基于SEER数据库肺腺癌脑转移预测模型的构建及分析

目的 构建可视化预测肺腺癌（LUAD）脑转移风险概率的列线图模型,提高患者生存率。方法 研究纳入监测、流行病学和最终结果（SEER）数据库中58 928例LUAD患者,并按7∶3比例随机分为训练集和验证集。在训练集中采用Lasso回归与多因素Logistic回归分析筛选最有意义的预测变量,构建预测LUAD脑转移的列线图模型。采用受试者工作特征（ROC）曲线的曲线下面积（AUC）,Boostrap绘制校正曲线,Brier评分验证模型区分度及校准度,决策曲线分析（DCA）评价预测模型的临床效能。结果 最终筛选出7个独立影响因素构建列线图预测模型。训练集和验证集列线图预测LUAD患者发生脑转移概率的AUC分别为0.853(95%CI:0.849～0.858)和0.851(95%CI:0.844～0.857),校准曲线显示模型预测概率与实际观察概率具有较高的一致性,Brier评分均为0.092,DCA显示净收益率较高,模型临床效能较好。结论 本研究成功建立了预测LUAD脑转移的列线图模型,该模型能够准确区分脑转移高风险患者,可以有效指导临床医师制订个体化治疗方案。     

曲妥珠单抗在HER-2阳性乳腺癌患者新辅助治疗中的应用研究进展

曲妥珠单抗是人表皮生长因子受体-2（human epidermal growth factor receptor-2,HER-2）的特异性抑制剂,在HER-2阳性乳腺癌患者新辅助治疗中的应用日益广泛。大规模的随机、对照临床试验证实,新辅助化疗联合曲妥珠单抗与单纯化疗比较能显著提高病理完全缓解（pathologic complete response,pCR）率。在曲妥珠单抗联合化疗的基础上加用拉帕替尼较单用曲妥珠单抗可大大提高pCR率。蒽环与非蒽环类化疗药物均可作为曲妥珠单抗的联合用药,内分泌治疗也可作为雌激素受体阳性患者的联合用药。pCR是曲妥珠单抗新辅助治疗后生存获益的独立预后因素,HER-2转阴而未达到pCR的患者为不良预后因素。本文将对曲妥珠单抗在HER-2阳性乳腺癌患者新辅助治疗中的研究进展进行综述。      

影响年轻乳腺癌患者新辅助化疗后病理完全缓解与预后的因素分析

目的 探讨影响年轻乳腺癌患者新辅助化疗后病理完全缓解(pCR)与预后的因素。方法 回顾性分析行新辅助化疗的145例年轻乳腺癌患者临床资料,分析影响年轻乳腺癌患者新辅助化疗后pCR与预后的因素。结果 145例年轻乳腺癌患者新辅助化疗后pCR率为34.48%。ER阳性、PR阳性、临床N分期、Ki67阳性、化疗方案与pCR有关(P<0.05),HER-2、化疗周期、分子分型、临床T分期与pCR无关(P>0.05);多因素分析显示,Ki67阳性、临床N分期是影响年轻乳腺癌患者新辅助化疗后pCR的独立因素(P<0.05且OR>1)。145例年轻乳腺癌患者新辅助化疗后1年复发率37.93%。ER阳性、PR阳性、临床T分期、临床N分期、pCR与新辅助化疗后预后有关(P<0.05),HER-2、Ki67、化疗方案、化疗周期、分子分型与新辅助化疗后预后无关(P>0.05);多因素分析显示,临床T分期、pCR是影响年轻乳腺癌患者新辅助化疗后预后的独立因素(P<0.05且OR>1)。结论 Ki67阳性、临床N分期是影响年轻乳腺癌患者新辅助化疗后pCR的独立因素...     

