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Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning 增强乳腺癌中HER2检测:通过深度学习预测免疫组织化学图像的荧光原位杂交(FISH)评分
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-03-06 DOI: 10.1002/2056-4538.70024
Daniel O Macaulay, Wenchao Han, Mark D Zarella, Chris A Garcia, Thomas E Tavolara

Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy, particularly with novel antibody-drug conjugates (ADCs) like trastuzumab deruxtecan, which shows promise for breast cancers with low HER2 expression. Current HER2 testing methods, including immunohistochemistry (IHC) and in situ hybridization (ISH), have limitations. IHC, a semi-quantitative assay, is prone to interobserver variability. While ISH provides higher precision than IHC, it remains more resource-intensive in terms of cost and workflow. However, turnaround time is typically faster than that of other advanced molecular methods such as next-generation sequencing. We adapted the clustering-constrained-attention multiple-instance deep learning model to improve IHC testing and reduce dependence on reflex fluorescence ISH (FISH) tests. Using 5,731 HER2 IHC images, including 592 cases with FISH testing, we trained two models: one for predicting HER2 scores from IHC images and another for predicting FISH scores from equivocal cases. The HER2 IHC score prediction model achieved 91% ± 0.01 overall accuracy and a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 ± 0.01. The FISH score prediction model had an ROC AUC of 0.84 ± 0.07, with sensitivity at 0.37 ± 0.13 and specificity at 0.96 ± 0.03. External validation on cases from 203 institutions showed similar performance. The HER2 IHC model maintained a 91% ± 0.01 accuracy and an ROC AUC of 0.98 ± 0.01, while the FISH model had an ROC AUC of 0.75 ± 0.03, with sensitivity at 0.28 ± 0.04 and specificity at 0.93 ± 0.01. Our model advances HER2 scoring by reducing subjectivity and variability in current scoring methods. Despite lower accuracy and sensitivity in the FISH prediction model, it may be a beneficial option for settings where reflex FISH testing is unavailable or prohibitive. With high specificity, our model can serve as an effective screening tool, enhancing breast cancer diagnosis and treatment selection.

乳腺癌影响着全球数百万人,为了有效治疗,需要进行精确的生物标志物检测。HER2检测对于指导治疗至关重要,特别是对于新型抗体-药物偶联物(adc),如曲妥珠单抗德鲁西替康(trastuzumab deruxtecan),它有望治疗HER2低表达的乳腺癌。目前的HER2检测方法,包括免疫组织化学(IHC)和原位杂交(ISH),都有局限性。免疫组化是一种半定量分析,容易在观察者之间发生变化。虽然ISH提供比IHC更高的精度,但在成本和工作流程方面,它仍然是资源密集型的。然而,周转时间通常比其他先进的分子方法(如下一代测序)要快。我们采用了聚类约束注意多实例深度学习模型来改进IHC测试,并减少对反射荧光ISH (FISH)测试的依赖。使用5,731张HER2 IHC图像,包括592例FISH检测病例,我们训练了两个模型:一个用于从IHC图像预测HER2评分,另一个用于从模棱两可的病例预测FISH评分。HER2 IHC评分预测模型总体准确率为91%±0.01,受试者工作特征曲线下面积(ROC)为0.98±0.01。FISH评分预测模型的ROC AUC为0.84±0.07,灵敏度为0.37±0.13,特异性为0.96±0.03。对203个机构的案例进行了外部验证,结果也大致相同。HER2 IHC模型的准确率为91%±0.01,ROC AUC为0.98±0.01;FISH模型的ROC AUC为0.75±0.03,灵敏度为0.28±0.04,特异性为0.93±0.01。我们的模型通过减少当前评分方法中的主观性和可变性来推进HER2评分。尽管FISH预测模型的准确性和灵敏度较低,但它可能是反射性FISH测试不可用或禁止的设置的有益选择。该模型具有较高的特异性,可作为有效的筛查工具,增强乳腺癌的诊断和治疗选择。
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引用次数: 0
Association between individual Warburg-related proteins and prognosis in colorectal cancer 结直肠癌个体warburg相关蛋白与预后的关系
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-02-27 DOI: 10.1002/2056-4538.70016
Kelly Offermans, Josien CA Jenniskens, Colinda CJM Simons, Iryna Samarska, Gregorio E Fazzi, Kim M Smits, Leo J Schouten, Matty P Weijenberg, Heike I Grabsch, Piet A van den Brandt

We previously showed that Warburg subtyping (low/moderate/high), based on the expression of six glycolytic proteins and transcriptional regulators [glucose transporter 1 (GLUT1), pyruvate kinase M2 (PKM2), lactate dehydrogenase A (LDHA), monocarboxylate transporter 4 (MCT4), p53, and PTEN], holds independent prognostic value in colorectal cancer (CRC) patients. The present study aimed to investigate whether the expression level of one of the proteins (GLUT1, PKM2, LDHA, MCT4, p53, and PTEN) can act as a proxy for our previously identified six protein-based Warburg subtypes. Protein expression levels for individual Warburg-related proteins were available for 2,251 CRC patients from the Netherlands Cohort Study. Kaplan–Meier curves and Cox regression were used to explore associations between individual Warburg-related proteins and CRC-specific and overall survival. Previously identified associations between Warburg subtypes and CRC-specific and overall survival were adjusted for individual proteins, showing a significant association with survival in the current study. Multivariable-adjusted analyses showed that the expression of GLUT1, LDHA, MCT4, PKM2, or p53 was associated with neither CRC-specific nor overall survival. Decreasing PTEN expression was associated with significantly poorer overall survival (p-trendcategories = 0.026). Additional adjustment for PTEN expression had minimal impact on the previously identified association between Warburg subtypes and survival, and the six protein-based Warburg-high subtype remained a statistically significant predictor of overall survival (hazard ratio 1.15; 95% CI 1.01–1.32). In conclusion, our results emphasise that individual Warburg-related proteins cannot serve as a proxy or surrogate marker for Warburg subtyping, thereby highlighting the importance of combining the expression levels of multiple Warburg-related proteins when examining the prognostic significance of a complex biological pathway such as the Warburg effect.

