rectal cancer using machine learning. Methods The clinical and imaging data of 281 patients with primary rectal cancer from April 2012 to May 2018 were retrospectively analyzed. All the patients were divided into a training set (n = 198) and a test set (n = 83) respectively. The radiomics features of the primary tumor were extracted from the enhanced computed tomography (CT), the T2-weighted imaging (T2WI) and the gadolinium contrast-enhanced T1-weighted imaging (CE-TIWI) of each patient. One optimal radiomics signature extracted from each modal image was generated by receiver operating characteristic (ROC) curve analysis after dimensionality reduction. Three kinds of models were constructed based on training set, including the clinical model (the optimal radiomics signature combining with the clinical features), the magnetic resonance imaging model (the optimal radiomics signature combining with the mrEMVI status) and the integrated model (the optimal radiomics signature combining with both the clinical features and the mrEMVI status). Finally, the optimal model was selected to create a radiomics nomogram. The performance of the nomogram to evaluate clinical efficacy was verified by ROC curves and decision curve analysis curves. Results The radiomics signature constructed based on T2WI showed the best performance, with an AUC value of 0.717, a sensitivity of 0.742 and a specificity of 0.621. The radiomics nomogram had the highest prediction efficiency, of which the AUC was 0.863, the sensitivity was 0.774 and the specificity was 0.801. Conclusion The radiomics nomogram had the highest efficiency in predicting EMVI. This may help patients choose the best treatment strategy and may strengthen personalized treatment methods to further optimize the treatment effect.

目的:探讨磁共振成像纹理特征对局部进展期直肠癌新辅助放化疗病理反应状态的预测价值.方法:回顾性分析58例局部进展期直肠癌患者的临床及影像学资料,利用MaZda软件于T2WI图像肿瘤最大层面手动勾画ROI,分别在新辅助放化疗前、后的图像上各提取9个一级、11个二级纹理特征参数,对参数进行统计学分析,比较pCR组与非pCR组在新辅助放化疗前、后纹理特征上的差别.结果:pCR组与非pCR组之间,治疗前7个二级纹理特征(pre-Correlat、InvDfMom、SumEntrp、DifEntrp、DifVarnc、Entropy、AngScMom)差异有统计学意义,治疗后3个一级纹理特征(post-Variance、Perc 90％、Perc 99％)、9个二级纹理特征(postContrast、SumOfsqs、InvDfMom、SumAverg、SumVarnc、SumEntrp、DifEntrp、DifVarnc、Entropy)差异有统计学意义.在预测pCR方面,单个纹理特征的ROC曲线结果显示,治疗前AUC值在0.632～0.835之间,治疗后AUC值在0.665～0.852之间,分别以DifVarnc、DifEntrp效能最高.采用多变量logistic回归分析预测pCR时,治疗前及治疗后的独立预测因子均是DifEntrp,治疗前(P= 0.005)AUC值为0.833,治疗后(P =0.004)AUC值为0.852.结论:基于T2WI图像的纹理特征有助于预测局部进展期直肠癌新辅助放化疗的疗效,而二级纹理特征的预测效能高于一级纹理特征,nCRT治疗后为疗效预测的更佳时期.

目的:分析卵巢甲状腺肿(SO)的影像学表现.方法:回顾性分析经病理证实的22例SO的CT和MRI表现.结果:22例中21例为单侧发病,其中左侧9例,右侧12例;1例为双侧发病.肿瘤最大径2.8～18.4cm,平均(10.1±4.0)cm.18例为囊实性肿块,以多房囊性成分为主,4例为囊性肿块.18例行CT检查,平扫肿瘤囊腔以低密度为主,内夹杂不同大小的高密度区,本组5例病灶出现高密度区,CT值为52～86HU,增强后未见强化.肿瘤实性成分表现为厚的分隔、囊壁、结节,增强后类似甲状腺强化,其中12例囊壁、分隔或结节中见钙化.4例行MRI检查,肿瘤囊性成分信号复杂,实性成分T1WI、T2WI呈等信号为主,DWI呈高信号,增强后实性成分明显强化.结论:盆腔内单侧囊实性肿块,囊性成分复杂,CT表现囊腔内高密度区及囊壁或实性结节中夹杂斑片状钙化,MRI中T2WI病变内见极低信号,增强后实性成分及囊壁、分隔明显强化,应考虑SO的可能.