乳腺癌新辅助化疗后同侧锁骨上淋巴结病理完全缓解列线图预测模型的构建

目的建立乳腺癌新辅助化疗后同侧锁骨上淋巴结病理完全缓解(ispCR)的预测模型, 以指导局部治疗。方法连续纳入2012年9月至2019年5月河南省肿瘤医院收治的首诊同侧锁骨上淋巴结转移且新辅助化疗后行同侧锁骨上淋巴结清扫的乳腺癌患者211例, 分为训练集142例, 验证集69例。采用单因素和多因素logistic回归分析确定乳腺癌新辅助化疗后ispCR的影响因素, 建立乳腺癌新辅助化疗后ispCR的列线图预测模型。通过受试者工作特征(ROC)曲线分析和绘制校准曲线对列线图预测模型进行内部和外部验证评价。结果单因素logistic回归分析显示, Ki-67指数、腋窝淋巴结转移数目、乳腺pCR、腋窝pCR、新辅助化疗后同侧锁骨上淋巴结大小与乳腺癌新辅助化疗后ispCR有关(均P<0.05)。多因素logistic回归分析显示, 腋窝淋巴结转移数目(OR=5.035, 95%CI为1.722～14.721)、乳腺pCR (OR=4.662, 95%CI为1.456～14.922)和新辅助化疗后同侧锁骨上淋巴结大小(OR=4.231, 95%CI为1.194～14.985)是乳腺癌新辅助...     

结合超声特征的腋窝淋巴结阳性乳腺癌新辅助化疗后腋窝病理完全缓解的预测模型

摘 要：［目的］ 分析经空芯针穿刺活检证实腋窝淋巴结阳性乳腺癌患者新辅助化疗（neoadjuvant chemotherapy,NAC）后腋窝病理完全缓解(pathological complete response,pCR)率及其影响因素,并整合超声影像特征与已知的临床病理特征建立预测模型,为新辅助化疗后乳腺癌患者腋窝处理的降级提供信息。［方法］ 回顾性分析哈尔滨医科大学附属肿瘤医院2017年1月至2018年12月入院接受NAC的481例乳腺癌患者的临床病理资料及超声影像特征,使用Logistic回归模型对临床病理特征及超声特征与NAC后腋窝淋巴结pCR的关系进行单因素及多因素分析,采用多因素分析中具有独立预测作用的指标构建新辅助化疗后腋窝pCR的预测列线图,并采用受试者工作特征（receiver operating characteristic,ROC）曲线及Bootstrapping法对此模型进行验证与校准。［结果］ 在481例患者中有147例(30.6%)实现了腋窝pCR。 单因素分析显示分子分型、乳腺原发灶临床疗效、淋巴结皮髓质分界是否清晰、彩色多谱勒血流图是否存在血流信号、淋巴结长径、淋巴结短径与腋窝pCR相关。多因素分析显示分子分型、乳腺原发灶临床疗效、CDFI血流信号、淋巴结短径是腋窝pCR的独立预测因素。与单独使用临床病理特征的预测模型相比,该模型具有良好的识别性能（ROC曲线下面积,0.784 vs 0.694,P<0.001）。［结论］ 结合超声特征的腋窝淋巴结阳性乳腺癌新辅助化疗后腋窝pCR的预测模型提高了仅应用临床病理特征的模型的预测能力,为NAC后选择合适的患者进行侵入性较小的腋窝手术方式提供了参考依据。     

体重指数与HER-2阳性乳腺癌新辅助化疗病理完全缓解的关系

目的:探讨体重指数(BMI)与HER-2阳性乳腺癌新辅助化疗病理完全缓解(pCR)的关系。方法:回顾性分析2013年1月1日至2014年12月31日间哈尔滨医科大学附属肿瘤医院196例接受了新辅助化疗并进行了手术的HER-2阳性乳腺癌患者的临床资料,分析BMI与不同临床病理特征的关系。使用Logistic回归模型进行单因素和多因素分析。结果:BMI各组之间年龄及pCR率差异具有统计学意义(分别为P=0.008,0.045);单因素分析显示:与cT1/cT2组相比,cT3/cT4组较难达到pCR(P=0.039);与N/U组相比,OW组pCR率更高(P=0.019);多因素分析显示:与N/U组及OB组相比,OW组pCR率更高(P=0.026)。结论:超重是HER-2阳性乳腺癌新辅助化疗患者pCR的独立预测因素。     