我们之前发现,基于六种糖酵解蛋白和转录调节因子[葡萄糖转运蛋白1 (GLUT1)、丙酮酸激酶M2 (PKM2)、乳酸脱氢酶A (LDHA)、单羧酸转运蛋白4 (MCT4)、p53和PTEN]表达的Warburg亚型(低/中/高)在结直肠癌(CRC)患者中具有独立的预后价值。本研究旨在探讨其中一种蛋白(GLUT1, PKM2, LDHA, MCT4, p53和PTEN)的表达水平是否可以作为我们之前确定的六种基于蛋白的Warburg亚型的代理。来自荷兰队列研究的2251例结直肠癌患者可获得个体warburg相关蛋白的蛋白表达水平。Kaplan-Meier曲线和Cox回归用于探讨个体warburg相关蛋白与crc特异性和总生存率之间的关系。先前确定的Warburg亚型与crc特异性和总生存率之间的关联根据个体蛋白进行了调整,在当前的研究中显示出与生存率的显著关联。多变量调整分析显示,GLUT1、LDHA、MCT4、PKM2或p53的表达与crc特异性生存和总生存均无关。PTEN表达降低与总生存率显著降低相关(p-trendcategories = 0.026)。PTEN表达的额外调整对先前确定的Warburg亚型与生存之间的关联影响最小,并且基于6蛋白的Warburg-high亚型仍然是总生存的统计学显著预测因子(风险比1.15;95% ci 1.01-1.32)。总之,我们的研究结果强调单个Warburg相关蛋白不能作为Warburg亚型的代理或替代标记物,从而强调了在检查复杂生物学途径(如Warburg效应)的预后意义时,结合多种Warburg相关蛋白的表达水平的重要性。
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引用次数: 0
Comprehensive transcriptomic profiling reveals molecular characteristics and biomarkers associated with risk stratification in papillary thyroid carcinoma 综合转录组学分析揭示了与甲状腺乳头状癌风险分层相关的分子特征和生物标志物
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-02-25 DOI: 10.1002/2056-4538.70022
Congcong Yan, Chen Zheng, Jiaxing Luo, Xue Wu, Xinyu Meng, Chaoyue Lv, Shurong Shen, Meng Zhou, Ouchen Wang

Papillary thyroid carcinoma (PTC) is one of the most common endocrine malignancies, with varying levels of risk and clinical behavior. A better understanding of the molecular characteristics could improve molecular diagnosis and risk assessment. In this study, we performed whole transcriptomic sequencing on 113 PTC cases, including 70 high-risk and 43 low-risk Chinese patients. Comparative transcriptional profiling analysis revealed two functionally distinct patterns of gene dysregulation between the risk subtypes. Low-risk PTCs showed significant upregulation of immune-related genes and increased immune cell infiltration, whereas high-risk PTCs presented extensive alterations in gene expression and activation of oncogenic signaling pathways. Additionally, we developed a 31-gene transcriptomic signature (PTCrisk) for differentiating high-risk from low-risk PTCs, which was validated across both in-house and external multicenter cohorts. PTCrisk scores were positively correlated with key clinicopathological features, including tumor size, lymph node metastasis, TNM stage, and BRAF mutation status. Overall, our study provides further molecular insights into PTC risk stratification and may contribute to the development of personalized therapeutic strategies for PTC patients.

甲状腺乳头状癌(PTC)是最常见的内分泌恶性肿瘤之一,具有不同程度的风险和临床行为。更好地了解分子特征可以提高分子诊断和风险评估。在本研究中,我们对113例PTC患者进行了全转录组测序,其中70例为高危患者,43例为低危患者。比较转录谱分析揭示了风险亚型之间基因失调的两种功能不同的模式。低风险PTCs表现出免疫相关基因的显著上调和免疫细胞浸润的增加,而高风险PTCs在基因表达和致癌信号通路的激活方面表现出广泛的改变。此外,我们开发了一种31个基因的转录组特征(PTCrisk),用于区分高风险和低风险的ptc,并在内部和外部多中心队列中进行了验证。PTCrisk评分与肿瘤大小、淋巴结转移、TNM分期、BRAF突变状态等关键临床病理特征呈正相关。总的来说,我们的研究为PTC风险分层提供了进一步的分子见解,并可能有助于PTC患者个性化治疗策略的发展。
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引用次数: 0
Subtype-associated complexity and prognostic significance of the NLRP3 inflammasome landscape in pancreatic neoplasms 胰腺肿瘤NLRP3炎性小体的亚型相关复杂性及预后意义
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-02-19 DOI: 10.1002/2056-4538.70019
Kristóf Németh, Eszter Mezei, Justína Vörös, Katalin Borka, Adrián Pesti, István Kenessey, András Kiss, András Budai

