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The potential role of the transient receptor potential Vanilloid 1 (TRPV1) non-selective cation channel in gastric carcinogenesis remains unclear. The main objective of this study was to evaluate TRPV1 expression in gastric cancer (GC) and precursor lesions compared with controls. Patient inclusion was based on a retrospective review of pathology records. Patients were subdivided into five groups: Helicobacter pylori (H. pylori)-associated gastritis with gastric intestinal metaplasia (GIM) (n = 12), chronic atrophic gastritis (CAG) with GIM (n = 13), H. pylori-associated gastritis without GIM (n = 19), GC (n = 6) and controls (n = 5). TRPV1 expression was determined with immunohistochemistry and was significantly higher in patients with H. pylori-associated gastritis compared with controls (p = 0.002). TRPV1 expression was even higher in the presence of GIM compared with patients without GIM and controls (p < 0.001). There was a complete loss of TRPV1 expression in patients with GC. TRPV1 expression seems to contribute to gastric-mucosal inflammation and precursors of GC, which significantly increases in cancer precursor lesions but is completely lost in GC. These findings suggest TRPV1 expression to be a potential marker for precancerous conditions and a target for individualized treatment. Longitudinal studies are necessary to further address the role of TRPV1 in gastric carcinogenesis.
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Infecções por Helicobacter , Neoplasias Gástricas , Canais de Cátion TRPV , Humanos , Canais de Cátion TRPV/metabolismo , Canais de Cátion TRPV/genética , Neoplasias Gástricas/metabolismo , Neoplasias Gástricas/patologia , Masculino , Feminino , Pessoa de Meia-Idade , Idoso , Infecções por Helicobacter/metabolismo , Infecções por Helicobacter/complicações , Infecções por Helicobacter/patologia , Carcinogênese/metabolismo , Carcinogênese/patologia , Estudos Retrospectivos , Lesões Pré-Cancerosas/metabolismo , Lesões Pré-Cancerosas/patologia , Helicobacter pylori/patogenicidade , Metaplasia/metabolismo , Metaplasia/patologia , Gastrite/metabolismo , Gastrite/patologia , Gastrite/microbiologia , Adulto , Imuno-Histoquímica , Mucosa Gástrica/metabolismo , Mucosa Gástrica/patologia , Gastrite Atrófica/metabolismo , Gastrite Atrófica/patologiaRESUMO
BACKGROUND: Long-term energy balance-related factors (i.e. lifestyle and physiological factors that influence the equilibrium between energy intake and energy expenditure over an extended period) like body mass index (BMI) are linked to colorectal cancer (CRC) risk, but their impact on CRC survival is unclear. We explored associations between these long-term energy balance-related factors and survival, and examined potential differences across metabolic Warburg-subtypes. METHODS: Associations between long-term energy balance-related factors and survival in the total series of CRC patients (n=2,347) obtained from the prospective Netherlands Cohort Study, as well as according to Warburg-subtype (Warburg-low: n=652, Warburg-moderate: n=802, Warburg-high: n=797), were investigated using Cox regression analysis. RESULTS: Among the long-term energy balance-related factors studied, only increasing pre-diagnostic BMI was associated with a borderline significant poorer overall survival in CRC patients (HR5kg/m2 1.07; 95%CI 0.99-1.15). Stratified analyses showed that pre-diagnostic weight gain (HR5kg 1.06; 95%CI 1.00-1.12), and potentially increased height (HR5cm 1.04; 95%CI 0.98-1.11), were associated with poor overall survival only in patients with Warburg-high CRC. Increasing pre-diagnostic BMI was associated with poor survival only in patients with Warburg-moderate CRC (CRC-specific: HR5kg/m2 1.12; 95%CI 0.96-1.32, overall: HR5kg/m2 1.20; 95%CI 1.05-1.36). No consistent patterns were observed across energy-restriction proxies. CONCLUSIONS: Maintaining a healthy pre-diagnostic BMI may be beneficial for CRC survival. Moreover, associations between pre-diagnostic BMI, weight change, early-life energy restriction, height and CRC survival differed according to Warburg-subtype. IMPACT: Understanding the biological pathways involved in associations between energy balance-related factors and CRC survival could help refine prevention strategies in the future.