乳腺癌新辅助化疗后病理完全缓解的因素分析

目的:研究代谢综合征（metabolic syndrome,MS）与乳腺癌新辅助化疗（neoadjuvant chemotherapy,NAC）病理完全缓解（pathological complete response,pCR）的关系。方法:收集2014年01月至2020年06月在哈尔滨医科大学附属肿瘤医院接受NAC后进行手术的女性乳腺癌患者526例,并收集患者的临床病理资料,根据MS诊断标准分为MS组99例与非MS组427例。采用Logistic回归模型进行单因素和多因素分析MS与pCR的关系。结果:105例患者NAC后获得pCR,其中MS组10例,非MS组95例。单因素分析显示:非MS组较MS组更易获得pCR（P=0.008）,激素受体（hormone receptor,HR）阴性、人类表皮生长因子受体2（human epidermal growth factor receptor-2,HER-2）阳性、Ki-67＞14%者更易获得pCR（P＜0.001、P＜0.001、P=0.002）。多因素分析显示:与HR阴性者相比,HR阳性者较难获得pCR（P＜0.001）;与HER-2阴性者相比,HER-2阳性者pCR率更高（P=0.033）;与非MS患者相比,合并MS患者更难获得pCR（P=0.041）。亚组分析显示:非MS组中HR阴性患者更易获得pCR（P＜0.001）。结论:HR状态、HER-2状态及MS是乳腺癌NAC后pCR的独立预测因素,合并代谢综合征的乳腺癌患者接受新辅助化疗后更难获得病理完全缓解,与长期预后相关性有待进一步研究。     

术前新辅助短程密集化疗疗效与乳腺癌受体亚型及组织学类型的关系

目的评价不同组织学类型和受体亚型的乳腺癌患者对新辅助短程密集化疗疗效反应的差异。方法 对2004年1月至2006年12月期间在西南医院乳腺疾病中心接受新辅助短程密集化疗的223例可手术乳腺癌患者资料进行回顾性分析。根据术前粗针穿刺结果,将患者的肿瘤分为雌激素受体（ER）阳性[人表皮生长因子受体（HER-2）阴性]、三阴性以及HER-2阳性。新辅助短程密集化疗为4个周期,化疗方案均为TE（多西紫杉醇75mg/m2d1＋表柔比星75mmg/m2d1）,14d为1个周期。采用χ2检验分析乳腺癌组织学分类和受体亚型与病理学完全缓解率和化疗有效率的关系。结果 总的化疗有效率和病理完全缓解（pCR）率分别为59%（132/223）和9%（21/223）。浸润性导管癌和浸润性小叶癌的化疗有效率分别为70%（122/175）和24%（8/33）（P〈0.01）,pCR率分别为11%（20/175）和3%（1/33）。ER阳性、三阴性和HER-2阳性乳腺癌患者的化疗有效率分别为46%（57/123）、84%（43/51）和65%（32/49）（χ2=22.49,P=0.00）,pCR率分别为2%（3/123）、23%（12/51）和12%（6/49）（χ2=19.39,P=0.00）。结论 浸润性小叶癌患者从新辅助化疗中获益较小,新辅助化疗后ER阳性乳腺癌的pCR率很低     

系统免疫炎性反应指数对乳腺癌新辅助化疗病理完全缓解的预测作用及其与p53的关系

目的 探讨系统免疫炎性反应指数（SII）对乳腺癌新辅助化疗（NAC）病理完全缓解（pCR）的预测作用及其与p53的关系。方法 回顾性分析387例接受新辅助化疗及手术的女性乳腺癌患者临床病理资料。Logistic回归模型进行单因素和多因素分析。结果 72例（18.6%）患者接受新辅助化疗后获得了pCR,其中低SII组48例,高SII组24例;p53阴性组39例,阳性组33例。单因素分析显示：pCR与临床T分期、激素受体（HR）状态、人表皮生长因子受体2（HER2）、Ki67值、分子分型、p53及SII相关（均P<0.05）;多因素分析显示：临床T分期、Ki67值、分子分型、p53及SII是影响乳腺癌患者pCR的独立预测因素。p53阴性的低SII组患者pCR率高于其他组。结论 SII是乳腺癌新辅助化疗病理完全缓解的独立预测因素,具有简单方便及重复性高等特点,p53阴性的低SII组患者pCR率高。     

