Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

Coupling radiomics analysis of CT image with diversification of tumor ecosystem: A new insight to overall survival in stage Ⅰ-Ⅲ colorectal cancer

Objective: This study aimed to establish a method to predict the overall survival(OS) of patients with stage Ⅰ-Ⅲ colorectal cancer(CRC) through coupling radiomics analysis of CT images with the measurement of tumor ecosystem diversification.Methods: We retrospectively identified 161 consecutive patients with stage Ⅰ-Ⅲ CRC who had underwent radical resection as a training cohort. A total of 248 patients were recruited for temporary independent validation as external validation cohort 1, with 103 patients from an external institute as the external validation cohort 2. CT image features to describe tumor spatial heterogeneity leveraging the measurement of diversification of tumor ecosystem, were extracted to build a marker, termed the EcoRad signature. Multivariate Cox regression was used to assess the EcoRad signature, with a prediction model constructed to demonstrate its incremental value to the traditional staging system for OS prediction.Results: The EcoRad signature was significantly associated with OS in the training cohort [hazard ratio(HR)=6.670;95% confidence interval(95% CI): 3.433-12.956;P<0.001), external validation cohort 1(HR=2.866;95% CI: 1.646-4.990;P<0.001) and external validation cohort 2(HR=3.342;95% CI: 1.289-8.663;P=0.002).Incorporating the EcoRad signature into the prediction model presented a higher prediction ability(P<0.001) with respect to the C-index(0.813, 95% CI: 0.804-0.822 in the training cohort;0.758, 95% CI: 0.751-0.765 in the external validation cohort 1;and 0.746, 95% CI: 0.722-0.770 in external validation cohort 2), compared with the reference model that only incorporated tumor, node, metastasis(TNM) system, as well as a better calibration,improved reclassification and superior clinical usefulness.Conclusions: This study establishes a method to measure the spatial heterogeneity of CRC through coupling radiomics analysis with measurement of diversification of the tumor ecosystem, and suggests that this approach could effectively predict OS and could be used as a supplement for risk stratification among stage Ⅰ-Ⅲ CRC patients.

自动量化的肿瘤-间质比预测胃癌新辅助化疗疗效

目的:探讨通过深度学习的方法来全自动定量评估术前活检标本的肿瘤-间质比(tumor-stroma ratio,TSR)是否可以预测胃癌患者新辅助化疗(neoadjuvant chemotherapy,NAC)疗效。方法:选取2013年3月至2020年3月在云南省肿瘤医院接受NAC治疗的胃癌患者的术前活检切片148张和手术切除切片43张。构建肿瘤区域分割模型和上皮-间质分割模型,使用手术切除切片训练和评估模型,在活检切片上预测,取二者预测结果的交集,根据TSR的定义得到TSR值。根据术后病理学肿瘤退缩分级(tumor regression grade,TRG)将所有患者分为反应良好者(TRG 0~1)和反应不良者(TRG 2~3)。采用单因素和多因素回归分析TSR与胃癌新辅助化疗疗效的相关性。结果:肿瘤组织分割模型的IOU(intersection over union)为0.94,上皮-间质分割模型的IOU为0.88。以44.93%和70.22%作为TSR的临界值,将患者分为低、中、高间质比组,三组之间反应良好者比例具有显著性差异(P<0.05)。多因素分析显示,TSR是治疗前对胃癌NAC反应的独立预测因子(OR=0.10,95%CI:0.03~0.32)。使用常规临床信息预测治疗响应的基础上,加入TSR三分类等级作为治疗响应的预测变量时,曲线下面积(area under curve,AUC)可从0.71提升至0.85。结论:该模型能够在病理切片上自动分割肿瘤区域、上皮区域和间质区域,并能够自动、准确的计算出TSR,同时发现基于此方法自动计算的TSR可以预测NAC疗效。

Artificial intelligence-based analysis of tumor-infiltrating lymphocyte spatial distribution for colorectal cancer prognosis

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.