Intraductal papillary mucinous neoplasm (IPMN) can progress into malignant pancreatic cancer, posing challenges in accurately assessing the risk of malignancy. While the nucleotide-binding oligomerization domain (NOD)-like receptor pyrin domain containing 3 (NLRP3) inflammasome pathway's role in pancreatic ductal adenocarcinoma (PDAC) has been extensively studied, its implications in IPMN remain unexplored. This study aimed to investigate the prognostic significance of NLRP3 inflammasome-related proteins across IPMN subtypes and their associations with tumor characteristics, with a secondary focus on comparing expression patterns in IPMN and PDAC. A cohort of 187 patients (100 IPMN and 87 PDAC) underwent high-dimensional histopathological imaging using the multiplexed immunohistochemical consecutive staining on single slide method and a semi-automated image analysis workflow. Expression levels of NLRP3, apoptosis-associated speck-like protein containing a caspase-recruitment domain (ASC), caspase-1, interleukin-1 beta, interleukin-18 (IL-18), interleukin-1 receptor antagonist, and interleukin-18 binding protein (IL-18BP) were evaluated and compared between IPMN and PDAC samples. The relationships between protein expression and tumor characteristics were examined. Principal component analysis distinguished between intestinal and nonintestinal clusters based on NLRP3-associated proteins. Lower IL-18 expression was linked to the intestinal subtype, while higher caspase-1 was linked to the pancreatobiliary subtype. Elevated caspase-1 and ASC expression were associated with invasiveness in IPMN. No significant correlation was found between the examined proteins and later-stage tumor characteristics in invasive cases. The IL-18/IL-18BP ratio was an independent prognostic factor in invasive IPMN. Our findings highlight the prognostic significance of IL-18 and the IL-18/IL-18BP ratio in invasive IPMNs. These results point to a complex regulation of NLRP3 inflammasome proteins, especially effector cytokines, in pancreatic neoplasms, which are strongly linked to subtype and prognosis.

导管内乳头状粘液瘤(IPMN)可发展为恶性胰腺癌,这对准确评估恶性肿瘤的风险提出了挑战。虽然含有3 (NLRP3)炎症体途径的核苷酸结合寡聚化结构域(NOD)样受体pyrin结构域在胰腺导管腺癌(PDAC)中的作用已被广泛研究,但其在IPMN中的意义仍未被探索。本研究旨在探讨NLRP3炎症小体相关蛋白在IPMN亚型中的预后意义及其与肿瘤特征的相关性,并比较IPMN和PDAC中的表达模式。187例患者(100例IPMN和87例PDAC)采用单片多路免疫组织化学连续染色法和半自动图像分析工作流程进行高维组织病理学成像。评估并比较IPMN和PDAC样品中NLRP3、含有caspase-募集结构域的凋亡相关斑点样蛋白(ASC)、caspase-1、白介素-1 β、白介素-18 (IL-18)、白介素-1受体拮抗剂和白介素-18结合蛋白(IL-18BP)的表达水平。研究蛋白表达与肿瘤特征的关系。主成分分析基于nlrp3相关蛋白区分肠道和非肠道簇。较低的IL-18表达与肠道亚型有关,而较高的caspase-1表达与胰胆道亚型有关。caspase-1和ASC表达升高与IPMN的侵袭性有关。在浸润性病例中,检测的蛋白与晚期肿瘤特征之间没有明显的相关性。IL-18/IL-18BP比值是侵袭性IPMN的独立预后因素。我们的研究结果强调了IL-18和IL-18/IL-18BP比值在侵袭性IPMNs中的预后意义。这些结果表明NLRP3炎性小体蛋白,特别是效应细胞因子在胰腺肿瘤中的复杂调控与亚型和预后密切相关。
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引用次数: 0
Sarcomatoid areas of urothelial carcinoma are enriched for CD163-positive antigen-presenting cells 尿路上皮癌肉瘤样区富含cd163阳性抗原呈递细胞
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-02-19 DOI: 10.1002/2056-4538.70021
Burles A Johnson III, Vamsi Parimi, Sonia Kamanda, David C Corney, Woonyoung Choi, Jean Hoffman-Censits, Max Kates, David J McConkey, Noah M Hahn, Andres Matoso

Sarcomatoid urothelial carcinoma (SUC) is a rare histologic subtype with poor prognosis. While there is known intra-tumoral heterogeneity between individual SUC tumors, the relationship between sarcomatoid and conventional urothelial carcinoma (CUC) within the same patient is poorly understood. The objective of this study was to identify differences between the sarcomatoid and CUC tumor microenvironment components that may drive this aggressive phenotype. Using tissue microarrays from eight patient tumors with mixed CUC and SUC, we examined paired CUC, mixed urothelial carcinoma (UC) regions, and SUC using the Nanostring Digital Spatial Profiling platform. We found SUC and mixed UC had higher levels of stromal cells, predominately macrophages and fibroblasts, when compared with CUC within the same tumor. CD14, CD163, and transforming growth factor-beta levels were significantly higher in SUC than in CUC. Immunohistochemical analysis revealed consistently moderate to strong expression of CD163-positive antigen-presenting cells (APCs) in SUC regions, whereas CD68-positive APC expression was generally absent. Thus, in mixed histology SUC, the SUC component preferentially expressed CD163-positive APCs and fibroblasts compared to the CUC component. As CD163-positive APCs and fibroblasts are known to be tumor-promoting and immune-suppressive, this infiltration may contribute to epithelial to mesenchymal transition and other aggressive properties of SUC.