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BACKGROUND: Stroma AReactive Invasion Front Areas (SARIFA) is a recently identified haematoxylin & eosin (H&E)based histopathologic biomarker in gastrointestinal cancers, including colorectal cancer (CRC), defined as direct contact between tumour cells and adipocytes at the tumour invasion front. The current study aimed at validating the prognostic relevance of SARIFA in a large population-based CRC series as well as at investigating the relationship between SARIFA-status and previously established Warburg-subtypes, both surrogates of the metabolic state of the tumour cells. METHODS: SARIFA-status (positive versus negative) was determined on H&E slides of 1,727 CRC specimens. Warburg-subtype (high versus moderate versus low) data was available from our previous study. The associations between SARIFA-status, Warburg-subtype, clinicopathological characteristics and CRC-specific as well as overall survival were investigated. RESULTS: 28.7% (n=496) CRC were SARIFA-positive. SARIFA-positivity was associated with more advanced disease stage, higher pT category, and more frequent lymph node involvement (all p<0.001). SARIFA-positivity was more common in Warburg-high CRC. 44.2% (n=219) of SARIFA-positive CRCs were Warburg-high compared to 22.8% (n=113) being Warburg-low and 33.1% (n=164) being Warburg-moderate (p<0.001). In multivariable-adjusted analysis, patients with SARIFA-positive CRCs had significantly poorer CRC-specific (HRCRC-specific 1.65; 95% CI 1.41-1.93) and overall survival (HRoverall survival 1.46; 95% CI 1.28-1.67) independent of clinically known risk factors and independent of Warburg-subtype. Combining the SARIFA-status and the Warburg-subtype to a combination score (SARIFA-negative/Warburg-high versus SARIFA-positive/Warburg-low versus SARIFA-positive/Warburg-high, and so on) did not improve the survival prediction compared to the use of SARIFA-status alone (SARIFA-negative + Warburg-high: HRCRC-specific 1.08; 95% CI 0.84-1.38; SARIFA-positive + Warburg-low: HRCRC-specific 1.79; 95% CI 1.32-2.41; SARIFA-positive + Warburg-high: HRCRC-specific 1.58; 95% CI 1.23-2.04). CONCLUSIONS: Our current study is the by far largest external validation of SARIFA-positivity as a novel independent negative prognostic H&E-based biomarker in CRC. In addition, our study shows that SARIFA-positivity is associated with the Warburg-high subtype. Further research is warranted to provide a more mechanistic understanding of the underlying tumour biology. Based on our data, we conclude SARIFA-status should be implemented in pathologic routine practice to stratify CRC patients.
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BACKGROUND: Surgery plus peri-operative/adjuvant chemotherapy is the standard of care for locally advanced GC/GEJC, though with unsatisfactory results. dMMR/MSI-high tumors have better prognosis and scant benefit from chemotherapy as compared to pMMR/MSS ones. The differential outcome of therapies in terms of safety and efficacy according to sex is still debated in GC/GEJC patients. METHODS: We previously performed an individual patient data pooled analysis of MAGIC, CLASSIC, ITACA-S, and ARTIST trials including GC/GEJC patients treated with surgery alone or surgery plus peri-operative/adjuvant chemotherapy to assess the value of MSI status. We performed a secondary analysis investigating the prognostic and predictive role of sex (female versus male) in the pooled analysis dataset in the overall population and patients stratified for MSI status (MSI-high versus MSS/MSI-low). Disease-free (DFS) and overall survival (OS) were calculated. RESULTS: Patients with MSI-high tumors had improved survival as compared to MSS/MSI-low ones irrespective of sex, whereas in those with MSS/MSI-low tumors, females had numerically longer OS and DFS (5-year OS was 63.2% versus 57.6%, HR 0.842; p = 0.058, and 5-year DFS was 55.8% versus 50.8%, HR 0.850; p = 0.0504 in female versus male patients). The numerical difference for the detrimental effect of chemotherapy in MSI-high GC was higher in females than males, while the significant benefit of chemotherapy over surgery alone was confirmed in MSS/MSI-low GC irrespective of sex. CONCLUSIONS: This pooled analysis including four randomized trials highlights a relevant impact of sex in the prognosis and treatment efficacy of MSI-high and MSS/MSI-low non-metastatic GC/GEJC.