Computed tomography-based radiomics for prediction of neoadjuvant chemotherapy outcomes in locally advanced gastric cancer: A pilot study

Objective： The standard treatment for patients with locally advanced gastric cancer has relied on perioperativeradio-chemotherapy or chemotherapy and surgery. The aim of this study was to investigate the wealth of radiomicsfor pre-treatment computed tomography （CT） in the prediction of the pathological response of locally advancedgastric cancer with preoperative chemotherapy.Methods： Thirty consecutive patients with CT-staged Ⅱ/Ⅲ gastric cancer receiving neoadjuvant chemotherapywere enrolled in this study between December 2014 and March 2017. All patients underwent upper abdominal CTduring the unenhanced, late arterial phase （AP） and portal venous phase （PP） before the administration ofneoadjuvant chemotherapy. In total, 19,985 radiomics features were extracted in the AP and PP for each patient.Four methods were adopted during feature selection and eight methods were used in the process of building theclassifier model. Thirty-two combinations of feature selection and classification methods were examined. Receiveroperating characteristic （ROC） curves were used to evaluate the capability of each combination of feature selectionand classification method to predict a non-good response （non-GR） based on tumor regression grade （TRG）.Results： The mean area under the curve （AUC） ranged from 0.194 to 0.621 in the AP, and from 0.455 to 0.722in the PP, according to different combinations of feature selection and the classification methods. There was onlyone cross-combination machine-learning method indicating a relatively higher AUC （〉0.600） in the AP, while 12cross-combination machine-learning methods presented relatively higher AUCs （all 〉0.600） in the PP. The featureselection method adopted by a filter based on linear discriminant analysis ＋ classifier of random forest achieved asignificantly prognostic performance in the PP （AUC, 0.722~0.108; accuracy, 0.793; sensitivity, 0.636; specificity,0.889; Z=2.039; P=0.041）.Conclusions： It is possible to predict non-GR after neoadiuvant chemotherapy in locally advanced gastriccancers based on the radiomics of CT.

A radiomics prognostic scoring system for predicting progression-free survival in patients with stageⅣnon-small cell lung cancer treated with platinum-based chemotherapy

Objective:To develop and validate a radiomics prognostic scoring system(RPSS)for prediction of progressionfree survival(PFS)in patients with stageⅣnon-small cell lung cancer(NSCLC)treated with platinum-based chemotherapy.Methods:In this retrospective study,four independent cohorts of stageⅣNSCLC patients treated with platinum-based chemotherapy were included for model construction and validation(Discovery:n=159;Internal validation:n=156;External validation:n=81,Mutation validation:n=64).First,a total of 1,182 three-dimensional radiomics features were extracted from pre-treatment computed tomography(CT)images of each patient.Then,a radiomics signature was constructed using the least absolute shrinkage and selection operator method(LASSO)penalized Cox regression analysis.Finally,an individualized prognostic scoring system incorporating radiomics signature and clinicopathologic risk factors was proposed for PFS prediction.Results:The established radiomics signature consisting of 16 features showed good discrimination for classifying patients with high-risk and low-risk progression to chemotherapy in all cohorts(All P<0.05).On the multivariable analysis,independent factors for PFS were radiomics signature,performance status(PS),and N stage,which were all selected into construction of RPSS.The RPSS showed significant prognostic performance for predicting PFS in discovery[C-index:0.772,95%confidence interval(95%CI):0.765-0.779],internal validation(C-index:0.738,95%CI:0.730-0.746),external validation(C-index:0.750,95%CI:0.734-0.765),and mutation validation(Cindex:0.739,95%CI:0.720-0.758).Decision curve analysis revealed that RPSS significantly outperformed the clinicopathologic-based model in terms of clinical usefulness(All P<0.05).Conclusions:This study established a radiomics prognostic scoring system as RPSS that can be conveniently used to achieve individualized prediction of PFS probability for stageⅣNSCLC patients treated with platinumbased chemotherapy,which holds promise for guiding personalized pre-therapy of stageⅣNSCLC.

