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Objective To investigate the clinical manifestations of familial adenomatous polyposis(FAP).Methods The clinical data of 100 patients with FAP diagnosed in the First Affiliated Hospital of Zhengzhou University from 2011 to 2021 were analyzed retrospectively.Results The main clinical manifestations were bloody stool(44.0%),abdominal pain(40.0%),changes of stool characteristics(25.0%),abdominal distension(18.0%)and diarrhea(17.0%);The most common types of adenomas were villous tubular adenoma(44.9%)and tubular adenoma(32.1%);Extraintestinal manifestations:3 cases of desmoid fibroma,2 cases of osteoma and 1 case of lipoma.47 cases of gastric polyps and the mostly pathological type was fundic gland polyp(34.0%).18 cases of duodenal polyps including 6 cases of adenomatous polyps and most were located at descending duodenum(61.1%).Adenoma canceration occurred in 21 patients.The average age was 38.7 and the canceration mainly occurs in the rectum.The gender,family history,age of onset,number of adenomas,diameter and pathological type of adenomas were statistically compared between patients with and without canceration.It was found that there were significant differences in gender,average age of onset,number of adenoma polyps,diameter of adenoma and pathological type between the two groups(P<0.05).Conclusions FAP is mainly characterized by bloody stool and abdominal pain,with high canceration rate.The risk factors include the age of onset,the number and size of adenomas and the pathological type.The main pathological types are villous tubular adenoma and tubular adenoma.At present,preventive colorectal resection is still the most effective way to treat FAP.No matter what type of methods patients choose,they should be followed up strictly under endoscopy in order to find the lesions in time and treat if necessary.
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BACKGROUND@#Artificial intelligence (AI) technology represented by deep learning has made remarkable achievements in digital pathology, enhancing the accuracy and reliability of diagnosis and prognosis evaluation. The spatial distribution of CD3 + and CD8 + T cells within the tumor microenvironment has been demonstrated to have a significant impact on the prognosis of colorectal cancer (CRC). This study aimed to investigate CD3 CT (CD3 + T cells density in the core of the tumor [CT]) prognostic ability in patients with CRC by using AI technology.@*METHODS@#The study involved the enrollment of 492 patients from two distinct medical centers, with 358 patients assigned to the training cohort and an additional 134 patients allocated to the validation cohort. To facilitate tissue segmentation and T-cells quantification in whole-slide images (WSIs), a fully automated workflow based on deep learning was devised. Upon the completion of tissue segmentation and subsequent cell segmentation, a comprehensive analysis was conducted.@*RESULTS@#The evaluation of various positive T cell densities revealed comparable discriminatory ability between CD3 CT and CD3-CD8 (the combination of CD3 + and CD8 + T cells density within the CT and invasive margin) in predicting mortality (C-index in training cohort: 0.65 vs. 0.64; validation cohort: 0.69 vs. 0.69). The CD3 CT was confirmed as an independent prognostic factor, with high CD3 CT density associated with increased overall survival (OS) in the training cohort (hazard ratio [HR] = 0.22, 95% confidence interval [CI]: 0.12-0.38, P <0.001) and validation cohort (HR = 0.21, 95% CI: 0.05-0.92, P = 0.037).@*CONCLUSIONS@#We quantify the spatial distribution of CD3 + and CD8 + T cells within tissue regions in WSIs using AI technology. The CD3 CT confirmed as a stage-independent predictor for OS in CRC patients. Moreover, CD3 CT shows promise in simplifying the CD3-CD8 system and facilitating its practical application in clinical settings.
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Humans , Lymphocytes, Tumor-Infiltrating , Colorectal Neoplasms , Artificial Intelligence , Reproducibility of Results , Prognosis , CD8-Positive T-Lymphocytes , Tumor MicroenvironmentABSTRACT
To investigate the effects of different wavelet filters on correlation and diagnostic performance of radiomics features. Methods: A total of 143 colorectal cancer (CRC) patients (64 positive in lymph node metastasis and 79 negative) with contrast-enhanced CT examination were recruited. After labeling the tumor area by experienced radiologists, radiomics wavelets features based on 48 different wavelets were extracted using in-house software coded by Matlab. The correlation coefficients of the features with same names between different wavelets were calculated and got the distribution of high-correlation features between each wavelet. The least absolute shrinkage and selection operator (LASSO) was used to build signatures between lymph node metastasis and wavelet features data set based on different wavelets. The numbers of features in signatures and diagnostic performance were compared using Delong's test. Results: With the difference of wavelet order increased, the number of high-correlation features between two wavelets decreased. Some features were prone to high correlation between different wavelets. When building radiomics signature based on single wavelet, signatures built from 'rbio2.2', 'sym7' and 'db7' did well in predicting lymph node metastasis. The signature based on Daubechies wavelet feature set had the highest performance in predicting lymph node metastasis, while the signature from Biorthogonal wavelet features was worst. Improvement was significant in diagnostic performance after excluding the high-correlation features in the whole features set (P=0.004). Conclusion: In order to reduce the data redundancy of features, it is recommended to select wavelets with large differences in wavelet orders when calculating radiomics wavelet features. It is necessary to remove high correlation features for improving the diagnostic performance of radiomics signature.