摘要肉瘤样尿路上皮癌是一种少见的组织学亚型,预后较差。虽然已知单个SUC肿瘤之间存在肿瘤内异质性,但同一患者中肉瘤样癌和常规尿路上皮癌(CUC)之间的关系尚不清楚。本研究的目的是确定可能驱动这种侵袭性表型的类肉瘤和CUC肿瘤微环境成分之间的差异。利用来自8例混合CUC和SUC患者的组织微阵列,我们使用纳米串数字空间分析平台检测了配对CUC、混合尿路上皮癌(UC)区域和SUC。我们发现,与同一肿瘤内的CUC相比,SUC和混合UC具有更高水平的间质细胞,主要是巨噬细胞和成纤维细胞。CD14、CD163和转化生长因子- β水平在SUC中显著高于CUC。免疫组织化学分析显示,在SUC区域中,cd163阳性抗原呈递细胞(APC)的表达一致中至强,而cd68阳性APC的表达普遍缺失。因此,在混合组织学的SUC中,与CUC组分相比,SUC组分优先表达cd163阳性apc和成纤维细胞。由于已知cd163阳性的apc和成纤维细胞具有促肿瘤和免疫抑制作用,这种浸润可能有助于上皮细胞向间质细胞的转化和SUC的其他侵袭性特性。
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引用次数: 0
Platinum drugs upregulate CXCR4 and PD-L1 expression via ROS-dependent pathways, with implications for novel combined treatment in gastric cancer 铂类药物通过ros依赖途径上调CXCR4和PD-L1的表达,对胃癌的新型联合治疗具有重要意义。
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-01-27 DOI: 10.1002/2056-4538.70015
Xiaoyu Kang, Lin Zhang, Shushang Liu, Fei Wang, Haiming Liu, Fenli Zhou, Fei Wu, Haohao Zhang, Daiming Fan, Yongzhan Nie, Zhangqian Chen

CXC chemokine receptor 4 (CXCR4) and programmed cell death-ligand 1 (PD-L1) are two critical molecules involved in the tumor immune microenvironment. However, the impact of platinum drugs, such as cisplatin, on CXCR4 or PD-L1 expression and the underlying mechanisms in gastric cancer (GC) remain unknown. Moreover, the correlation between their expression levels in GC remains elusive. Immunohistochemistry, western blot, and RT-qPCR were performed to determine the expression pattern of CXCR4 and PD-L1 in GC. Clinical samples, patient-derived xenografts, and cell-derived xenografts were utilized to investigate the effects of platinum drugs on the expression levels of CXCR4 and PD-L1. Postchemotherapy resected GC tumor tissues showed higher CXCR4 and PD-L1 expression levels than pretreatment biopsies (p < 0.05). Similarly, GC xenografts treated with platinum-based chemotherapy exhibited increased CXCR4 and PD-L1 expression levels compared to saline-treated controls (p < 0.05). A positive correlation was detected between the expression levels of CXCR4 and PD-L1 in GC tumor tissues. Increased levels of CXCR4 and PD-L1 expression, in a dose- and time-dependent manner upon cisplatin treatment, were observed in GC cells (p < 0.05). Cisplatin-induced CXCR4 upregulation relies on ROS/HIF-1α and ROS/NF-κB pathways, while cisplatin-induced PD-L1 upregulation is cyclic GMP-AMP synthase/stimulator of IFN genes-dependent and associated with elevated ROS levels in GC cells. CXCR4 expression was found to be positively correlated with PD-L1 expression in GC. Platinum drugs upregulated the levels of CXCR4 and PD-L1 expression in GC. A combined strategy targeting CXCR-4 and PD-L1 might have clinical prospects for GC patients.