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Junção Esofagogástrica , Instabilidade de Microssatélites , Ensaios Clínicos Controlados Aleatórios como Assunto , Neoplasias Gástricas , Humanos , Masculino , Feminino , Junção Esofagogástrica/patologia , Neoplasias Gástricas/genética , Neoplasias Gástricas/patologia , Neoplasias Gástricas/terapia , Neoplasias Gástricas/tratamento farmacológico , Neoplasias Gástricas/mortalidade , Prognóstico , Fatores Sexuais , Neoplasias Esofágicas/genética , Neoplasias Esofágicas/patologia , Neoplasias Esofágicas/mortalidade , Neoplasias Esofágicas/terapia , Neoplasias Esofágicas/tratamento farmacológico , Pessoa de Meia-Idade , Idoso , Quimioterapia AdjuvanteRESUMO
Background: Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured. Objective: To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research. Methods: A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles. Results: A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible. Conclusions: Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.
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BACKGROUND: Stromal tumour infiltrating lymphocytes (sTILs) and presence of tertiary lymphoid structures have been proposed as indicators of tumour-related immune response in breast cancer. An increased number of germinal centres (GCs) in lymph nodes is considered a sign of humoral immune reactivity. AIMS: It is unclear whether a relationship exists between number and size of GCs within tumour positive sentinel lymph nodes (SLNpos), sTILs and tertiary lymphoid structures within matched primary breast cancer and breast cancer subtype. METHODS: Axillary SLNpos from 175 patients with breast cancer were manually contoured in digitized haematoxylin and eosin stained sections. Total SLN area, GC number and GC area were measured in SLNpos with the largest metastatic area. To correct for SLN size, GC number and GC area were divided by SLN area. sTILs and presence of tertiary lymphoid structures were assessed in the primary breast cancer. RESULTS: A higher GC number and larger GC area were found in patients with high sTILs (≥2%) (both P < 0.001) and in patients with presence of tertiary lymphoid structures (PGC number = 0.034 and PGC area = 0.016). Triple negative and HER2-positive (N = 45) breast cancer subtypes had a higher GC number and higher sTILs compared to hormone receptor positive, HER2-negative breast cancer (N = 130) (PGC number < 0.001 and PsTILs= 0.001). CONCLUSION: This study suggests GCs measured within SLNpos might be useful indicators of the humoral anti-tumour immune response in breast cancer. Future studies are needed investigating underlying biological mechanisms and prognostic value of GCs in SLNs.