Prognostic value of a modified Immunoscore in patients with stage Ⅰ-Ⅲ resectable colon cancer

Objective: The Immunoscore method has proved fruitful for predicting prognosis in patients with colon cancer.However, there is still room for improvement in this scoring method to achieve further advances in its clinical translation. This study aimed to develop and validate a modified Immunoscore(IS-mod) system for predicting overall survival(OS) in patients with stage Ⅰ-Ⅲ colon cancer.Methods: The IS-mod was proposed by counting CD3+ and CD8+ immune cells in regions of the tumor core and its invasive margin by drawing two lines of interest. A discovery cohort(N=212) and validation cohort(N=103)from two centers were used to evaluate the prognostic value of the IS-mod.Results: In the discovery cohort, 5-year survival rates were 88.6% in the high IS-mod group and 60.7% in the low IS-mod group. Multivariate analysis confirmed that the IS-mod was an independent prognostic factor for OS[adjusted hazard ratio(HR)=0.36, 95% confidence interval(95% CI): 0.20-0.63]. With less annotation and computation cost, the IS-mod achieved performance comparable to that of the Immunoscore-like(IS-like) system(C-index, 0.676 vs. 0.661, P=0.231). The 2-category IS-mod using 47.5% as the threshold had a better prognostic value than that using a fixed threshold of 25%(C-index, 0.653 vs. 0.573, P=0.004). Similar results were confirmed in the validation cohort.Conclusions: Our method simplifies the annotation and accelerates the calculation of Immunoscore method,thus making it easier for clinical implementation. The IS-mod achieved comparable prognostic performance when compared to the IS-like system in both cohorts. Besides, we further found that even with a small reference set(N≥120), the IS-mod still demonstrated a stable prognostic value. This finding may inspire other institutions to develop a local reference set of an IS-mod system for more accurate risk stratification of colon cancer.

Deep learning quantified mucus-tumor ratio predicting survival of patients with colorectal cancer using whole-slide images

Background:In colorectal cancer(CRC),mucinous adenocarcinoma differs from other adenocarcinomas in gene-phenotype,morphology,and prognosis.However,mucinous components are present in a large number of adenocarcinomas,and the prognostic value of mucus proportion has not been investigated.Artificial intelligence provides a way to quantify mucus proportion on whole-slide images(WSIs)accurately.We aimed to quantify mucus proportion by deep learning and further investigate its prognostic value in two CRC patient cohorts.Methods:Deep learning was used to segment WSIs stained with hematoxylin and eosin.Mucus-tumor ratio(MTR)was defined as the proportion of mucinous component in the tumor area.A training cohort(N=419)and a validation cohort(N=315)were used to evaluate the prognostic value of MTR.Survival analysis was performed using the Cox proportional hazard model.Result:Patients were stratified tomucus-low andmucus-high groups,with 24.1%as the threshold.In the training cohort,patients with mucus-high had unfavorable outcomes(hazard ratio for high vs.low 1.88,95%confidence interval 1.18–2.99,P=0.008),with 5-year overall survival rates of 54.8%and 73.7%in mucus-high and mucus-lowgroups,respectively.The resultswere confirmed in the validation cohort(2.09,1.21–3.60,0.008;62.8%vs.79.8%).The prognostic value of MTR was maintained in multivariate analysis for both cohorts.Conclusion:The deep learning quantified MTR was an independent prognostic factor in CRC.With the advantages of advanced efficiency and high consistency,our method is suitable for clinical application and promotes precision medicine development.

CA242围手术期变化模式及纵向轨迹与结直肠癌预后的关联:一项回顾性纵向队列研究

以往关于CA242的研究大多局限于术前水平,忽略了其围手术期变化模式和纵向轨迹.基于一项结直肠癌(CRC)患者的回顾性队列,本研究根据术前和术后CA242水平确定了围手术期CA242变化模式,利用随访期间CA242的重复测量识别了CA242纵向轨迹,并评估了其与CRC预后的关系.结果显示,围手术期CA242变化模式是CRC的独立预后因素.术后CA242持续升高患者的无复发生存率(RFS)低于术后CA242恢复正常的患者,且术后/术前CA242比值可进一步对这两组患者进行复发风险分层.轨迹分析发现,术后3年内CA242存在低稳定、早期上升和晚期上升三种纵向变化模式.纵向轨迹与RFS显著相关,且独立于术前和术后CA242.在CEA正常的患者中,CA242的围手术期变化模式和纵向轨迹与RFS之间仍然存在显著关联,表明在CEA基础上测量CA242有助于识别预后较差的高危CRC患者.基于本研究结果,建议对CRC患者进行CA242的常规术后随访.