CXC趋化因子受体4 (CXCR4)和程序性细胞死亡配体1 (PD-L1)是参与肿瘤免疫微环境的两个关键分子。然而,铂类药物(如顺铂)对胃癌(GC)中CXCR4或PD-L1表达的影响及其潜在机制尚不清楚。此外,它们在GC中的表达水平之间的相关性尚不明确。采用免疫组化、western blot、RT-qPCR检测CXCR4和PD-L1在GC中的表达模式。我们利用临床样本、患者来源的异种移植物和细胞来源的异种移植物来研究铂类药物对CXCR4和PD-L1表达水平的影响。化疗后切除的胃癌组织显示CXCR4和PD-L1表达水平高于预处理活检(p
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引用次数: 0
Authors' reply: Re: Koga et al. Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models 作者回复:Re: Koga et al。检索增强生成与基于文档的生成:大型语言模型中的关键区别。
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-01-21 DOI: 10.1002/2056-4538.70013
Katherine J Hewitt, Isabella C Wiest, Jakob N Kather
<p>We thank Koga <i>et al</i> for their knowledgeable comments on our work. Their letter highlights a valid question that requires clarification [<span>1</span>].</p><p>Our study assessed the ability of three large language models (LLMs) to diagnose neuropathology cases from free-text descriptions of adult-type diffuse gliomas, for which we compared two methodologies. The first method provided each model with the free-text tumor descriptions alone, while the second approach additionally provided the models with a Word document of the WHO CNS5. We termed these approaches zero-shot and retrieval-augmented generation (RAG), respectively [<span>2</span>]. Koga <i>et al</i> point out that the methodology we describe in our paper as RAG, may be better described as document-grounded generation, or in-context learning.</p><p>While we agree with the definition of RAG provided in the letter as it was initially defined [<span>3</span>], the field has evolved significantly since the approach was first proposed by Lewis <i>et al</i> in 2020. Three paradigms of RAG are now increasingly recognized: naive RAG, advanced RAG, and modular RAG [<span>4</span>]. Naive RAG is an approach where the data for indexing are generally obtained offline and converted into a format such as PDF or Word, and uploaded with the query via the context window. Advanced RAG and modular RAG offer specific improvements to address the limitations of naive RAG; however, to achieve this, they utilize more technical approaches.</p><p>The intention for our paper was to use naive RAG. We chose this approach as it leverages the easiest possible way for improving an LLM response that would be reproducible by doctors, considering that most doctors would be unable to utilize the application programming interface and programmatically build a RAG pipeline. As discussed by Koga <i>et al</i>, the key difference between naive RAG and document-grounding lies in how the document is utilized when the model retrieves its response [<span>5</span>]. Document-grounding submits the document with the user query and is equivalent to inserting the entire document text into the context window [<span>5</span>]. Whereas with naive RAG, relevant parts of the document are identified by the model and used with the query to dynamically search its database [<span>4</span>]. Both approaches are examples of in-context learning as they acquire additional knowledge from the prompt without requiring parameter updates [<span>6</span>].</p><p>Bereft of transparency from the LLM providers regarding how they process the document once it has been submitted via the graphical user interface, it is difficult to know whether naive RAG or document-grounding was used to formulate a response. To our knowledge, details regarding how appended documents are utilized during a query are not freely available online by ChatGPT, Llama, or Claude. Furthermore, due to the speed of development in the field, technical aspects of how documents are u
我们感谢Koga等人对我们的工作提出的有见地的评论。他们的信强调了一个需要澄清的合理问题。我们的研究评估了三种大型语言模型(llm)从成人型弥漫性神经胶质瘤的自由文本描述中诊断神经病理学病例的能力,为此我们比较了两种方法。第一种方法为每个模型单独提供自由文本的肿瘤描述,而第二种方法为模型额外提供WHO CNS5的Word文档。我们将这些方法分别命名为零射击和检索增强生成(RAG)。Koga等人指出,我们在论文中描述为RAG的方法可能被更好地描述为基于文档的生成或上下文学习。虽然我们同意信中提供的RAG的定义,因为它最初是在2010年定义的,但自Lewis等人在2020年首次提出该方法以来,该领域已经发生了重大变化。现在越来越多地认识到RAG的三种范式:朴素的RAG、高级的RAG和模块化的RAG[4]。朴素RAG是一种方法,在这种方法中,用于索引的数据通常是脱机获取的,并转换为PDF或Word等格式,然后通过上下文窗口与查询一起上传。高级RAG和模块化RAG提供了特定的改进,以解决原始RAG的局限性;然而,为了实现这一点,他们使用了更多的技术方法。我们论文的目的是使用朴素的RAG。考虑到大多数医生无法利用应用程序编程接口并以编程方式构建RAG管道,我们选择了这种方法,因为它利用了最简单的方法来改进医生可重复的LLM响应。正如Koga等人所讨论的,朴素RAG和基于文档之间的关键区别在于当模型检索其响应[5]时如何利用文档。文档基础提交带有用户查询的文档,相当于将整个文档文本插入上下文窗口[5]。而对于朴素的RAG,文档的相关部分由模型识别,并与查询一起用于动态搜索其数据库[4]。这两种方法都是上下文学习的例子,因为它们从提示符中获取额外的知识,而不需要参数更新[6]。在通过图形用户界面提交文档后,LLM提供者对于如何处理文档缺乏透明度,因此很难知道是否使用了幼稚的RAG或文档基础来制定响应。据我们所知,关于在查询期间如何使用附加文档的详细信息,ChatGPT、Llama或Claude并没有免费在线提供。此外,由于该领域的发展速度,自我们今年早些时候进行实验以来,如何利用文件的技术方面可能已经发生了变化。尽管如此,我们还是联系了ChatGPT, Anthropic和Poe,以帮助澄清这一点。所有三个提供者都确认使用查询上传的文档将用于RAG。然而,ChatGPT和Anthropic的回复都是由机器人生成的,这表明需要对实际使用的技术方法进行更可靠和更大的透明度。我们感谢你赞同我们的结论,并感谢有机会讨论这一重要区别。需要进一步的澄清和透明度,以明确区分特定法学硕士平台所采用的机制,特别是关于如何处理附加文件。我们建议我们的工作使用上下文学习,因为RAG和文档基础都是这种更广泛范例的方法。尽管如此,我们仍然致力于澄清此事,并感谢Koga等人的参与和宝贵的投入。KJH写了初稿;审查和批评;最后的批准。ICW审查和批评;最后的批准。JNK审查和批评;最后的批准。
{"title":"Authors' reply: Re: Koga et al. Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models","authors":"Katherine J Hewitt,&nbsp;Isabella C Wiest,&nbsp;Jakob N Kather","doi":"10.1002/2056-4538.70013","DOIUrl":"10.1002/2056-4538.70013","url":null,"abstract":"&lt;p&gt;We thank Koga &lt;i&gt;et al&lt;/i&gt; for their knowledgeable comments on our work. Their letter highlights a valid question that requires clarification [&lt;span&gt;1&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Our study assessed the ability of three large language models (LLMs) to diagnose neuropathology cases from free-text descriptions of adult-type diffuse gliomas, for which we compared two methodologies. The first method provided each model with the free-text tumor descriptions alone, while the second approach additionally provided the models with a Word document of the WHO CNS5. We termed these approaches zero-shot and retrieval-augmented generation (RAG), respectively [&lt;span&gt;2&lt;/span&gt;]. Koga &lt;i&gt;et al&lt;/i&gt; point out that the methodology we describe in our paper as RAG, may be better described as document-grounded generation, or in-context learning.&lt;/p&gt;&lt;p&gt;While we agree with the definition of RAG provided in the letter as it was initially defined [&lt;span&gt;3&lt;/span&gt;], the field has evolved significantly since the approach was first proposed by Lewis &lt;i&gt;et al&lt;/i&gt; in 2020. Three paradigms of RAG are now increasingly recognized: naive RAG, advanced RAG, and modular RAG [&lt;span&gt;4&lt;/span&gt;]. Naive RAG is an approach where the data for indexing are generally obtained offline and converted into a format such as PDF or Word, and uploaded with the query via the context window. Advanced RAG and modular RAG offer specific improvements to address the limitations of naive RAG; however, to achieve this, they utilize more technical approaches.&lt;/p&gt;&lt;p&gt;The intention for our paper was to use naive RAG. We chose this approach as it leverages the easiest possible way for improving an LLM response that would be reproducible by doctors, considering that most doctors would be unable to utilize the application programming interface and programmatically build a RAG pipeline. As discussed by Koga &lt;i&gt;et al&lt;/i&gt;, the key difference between naive RAG and document-grounding lies in how the document is utilized when the model retrieves its response [&lt;span&gt;5&lt;/span&gt;]. Document-grounding submits the document with the user query and is equivalent to inserting the entire document text into the context window [&lt;span&gt;5&lt;/span&gt;]. Whereas with naive RAG, relevant parts of the document are identified by the model and used with the query to dynamically search its database [&lt;span&gt;4&lt;/span&gt;]. Both approaches are examples of in-context learning as they acquire additional knowledge from the prompt without requiring parameter updates [&lt;span&gt;6&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Bereft of transparency from the LLM providers regarding how they process the document once it has been submitted via the graphical user interface, it is difficult to know whether naive RAG or document-grounding was used to formulate a response. To our knowledge, details regarding how appended documents are utilized during a query are not freely available online by ChatGPT, Llama, or Claude. Furthermore, due to the speed of development in the field, technical aspects of how documents are u","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11747990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models 检索增强生成与基于文档的生成:大型语言模型中的关键区别。
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-01-16 DOI: 10.1002/2056-4538.70014
Shunsuke Koga, Daisuke Ono, Amrom Obstfeld
<p>We read with great interest the article by Hewitt <i>et al</i>., ‘Large language models as a diagnostic support tool in neuropathology’ [<span>1</span>]. The authors effectively applied large language models (LLMs) to interpreting the WHO classification of central nervous system tumors; however, we wish to address a technical aspect of their study that warrants clarification.</p><p>The authors described their approach as retrieval-augmented generation (RAG). Based on the methods described, the study involved attaching a Word document containing the WHO diagnostic criteria to the prompt to guide its responses. We believe that this approach is more accurately described as document-grounded generation rather than true RAG. Document-grounded generation refers to methods where the model generates outputs explicitly based on a preprovided document, which serves as a static reference [<span>2</span>]. Unlike RAG, which retrieves information dynamically from external sources [<span>3</span>], document-grounded generation relies entirely on data embedded in the input prompt at the time of execution. In this study, the WHO criteria were provided with the prompt, which allowed the model to use this information without real-time retrieval. This method is a type of in-context learning, relying on curated contextual data embedded in the input [<span>4</span>].</p><p>Our own work provides an example of in-context learning in a different domain, namely image classification. We evaluated GPT-4 Vision (GPT-4V) for classifying histopathological images stained with tau immunohistochemistry, including neuritic plaques, astrocytic plaques, and tufted astrocytes [<span>5</span>]. Although GPT-4V initially struggled, few-shot learning with annotated examples, which is a specific application of in-context learning, significantly improved its accuracy, matching that of a convolutional neural network model trained on a larger dataset. These findings demonstrate the utility of in-context learning for both text-based and image-based tasks, with the latter presenting unique challenges for LLMs [<span>6</span>].</p><p>Although in-context learning is an effective approach, it has limitations worth considering. Since this method uses static data that are preloaded data into the prompt, errors can occur if the information is outdated or inaccurate. In-context learning may also lead to overfitting to the given context, limiting the model's ability to generalize to other scenarios. If the contextual data are overly complex, the model might misinterpret the information or fail to generate accurate outputs [<span>7</span>]. To ensure reliability, it is important to carefully select the input data, update it regularly, and consider these limitations when designing tasks.</p><p>In summary, clarifying the differences between RAG, document-grounded generation, and in-context learning is essential, especially for readers less familiar with these concepts. Nonetheless, we support the au
我们非常感兴趣地阅读了Hewitt等人的文章,“大型语言模型作为神经病理学的诊断支持工具”。作者有效地应用大语言模型(LLMs)来解释WHO对中枢神经系统肿瘤的分类;然而,我们希望处理他们的研究中值得澄清的一个技术方面。作者将他们的方法描述为检索增强生成(RAG)。根据所描述的方法,该研究涉及将包含世卫组织诊断标准的Word文档附加到提示框中,以指导其响应。我们认为这种方法更准确地描述为基于文档的生成,而不是真正的RAG。基于文档的生成是指模型根据预先提供的文档显式生成输出的方法,该文档用作静态引用[2]。与从外部源[3]动态检索信息的RAG不同,基于文档的生成完全依赖于执行时嵌入在输入提示符中的数据。在本研究中,为WHO标准提供了提示,允许模型使用这些信息而无需实时检索。这种方法是一种上下文学习,依赖于嵌入在输入[4]中的精心策划的上下文数据。我们自己的工作提供了一个不同领域的上下文学习的例子,即图像分类。我们评估了GPT-4 Vision (GPT-4V)对tau免疫组化染色的组织病理学图像的分类,包括神经斑块、星形细胞斑块和簇状星形细胞[5]。尽管GPT-4V最初遇到了困难,但带有注释示例的少数镜头学习(这是上下文学习的特定应用)显着提高了其准确性,与在更大数据集上训练的卷积神经网络模型相匹配。这些发现证明了上下文学习在基于文本和基于图像的任务中的效用,后者对法学硕士b[6]提出了独特的挑战。虽然情境学习是一种有效的学习方法,但它也有值得考虑的局限性。由于此方法使用预加载到提示符中的静态数据,因此如果信息过时或不准确,可能会发生错误。情境学习也可能导致对给定情境的过度拟合,限制了模型推广到其他场景的能力。如果上下文数据过于复杂,则模型可能会误解信息或无法生成准确的输出[7]。为了确保可靠性,必须仔细选择输入数据,定期更新数据,并在设计任务时考虑这些限制。总之,澄清RAG、基于文档的生成和上下文学习之间的区别是必要的,特别是对于不太熟悉这些概念的读者。尽管如此,我们支持作者的结论,即纳入外部数据可以提高诊断性能。他们的研究被解释为基于文档生成的一个例子,展示了法学硕士如何在精心策划的上下文数据的支持下有效地协助医疗任务。概念化,起草手稿。应该:审阅和编辑稿件。AO:审阅和编辑稿件。没有宣布利益冲突。
{"title":"Retrieval-augmented generation versus document-grounded generation: a key distinction in large language models","authors":"Shunsuke Koga,&nbsp;Daisuke Ono,&nbsp;Amrom Obstfeld","doi":"10.1002/2056-4538.70014","DOIUrl":"10.1002/2056-4538.70014","url":null,"abstract":"&lt;p&gt;We read with great interest the article by Hewitt &lt;i&gt;et al&lt;/i&gt;., ‘Large language models as a diagnostic support tool in neuropathology’ [&lt;span&gt;1&lt;/span&gt;]. The authors effectively applied large language models (LLMs) to interpreting the WHO classification of central nervous system tumors; however, we wish to address a technical aspect of their study that warrants clarification.&lt;/p&gt;&lt;p&gt;The authors described their approach as retrieval-augmented generation (RAG). Based on the methods described, the study involved attaching a Word document containing the WHO diagnostic criteria to the prompt to guide its responses. We believe that this approach is more accurately described as document-grounded generation rather than true RAG. Document-grounded generation refers to methods where the model generates outputs explicitly based on a preprovided document, which serves as a static reference [&lt;span&gt;2&lt;/span&gt;]. Unlike RAG, which retrieves information dynamically from external sources [&lt;span&gt;3&lt;/span&gt;], document-grounded generation relies entirely on data embedded in the input prompt at the time of execution. In this study, the WHO criteria were provided with the prompt, which allowed the model to use this information without real-time retrieval. This method is a type of in-context learning, relying on curated contextual data embedded in the input [&lt;span&gt;4&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Our own work provides an example of in-context learning in a different domain, namely image classification. We evaluated GPT-4 Vision (GPT-4V) for classifying histopathological images stained with tau immunohistochemistry, including neuritic plaques, astrocytic plaques, and tufted astrocytes [&lt;span&gt;5&lt;/span&gt;]. Although GPT-4V initially struggled, few-shot learning with annotated examples, which is a specific application of in-context learning, significantly improved its accuracy, matching that of a convolutional neural network model trained on a larger dataset. These findings demonstrate the utility of in-context learning for both text-based and image-based tasks, with the latter presenting unique challenges for LLMs [&lt;span&gt;6&lt;/span&gt;].&lt;/p&gt;&lt;p&gt;Although in-context learning is an effective approach, it has limitations worth considering. Since this method uses static data that are preloaded data into the prompt, errors can occur if the information is outdated or inaccurate. In-context learning may also lead to overfitting to the given context, limiting the model's ability to generalize to other scenarios. If the contextual data are overly complex, the model might misinterpret the information or fail to generate accurate outputs [&lt;span&gt;7&lt;/span&gt;]. To ensure reliability, it is important to carefully select the input data, update it regularly, and consider these limitations when designing tasks.&lt;/p&gt;&lt;p&gt;In summary, clarifying the differences between RAG, document-grounded generation, and in-context learning is essential, especially for readers less familiar with these concepts. Nonetheless, we support the au","PeriodicalId":48612,"journal":{"name":"Journal of Pathology Clinical Research","volume":"11 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11736412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Immunohistochemistry-based molecular subtypes of urothelial carcinoma derive different survival benefit from platinum chemotherapy 基于免疫组织化学的尿路上皮癌分子亚型从铂化疗中获得不同的生存益处。
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2025-01-16 DOI: 10.1002/2056-4538.70017
Csilla Olah, Oleksandr Shmorhun, Gilbert Georg Klamminger, Josefine Rawitzer, Lara Sichward, Boris Hadaschik, Mulham Al-Nader, Ulrich Krafft, Christian Niedworok, Melinda Váradi, Peter Nyirady, Andras Kiss, Eszter Szekely, Henning Reis, Tibor Szarvas