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Humans , Colorectal Neoplasms , Lymphatic Metastasis , Retrospective StudiesABSTRACT
Purpose Lymph-vascular invasion (LVI) is a risk factor for the prognosis of colorectal cancer, and it is of great value to evaluate the status of lymphatic vessels before treatment. This study aims to predict colorectal cancer LVI preoperatively based on radiomics. Materials and Methods Radiomics features were extracted from preoperative CT images of colorectal cancer retrospectively collected and radiomics labels were constructed. The predictive efficacy of radiomics labels were assessed and internally verified. Joint predictive factors were established by combining clinical factors with independent predictive efficacy and radiomics labels, and their predictive efficacy was evaluated. Results Radiomics labels consisted of 58 radiomics features were correlated with LVI status (P<0.0001)with the former showing good discrimination ability[C-index 0.719,95% CI:0.715-0.723]and classification ability(sensitivity 0.726, specificity 0.628) with internal validation (C-index 0.720). Joint predictive factors containing radiomics labels and carcino-embryonic antigen further enhanced the predictability of radiomics labels (C-index 0.751, sensitivity 0.788, specificity 0.667). Conclusion The radiomics labels built in this study can provide individualized prediction of LVI status of patients with colorectal cancer before surgery. Joint predictive factors in combination with clinical risk factors further improved predictive efficacy.
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Objective To develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer (NSCLC). Methods In this retrospective study, 657 patients with histologically confirmed was collected from October 2007 to December 2014.The primary dataset consisted of patients with histologically confirmed NSCLC from October 2007 to April 2012, while independent validation was conducted from May 2012 to December 2014.All the patients underwent non-enhanced and contrast-enhanced CT images scan with a standard protocol. The pathological stage (PTNM) of patients with NSCLC were determined by the intraoperative and postoperative pathological findings,and were divided into early stage(Ⅰ,Ⅱstage)and advanced stage(Ⅲ,Ⅳstage).A list of radiomics features were extracted using the software Matlab 2014a and the corresponding radiomics signature was constructed. Multivariable logistic regression analysis was performed with radiomics signature and clinical variables for developing the prediction model. The model performance was assessed with respect to discrimination using the area under the curve (AUC) of receiver operating characteristic(ROC) analysis. Results The discrimination performance of radiomics signature yielded a AUC of 0.715[95% confidence interval (CI):0.709 to 0.721] in the primary dataset and a AUC of 0.724(95% CI:0.717 to 0.731) in the validation dataset. On multivariable logistic regression, radiomics signature, tumor diameter, carcinoembryonic antigen (CEA) level, and cytokeratin 19 fragment (CYFRA21-1) level were showed independently associated with the stage ( Ⅰ,Ⅱ stage vs. Ⅲ, Ⅳ stage) of NSCLC. The prediction model showed good discrimination in both primary dataset (AUC=0.787, 95%CI:0.781 to 0.793;sensitivity=73.4%, specificity=72.2% ,positive predictive value=0.707,negative predictive value=0.868) and independent validation dataset (AUC=0.777, 95% CI:0.771 to 0.783,sensitivity=91.3% ,specificity=67.3% ,positive predictive value=0.607, negative predictive value=0.946). Conclusion The radiomics predictive model, which integrated with the radiomics signature and clinical characteristics can be used as a promising and applicable adjunct approach for preoperatively predicting the clinical stage (Ⅰ,Ⅱ stage vs. Ⅲ,Ⅳ stage) of patients with NSCLC.
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For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS). A primary dataset contained 80 patients (median PFS, 9.5 months) with positive EGFR mutations and a validation dataset contained 72 NSCLC (median PFS, 10.2 months) patients were used for prognosis trial. The experiment results indicated that the features: "Cluster Prominence of Gray Level Co-occurrence" (hazard ratio [HR]: 2.13, 95% confidence interval [CI]: (1.33, 3.40), P = 0.010) and "Short Run High Gray Level Emphasis of Run Length" (HR: 2.43, 95%CI: (1.46, 4.05), P = 0.005) were significantly associated with PFS in the primary dataset, and these two texture features also make a consistent performance on the validation cohort. Our study further supported that the quantitative measurement of tumor heterogeneity can be associated with prognosis of NSCLC patients with EGFR mutation.
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Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Mutation , Carcinoma, Non-Small-Cell Lung/drug therapy , Disease Progression , Disease-Free Survival , ErbB Receptors , Humans , Lung Neoplasms/drug therapy , Neoplasm Recurrence, Local/drug therapy , Protein Kinase Inhibitors/therapeutic useABSTRACT
Objective To investigate the effect of image registration on quantitative measurements of free breathing diffusion kurtosis imaging (DKI) in normal human kidney. Methods Twenty healthy volunteers were prospectively enrolled to undergo DKI imaging with a 3.0 T MR scanner. Three b values (0, 500, and 1 000 s/mm2) were adopted,with image registration performed after image acquisition. Acquired images were fitted using the DKI fitting model to generate the DKI metric maps,which were performed on both the pre-registration images and post-registration images. Image quality of the derived metric maps (before and after image registration,respectively) was assessed by two radiologists. Measurements of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (D|), axial diffusivity (D⊥), mean kurtosis (MK), radial kurtosis (K|) and axial kurtosis (K⊥) were conducted. The inter-observer reproducibility of the image quality assessment was analyzed using intra-class correlation coefficient(ICC). Wilcoxon signed-rank test was used to evaluate the difference in the subjective scores of the metric maps between those obtained before registration and those after registration. While paired t test or Wilcoxon signed-rank test was performed to analyze the difference in the quantitative measurements of DKI metrics of the renal cortex and medulla between those obtained before registration and those after registration.Results For the inter-observer reproducibility, satisfactory ICCs were obtained for the quantitative metric measurements (pre-registration:0.784 to 0.821;post-registration:0.836 to 0.934). Significant difference was notice between subjective scores for the quality of metric maps (P<0.05 for each comparison). In both the renal cortex and medulla, significant difference was noticed between each metric value obtained with pre-registration images and that with post-registration images (P<0.05 for each comparison). Conclusion Image registration can not only offer higher quality DKI metric maps,but also has effect on the quantitative measurements of obtained metric maps.