超声结合临床特征基于166例乳腺癌新辅助化疗后腋窝淋巴结评估中的预测价值分析

[目的]探讨超声结合临床特征在乳腺癌新辅助化疗（neoadjuvant chemotherapy,NAC）后腋窝淋巴结状态的预测价值。[方法]回顾性总结2020年6月至2021年10月浙江省肿瘤医院166例初诊乳腺癌且有腋窝淋巴结转移病例,使用Logistic回归模型对临床病理及超声特征与NAC后腋窝淋巴结病理完全缓解(pathological complete response,pCR)的关系进行单因素与多因素分析,建立预测模型,绘制受试者工作特征（receiver operating characteristic,ROC）曲线并得出曲线下面积（area under curve,AUC）。[结果]166例乳腺癌患者中,61例经NAC后实现腋窝淋巴结pCR,单因素与多因素分析发现NAC后腋窝淋巴结纵横比值>2、淋巴结皮质厚度≤3 mm,乳腺癌分子分型为三阴性型与HER2阳性型、乳腺癌原发灶临床疗效达完全缓解和腋窝淋巴结pCR显著相关(P<0.05)。与单独使用临床病理或超声特征的预测模型相比,超声结合临床病理特征预测模型具有良好的识别性能（AUC为0.88,灵敏度为78.7...     

前列腺穿刺对根治性前列腺切除术后标本ISUP升级预测列线图模型的建立及其应用

目的:构建一个从前列腺穿刺组织到根治性前列腺切除术（RP）后标本ISUP分级升高(ISUP grade upgrading,IGU)风险的预测列线图模型并进行内部验证。方法:对2019年05月至2020年05月我院泌尿外科收治的166例前列腺癌患者临床和病理学资料进行回顾性分析。采用单因素及多因素Logistic回归分析得到IGU的独立危险因素,后根据这些因素构建列线图预测模型。通过校准图进行模型校准,C-指数评估模型的预测能力,决策曲线分析用于检验临床效用,采用Bootstrap resampling对模型进行诊断效能内部验证。结果:该研究中ISUP升级组有47例（28.3%）患者,未升级组有119例（71.7%）患者。多因素logistic回归分析发现前列腺穿刺活检组织Gleason评分（P=0.001）、前列腺穿刺活检方法（P=0.03）和穿刺阳性针数（P=0.04）是IGU的独立危险因素。IGU列线图模型是基于上述独立因素而构建,模型的ROC曲线下面积为0.802,C-指数为0.798,校准图显示预测曲线与实际曲线有较好的相符度。列线图模型在内部验证中C-指数达到0.772。决策曲线分析表明,RP-ISUP升级风险的区间阈值为3%~67%。结论:该研究构建了一个准确性相对较高的列线图模型,有助于临床医生评估RP术后标本ISUP分级升高（特别是经直肠穿刺活检诊断的低风险前列腺癌）的风险。     

Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Combining Both Clinicopathological and Imaging Indicators

To construct a nomogram for early prediction of pathological complete response (pCR) in patients with breast cancer (BC) after neoadjuvant chemotherapy (NAC). A total of 257 patients with BC from the fourth hospital of Hebei Medical University were included in the study. The patients were divided into training (n = 128) and validation groups (n = 129). Variables were screened using univariate and multivariate logistic regression analyses, and the nomogram model was set up based on the training group. The training and validation groups were validated using the receiver operating characteristic (ROC) curves and calibration plots. The diagnostic value of the nomogram was evaluated using decision curve analysis (DCA). Indicators such as hormone receptor status, clinical TNM stage, and change rate in apparent diffusion coefficient of breast magnetic resonance imaging after two NAC cycles were used for nomogram construction. The calibration plots showed high consistency between nomogram-predicted and actual pCR probabilities in the training and validation groups. The areas under the curve of the ROC curve with discrimination ability were 0.942 and 0.921 in the training and validation groups, respectively. This showed an excellent discrimination ability of our nomogram for pCR prediction. Further, DCA showed favorable diagnostic value in our model. The nomogram may be instructive to clinicians for early prediction of pCR and helpful to adjust the treatment program on time in neoadjuvant management.     