Distinct molecular subtypes of muscle-invasive bladder cancer (MIBC) may show different platinum sensitivities. Currently available data were mostly generated at transcriptome level and have limited comparability to each other. We aimed to determine the platinum sensitivity of molecular subtypes by using the protein expression-based Lund Taxonomy. In addition, we assessed the tumor heterogeneity within the primary tumor and between the primary and lymph node (LN) metastatic sites. Thirteen immunohistochemical markers were stained in a tissue microarray with an overall number of 1,508 cores. Statistical evaluation was performed in 199 patients divided into three chemo-naïve MIBC cohorts: (1) pT3/4 and/or LN+ patients who received radical cystectomy without platinum treatment, (2) patients who received adjuvant chemotherapy (AC), and (3) patients who underwent palliative platinum treatment for metastatic disease or postoperative progression. Overall survival (OS) was used as the primary endpoint. Patients with the genomically unstable (GU) subtype had significantly better OS in the AC group compared to the radical cystectomy group (HR: 0.395, 95% CI: 0.205–0.795, p = 0.005). In contrast, no such association was observed for the basal/squamous (Ba/Sq) subtype. Intratumor heterogeneity was present in 19% of cases, with the lowest level in the Ba/Sq and GU tumors (14% each) and the highest level of 43% in small-cell/neuroendocrine-like tumors. There was greater subtype heterogeneity between primary tumors and LN metastases. In conclusion, immunohistochemistry-based Lund Taxonomy subtypes remain stable within the same primary tumor, with the GU subtype deriving the greatest OS benefit from AC. However, high tumor heterogeneity between the primary tumor and metastatic sites can impact the effectiveness of therapies.