目的 探讨术前CT腹膜癌指数(PCI)作为无创性手段预测卵巢癌患者行卵巢肿瘤减灭术的效果的可行性及其与卵巢癌患者疾病无进展生存期(PFS)和总生存期(0S)之间的关系.方法 回顾性分析有术前全腹平扫+增强CT、治疗方式为直接手术且经病理证实为卵巢上皮性肿瘤患者,所有患者临床及影像资料完整,其临床随访截止时间为2020年12月30日.CT-PCI评分由两名放射科阅片者在不知道手术及病理结果的情况下分别进行回顾性阅片获得.手术PCI评分通过手术记录及病理资料获得.术前CT-PCI分数与手术PCI分数进行对照,计算Spearsrman相关系数.CT-PCI分数与初始肿瘤减灭术术后残留的关系用ROC曲线分析.CT-PCI与卵巢癌患者PFS及OS的关系用Kaplan-Meier生存曲线和多因素Cox回归分析进行.结果 共纳入病例150例.CT-PCI分数与术后有无残留相关(OR 1.633,95％置信区间1.421～1.878,P＜0.001).CT-PCI分数影响卵巢癌患者的PFS(HR 1.062,95％CI 1.015 ～1.112,P=0.009)及OS(HR 1.076,95％CI 1.018～1.137,P=0.009).上腹部腹膜受侵、小肠受侵是疾病PFS缩短的独立危险因素[HR =6.718(P ＜0.001),HR =9.763(P ＜0.001)];也是OS缩短的独立危险因素[HR =4.127(P=0.003),HR =7.480(P ＜0.001)].结论 术前CT-PCI分数与术后有无残留相关.术前CT-PCI分数与卵巢癌患者PFS和OS呈负相关.上腹部受侵和小肠肠管受侵或可作为卵巢癌患者PFS及OS的独立预后因素.

目的 利用64排螺旋CT对上腔静脉(SVC)的解剖进行观察分析,为临床应用及相关研究提供基础.方法 回顾分析本院306例患者的胸部薄层增强CT影像资料,测量气管隆突水平SVC的左右径及前后径,分析性别和年龄因素在SVC管径测值差异性;另外分别记录SVC起始部及房腔交界对应椎体的位置.结果 306例患者气管隆突水平SVC左右径、前后径分别为(18.8±2.9)mm、(21.4±2.9)mm.左右径存在显著性别差异,但相同性别不同年龄组间比较差异无统计学意义;前后径无显著性别及年龄差异.68.6％的患者SVC起始部位于T3水平、81.7％的患者房腔交界位于T6～T7水平.结论 胸部薄层增强CT可以清晰显示SVC的解剖结构及毗邻关系,可为临床应用提供可靠的解剖依据.

目的:探讨非特异性肉芽肿性乳腺炎(IGM)的X线、CT及MRI特征.方法:回顾性分析53例经手术病理证实的IGM的X线、CT及MRI资料.结果:X线示15例病灶呈等或稍高于正常腺体密度肿块影;5例呈局限性致密影或结构扭曲;9例因腺体致密显示不清.CT示14例呈等或稍高于正常腺体密度肿块影,增强扫描明显强化;2例呈囊性改变,增强扫描边缘强化.MRI示T1WI呈等或稍高信号,T2WI呈不等高信号,增强扫描病变片状强化,信号高于正常腺体,部分病变伴脓肿形成,部分伴环形强化结节影,TIC呈多样性.X线、CT及MRI可见部分病例伴乳头内陷、局部皮肤增厚.结论:IGM乳腺X线表现特异性差,CT与MRI表现具有一定特异性,影像结合临床有助于准确诊断.

Objective To investigate the clinical application and manifestation of dynamic contrastenhanced MRI (DCE-MRI) in differentiating true progession from pseudoprogression in patients with gliobastomas.Methods Twenty five glioma patients were treated with postoperative concurrent chemoradiotherapy and enrolled in this study.All patients were underwent DCE-MRI using a 1.5T scanner.Fifteen patients were confimmed by secondary pathology or clinical and imaging follow-up of patients with gliomas true progession (TP),10 patients were pseudoprogress (PP).Nonparametric Mann-Whitney test was used to compare perfusion parameters between two groups (TP and PP),were used for receiver operating characteristic (ROC) curve analysis to clear if these parameters can be the indicators to differentiate true progession from pseudoprogression.Results Ktrans (volume transfer constant),Ve (fractional volume of extravascular extracellular) values between TP and PP glioma groups were statistically significant,K and Ve values were significantly higher in the TP group than in the PP group (P ＜ 0.05).The areas under the ROC curve are 0.990 and 0.847,respectively.Kep (efflux rate constant) value,Vp (fractional volume of plasma) value in the identification of glioma TP group and PP group was not statistically significant (P ＞ 0.05).Conclusions DCE-MRI can be used to identify glioma TP and PP,Ktrans value and Ve value have clinical significance.