基于CT影像组学的列线图模型预测肺部肿瘤立体定向放射治疗疗效

目的:基于治疗前CT图像筛选放射组学特征构建列线图模型预测早期非小细胞肺癌（early stage-non-small cell lung cancer,ES-NSCLC）和肺部寡转移癌的放疗疗效。方法:本研究纳入122例接受立体定向放射治疗（stereotactic body radiotherapy,SBRT）的ES-NSCLC和肺部寡转移癌的患者,随机分为训练集和验证集。使用最小绝对收缩和选择算子（least absolute shrinkage and selection operator,LASSO）和逻辑回归（logistic regression）筛选训练集中与放疗疗效相关的放射组学特征以建立列线图模型。用受试者工作特征曲线（receiver operating characteristic curves,ROC）下面积（area under the curve,AUC）、校准曲线和决策曲线（decision curve analysis,DCA）评价模型性能。结果:经筛选得出6个放射组学特征形成放射组学特征分数（radiomics score,Rad-score）以建立列线图模型。模型训练集的AUC值为0.808（95%CI:0.712~0.884,P<0.001）,验证集的AUC为0.741（95%CI:0.556~0.879,P=0.003）。Delong检测显示模型表现均衡（P=0.496）,校准曲线和DCA均显示了模型较好的预测性能和较高的临床价值。结论:我们基于治疗前CT图像开发并验证了用于预测肺部肿瘤SBRT治疗疗效的列线图模型,该模型具有较高的预测性能和临床实用性。     

Radiomic Nomogram for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer: Predictive Value of Staging Contrast-enhanced CT

IntroductionThe purpose of this study was to predict pathologic complete response (pCR) to neoadjuvant therapy in breast cancer using radiomics based on pretreatment staging contrast-enhanced computed tomography (CECT).Patients and MethodsA total of 215 patients were retrospectively analyzed. Based on the intratumoral and peritumoral regions of CECT images, radiomic features were extracted and selected, respectively, to develop an intratumoral signature and a peritumoral signature with logistic regression in a training dataset (138 patients from November 2015 to October 2017). We also developed a clinical model with the molecular characterization of the tumor. A radiomic nomogram was further constructed by incorporating the intratumoral and peritumoral signatures with molecular characterization. The performance of the nomogram was validated in terms of discrimination, calibration, and clinical utility in an independent validation dataset (77 patients from November 2017 to December 2018). Stratified analysis was performed to develop a subtype-specific radiomic signature for each subgroup.ResultsCompared with the clinical model (area under the curve [AUC], 0.756), the radiomic nomogram (AUC, 0.818) achieved better performance for pCR prediction in the validation dataset with continuous net reclassification improvement of 0.787 and good calibration. Decision curve analysis suggested the nomogram was clinically useful. Subtype-specific radiomic signatures showed improved AUCs (luminal subgroup, 0.936; human epidermal growth factor receptor 2-positive subgroup, 0.825; and triple negative subgroup, 0.858) for pCR prediction.ConclusionThis study has revealed a predictive value of pretreatment staging-CECT and successfully developed and validated a radiomic nomogram for individualized prediction of pCR to neoadjuvant therapy in breast cancer, which could assist clinical decision-making and improve patient outcome.     

Development and Validation of a Nomogram for Individually Predicting Pathologic Complete Remission After Preoperative Chemotherapy in Chinese Breast Cancer: A Population-Based Study

PurposeTo explore the independent predictors of pathologic complete remission response (pCR) for Chinese patients with breast cancer (BC) after preoperative chemotherapy and to develop an individualized nomogram for predicting the probability of pCR.Patients and MethodsThe clinicopathologic data of clinical stage I-III BC patients who received preoperative chemotherapy in Xijing Hospital were retrospectively analyzed. A total of 689 BC patients diagnosed in 2015-2017 were included in the training set to develop a nomogram. A separate cohort of 357 patients in the same center was regarded as a validation set for externally examining the performance of the model. The area under the receiver operating characteristic curve and calibration curve were used to verify the predictive performance of the nomogram.ResultsMultivariate logistic regression analysis showed that independent predictors of pCR were menopause status at diagnosis, family history of BC, initial tumor size, estrogen receptor status, HER2/neu (human epidermal growth factor receptor 2) status, and Ki-67 expression. On the basis of these factors, a nomogram was developed using R software. Our nomogram had good discrimination in the training and validation set (area under the receiver operating characteristic curve, 0.762 and 0.768, respectively). The calibration curves further confirmed that the model performs well.ConclusionMenopause status and family history of BC were independent predictors of pCR after preoperative chemotherapy for the first time. The nomogram can accurately predict pCR rate in BC, which may provide some guidelines for breast surgery options and patient counseling.     