不同分子亚型的肌肉浸润性膀胱癌(MIBC)可能表现出不同的铂敏感性。目前可用的数据大多是在转录组水平产生的,彼此之间的可比性有限。我们的目的是通过基于蛋白表达的Lund分类来确定分子亚型的铂敏感性。此外,我们评估了原发肿瘤内部以及原发和淋巴结(LN)转移部位之间的肿瘤异质性。13个免疫组织化学标记在组织芯片上染色,总共有1,508个核。对199例患者进行统计评估,分为三个chemo-naïve MIBC队列:(1)接受根治性膀胱切除术而不接受铂治疗的pT3/4和/或LN+患者,(2)接受辅助化疗(AC)的患者,(3)因转移性疾病或术后进展而接受姑息性铂治疗的患者。总生存期(OS)作为主要终点。与根治性膀胱切除术组相比,AC组基因组不稳定(GU)亚型患者的OS明显更好(HR: 0.395, 95% CI: 0.205-0.795, p = 0.005)。相比之下,基底/鳞状(Ba/Sq)亚型没有观察到这种关联。19%的病例存在肿瘤内异质性,Ba/Sq和GU肿瘤的异质性最低(各占14%),小细胞/神经内分泌样肿瘤的异质性最高(43%)。原发肿瘤和淋巴结转移之间存在更大的亚型异质性。总之,基于免疫组织化学的Lund分类亚型在同一原发肿瘤中保持稳定,其中GU亚型从AC中获得最大的OS获益。然而,原发肿瘤和转移部位之间的高度肿瘤异质性会影响治疗的有效性。
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引用次数: 0
Loss of MTAP expression is strongly linked to homozygous 9p21 deletion, unfavorable tumor phenotype, and noninflamed microenvironment in urothelial bladder cancer MTAP表达的缺失与尿路上皮膀胱癌的同源性9p21缺失、不利的肿瘤表型和非炎症微环境密切相关。
IF 3.7 2区 医学 Q1 PATHOLOGY Pub Date : 2024-12-12 DOI: 10.1002/2056-4538.70012
Natalia Gorbokon, Niklas Wößner, Viktoria Ahlburg, Henning Plage, Sebastian Hofbauer, Kira Furlano, Sarah Weinberger, Paul Giacomo Bruch, Simon Schallenberg, Florian Roßner, Sefer Elezkurtaj, Maximilian Lennartz, Niclas C Blessin, Andreas H Marx, Henrik Samtleben, Margit Fisch, Michael Rink, Marcin Slojewski, Krystian Kaczmarek, Thorsten Ecke, Tobias Klatte, Stefan Koch, Nico Adamini, Sarah Minner, Ronald Simon, Guido Sauter, Henrik Zecha, David Horst, Thorsten Schlomm, Lukas Bubendorf, Martina Kluth