Objective To investigate the anatomical parameters involved in positioning the tip of peripherally inserted central catheter( PICC) . Methods Imaging data of 334 patients underwent contrast-enhanced thin-slice chest CT scan in the Affiliated Cancer Hospital of Xiangya School of Medicine of Central South University from June 2013 to January 2015 were retrospectively analyzed. Absolute value of anatomical parameters including distance between the upper margin of the right clavicular notch( RSCJ) and atriocaval junction ( ACJ) , distance between RSCJ and pericardial reflection( PR) , distance between carina and ACJ, distance between carina and PR, distance between right tracheobronchial angle ( RTBA) and ACJ, distance between RTBA and PR ,and the length of superior vena cava were calculated respectively. Each absolute value of anatomical parameter was divided by the height of the sixth thoracic vertebra body unit ( T6 unit) , denoted as relative value of anatomical parameters correspondingly. Results The ACJs were identified at the level of T6 or T7 in 82%(274/334) of patients. The PRs were found at the level of T5 or T6 in 79. 3%(265/334) of patients. The absoulute distance and relative distance (mean ± SD) of carina-to-ACJ, carina-to-PR, RSCJ-to-ACJ, RSCJ-to-PR, RTBA-to-ACJ and RTBA-to-PR were (38. 4 ± 8. 8),(21. 9 ± 9. 2), (50. 7 ± 9. 1),(34. 2 ± 9. 4),(110. 2 ± 15. 9),(93. 7 ± 16. 3 )mm, and (1. 85 ± 0. 43), (1. 05 ± 0. 44), (2. 44 ± 0. 45),(1. 64 ± 0. 45), (5. 30 ± 0. 75),(4. 50 ± 0. 75), respectively. The correlation analysis showed the absolute values of anatomic parameters were highly related with the patients ’ heights ( P <0. 01), while the relative values were significantly weakened with the patients’ heights and the partial relative values of anatomic parameters were not related with the patients’ heights(P>0. 05). Conclusions due to the varied position of PR.

Objective The aim of the study was to investigate the utility of intravoxel incoherent motion (IVIM) diffusion-weighted magnetic resonance imaging (DWI) for differentiating nasopharyngeal carcinoma (NPC) from lymphoma. Methods Intravoxel incoherent motion–based parameters including the apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudodiffusion coefficient (D*), perfusion fraction (f), and fD* (the product of D* and f) were retrospectively compared between 102 patients (82 with NPC, 20 with lymphoma) who received pretreatment IVIM DWI. Results Compared with lymphoma, NPC exhibited higher ADC, D, D*, fD* values (P < 0.001) and f value (P = 0.047). The optimal cutoff values (area under the curve, sensitivity, and specificity, respectively) for distinguishing the 2 tumors were as follows: ADC value of 0.761 × 10−3 mm2/s (0.781, 93.90%, 55.00%); D, 0.66 × 10−3 mm2/s (0.802, 54.88%, 100.00%); D*, 7.89 × 10−3 mm2/s (0.898, 82.93%, 85.00%); f, 0.29 (0.644, 41.46%, 95.00%); and fD*, 1.99 × 10−3 mm2/s (0.960, 85.37%, 100.00%). Conclusions Nasopharyngeal carcinoma exhibits different IVIM-based imaging features from lymphoma. Intravoxel incoherent motion DWI is useful for differentiating lymphoma from NPC.

Rationale and Objectives: The aim of the study was to investigate the diagnostic value of intravoxel incoherent motion diffusion-weighted magnetic resonance imaging (IVIM DWI) for discriminating nonmetastatic from metastatic mesorectal lymph nodes in rectal cancer.Materials and Methods: IVIM DWI was performed preoperatively on 50 patients with rectal carcinoma. The short-axis diameter, short to long-axis diameter ratio, and IVIM-based parameter (pure diffusion coefficient [D], pseudo-diffusion coefficient [D-star] and perfusion fraction [f]) values were compared between the metastatic and nonmetastatic lymph node groups.Results: The short-axis diameter; short- to long-axis diameter ratio; and D, D-star, and f values for the nonmetastatic lymph node group (n = 28) were 6.446 +/- 1.201 mm, 0.815 +/- 0.099, 1.071 +/- 0.234 x 10(-3) mm(2)/s, 15.443 +/- 5.946 mm(2)/s and 0.261 +/- 0.128, respectively, and were 9.045 +/- 3.185 mm, 0.809 +/- 0.099, 0.816 +/- 0.121 x 10(-3) mm(2)/s, 11.679 +/- 7.521 x 10(-3) mm(2)/s, and 0.190 +/- 0.064, respectively, for the metastatic lymph node group (n = 31). The short-axis diameter for the metastatic group was significantly higher than for the nonmetastatic group (P < 0.001). The metastatic group exhibited significantly lower D and D-star values than the nonmetastatic group (P < 0.01). The short to long-axis diameter ratio and f values did not differ significantly between the two groups. Optimal cutoff values (area under the curve, sensitivity, and specificity) for distinguishing metastatic from nonmetastatic lymph nodes were as follows: short-axis diameter = 5.563 mm (0.783, 74.2%, 82.1%); D = 0.667 x 10(-3) mm(2)/s (0.885, 77.4%, 89.3%); and D-star = 0.485 x10(-3) mm(2)/s (0.727, 80.6%, 67.9%).Conclusion: IVIM DWI is useful to differentiate between metastatic and nonmetastatic mesorectal lymph nodes in rectal cancer.