早发型非转移性结直肠癌患者预后预测模型的构建及外部验证

目的:探讨影响早发型非转移性结直肠癌（early-onset non-metastatic colorectal cancer,EONCRC）患者预后的相关独立危险因素,并构建列线图预测EONCRC患者预后。方法:从美国监测、流行病学和结果数据库SEER数据库中收集了9 097例EONCRC患者的数据,患者按照7∶3比例随机分配到训练集(6 369例)和验证集(2 728例)。通过单变量、多变量COX比例风险回归分析确定独立的预后因素,并构建列线图。 使用C指数、ROC曲线和校准曲线评价列线图的区分度、预测效能和校准度。使用新疆军区总医院收治的EONCRC患者临床资料(n=171)对列线图进行了外部验证并对其预后影响因素进行了分析。结果:多因素分析确定了与总生存期有关的8个独立风险因素,分别是组织学分化程度、组织学类型、神经浸润、分期、T分期、手术、化疗和放疗,并将它们纳入列线图。SEER训练集、SEER验证集、外部验证集的C指数值分别为0.765(95%置信区间,0.749~0.781)、0.785(95%置信区间,0.763~0.807)、0.766(95%置信区间,0.713~0.819),校准曲线表明了列线图预测总生存率与实际总生存率具有良好的一致性。ROC曲线显示,列线图可以准确预测EONCRC患者1年(AUC=0.834 9)、3年(AUC=0.794 7)和5年(AUC=0.771 2)的生存率。根据列线图的风险评分将患者分为高风险、中风险和低风险组,在SEER训练集、SEER验证集、外部验证集中,低风险组的5年生存率均最高,其次是中风险组和高危组。结论:本研究确定了EONCRC患者预后相关的8个独立危险因素,列线图能准确预测中国及美国EONCRC患者1年、3年、5年总生存率,对EONCRC患者进行个体化的分层及预后评估,为临床的诊疗提供科学依据。     

A Model to Predict Pathologic Complete Response of Axillary Lymph Nodes to Neoadjuvant Chemo(Immuno)Therapy in Patients With Clinically Node-Positive Breast Cancer

BackgroundBetween 20% and 42% of patients with clinically node-positive breast cancer achieve a pathologic complete response (pCR) of axillary lymph nodes after neoadjuvant chemotherapy or immunotherapy, or both, (chemo[immuno]therapy). Hypothetically, axillary lymph node dissection (ALND) may be safely omitted in these patients. This study aimed to develop a model for predicting axillary pCR in these patients.Patients and MethodsWe retrospectively identified patients with clinically node-positive breast cancer who were treated with neoadjuvant chemo(immuno)therapy and ALND between 2005 and 2012 in 5 hospitals. Patient and tumor characteristics, neoadjuvant chemo(immuno)therapy regimens, and pathology reports were extracted. Binary logistic regression analysis was used to predict axillary pCR with the following variables: age, tumor stage and type, hormone receptor and human epidermal growth factor receptor 2 (HER2) status, and administration of taxane and trastuzumab. The model was internally validated by bootstrap resampling. The overall performance of the model was assessed by the Brier score and the discriminative performance by receiver operating characteristic (ROC) curve analysis.ResultsA model was developed based on 291 patients and was internally validated with a scaled Brier score of 0.14. The area under the ROC curve of this model was 0.77 (95% confidence interval [CI], 0.71-0.82). At a cutoff value of predicted probability ≥ 0.50, the model demonstrated specificity of 88%, sensitivity of 43%, positive predictive value (PPV) of 65%, and negative predictive value (NPV) of 75%.ConclusionThis prediction model shows reasonable accuracy for predicting axillary pCR. However, omitting axillary treatment based solely on the nomogram score is not justified. Further research is warranted to noninvasively identify patients with axillary pCR.