Homozygous 9p21 deletions usually result in a complete loss of S-methyl-5′-thioadenosine phosphorylase (MTAP) expression visualizable by immunohistochemistry (IHC). MTAP deficiency has been proposed as a marker for predicting targeted treatment response. A tissue microarray including 2,710 urothelial bladder carcinomas were analyzed for 9p21 deletion by fluorescence in situ hybridization and MTAP expression by IHC. Data were compared with data on tumor phenotype, patient survival, intratumoral lymphocyte subsets, and PD-L1 expression. The 9p21 deletion rate increased from pTaG2 low (9.2% homozygous, 25.8% heterozygous) to pTaG2 high (32.6%, 20.9%; p < 0.0001) but was slightly lower in pTaG3 (16.7%, 16.7%) tumors. In pT2–4 carcinomas, 23.3% homozygous and 17.9% heterozygous deletions were found, and deletions were tied to advanced pT (p = 0.0014) and poor overall survival (p = 0.0461). Complete MTAP loss was seen in 98.4% of homozygous deleted while only 1.6% of MTAP negative tumors had retained 9p21 copies (p < 0.0001). MTAP loss was linked to advanced stage and poor overall survival in pT2–4 carcinomas (p < 0.05 each). The relationship between 9p21 deletions/MTAP loss and poor patient prognosis was independent of pT and pN (p < 0.05 each). The 9p21 deletions were associated with a noninflamed microenvironment (p < 0.05). Complete MTAP loss is strongly tied to homozygous 9p21 deletion, aggressive disease, and noninflamed microenvironment. Drugs targeting MTAP-deficiency may be useful in urothelial bladder carcinoma. MTAP IHC is a near perfect surrogate for MTAP deficiency in this tumor type.

9p21 基因缺失通常会导致 S-甲基-5'-硫代腺苷磷酸化酶(MTAP)表达的完全丧失,免疫组织化学(IHC)可对其进行检测。MTAP 缺乏被认为是预测靶向治疗反应的标志物。通过荧光原位杂交分析了包括 2,710 例尿路膀胱癌在内的组织芯片的 9p21 缺失情况,并通过 IHC 分析了 MTAP 的表达情况。数据与肿瘤表型、患者生存期、瘤内淋巴细胞亚群和 PD-L1 表达等数据进行了比较。9p21缺失率从pTaG2低(9.2%同卵,25.8%杂合)上升到pTaG2高(32.6%,20.9%;p
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引用次数: 0
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