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Neoplasias da Mama , Linfonodo Sentinela , Estruturas Linfoides Terciárias , Humanos , Feminino , Neoplasias da Mama/patologia , Linfonodo Sentinela/patologia , Linfócitos do Interstício Tumoral/patologia , Estruturas Linfoides Terciárias/patologia , Linfonodos/patologia , Centro Germinativo/patologia , Axila/patologiaRESUMO
BACKGROUND: Artificial intelligence (AI) has numerous applications in pathology, supporting diagnosis and prognostication in cancer. However, most AI models are trained on highly selected data, typically one tissue slide per patient. In reality, especially for large surgical resection specimens, dozens of slides can be available for each patient. Manually sorting and labelling whole-slide images (WSIs) is a very time-consuming process, hindering the direct application of AI on the collected tissue samples from large cohorts. In this study we addressed this issue by developing a deep-learning (DL)-based method for automatic curation of large pathology datasets with several slides per patient. METHODS: We collected multiple large multicentric datasets of colorectal cancer histopathological slides from the United Kingdom (FOXTROT, N = 21,384 slides; CR07, N = 7985 slides) and Germany (DACHS, N = 3606 slides). These datasets contained multiple types of tissue slides, including bowel resection specimens, endoscopic biopsies, lymph node resections, immunohistochemistry-stained slides, and tissue microarrays. We developed, trained, and tested a deep convolutional neural network model to predict the type of slide from the slide overview (thumbnail) image. The primary statistical endpoint was the macro-averaged area under the receiver operating curve (AUROCs) for detection of the type of slide. RESULTS: In the primary dataset (FOXTROT), with an AUROC of 0.995 [95% confidence interval [CI]: 0.994-0.996] the algorithm achieved a high classification performance and was able to accurately predict the type of slide from the thumbnail image alone. In the two external test cohorts (CR07, DACHS) AUROCs of 0.982 [95% CI: 0.979-0.985] and 0.875 [95% CI: 0.864-0.887] were observed, which indicates the generalizability of the trained model on unseen datasets. With a confidence threshold of 0.95, the model reached an accuracy of 94.6% (7331 classified cases) in CR07 and 85.1% (2752 classified cases) for the DACHS cohort. CONCLUSION: Our findings show that using the low-resolution thumbnail image is sufficient to accurately classify the type of slide in digital pathology. This can support researchers to make the vast resource of existing pathology archives accessible to modern AI models with only minimal manual annotations.
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Neoplasias Colorretais , Aprendizado Profundo , Humanos , Neoplasias Colorretais/patologia , Neoplasias Colorretais/diagnóstico , Redes Neurais de Computação , Processamento de Imagem Assistida por Computador/métodos , Interpretação de Imagem Assistida por Computador/métodosRESUMO
BACKGROUND: Precise prognosis prediction in patients with colorectal cancer (ie, forecasting survival) is pivotal for individualised treatment and care. Histopathological tissue slides of colorectal cancer specimens contain rich prognostically relevant information. However, existing studies do not have multicentre external validation with real-world sample processing protocols, and algorithms are not yet widely used in clinical routine. METHODS: In this retrospective, multicentre study, we collected tissue samples from four groups of patients with resected colorectal cancer from Australia, Germany, and the USA. We developed and externally validated a deep learning-based prognostic-stratification system for automatic prediction of overall and cancer-specific survival in patients with resected colorectal cancer. We used the model-predicted risk scores to stratify patients into different risk groups and compared survival outcomes between these groups. Additionally, we evaluated the prognostic value of these risk groups after adjusting for established prognostic variables. FINDINGS: We trained and validated our model on a total of 4428 patients. We found that patients could be divided into high-risk and low-risk groups on the basis of the deep learning-based risk score. On the internal test set, the group with a high-risk score had a worse prognosis than the group with a low-risk score, as reflected by a hazard ratio (HR) of 4·50 (95% CI 3·33-6·09) for overall survival and 8·35 (5·06-13·78) for disease-specific survival (DSS). We found consistent performance across three large external test sets. In a test set of 1395 patients, the high-risk group had a lower DSS than the low-risk group, with an HR of 3·08 (2·44-3·89). In two additional test sets, the HRs for DSS were 2·23 (1·23-4·04) and 3·07 (1·78-5·3). We showed that the prognostic value of the deep learning-based risk score is independent of established clinical risk factors. INTERPRETATION: Our findings indicate that attention-based self-supervised deep learning can robustly offer a prognosis on clinical outcomes in patients with colorectal cancer, generalising across different populations and serving as a potentially new prognostic tool in clinical decision making for colorectal cancer management. We release all source codes and trained models under an open-source licence, allowing other researchers to reuse and build upon our work. FUNDING: The German Federal Ministry of Health, the Max-Eder-Programme of German Cancer Aid, the German Federal Ministry of Education and Research, the German Academic Exchange Service, and the EU.
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Neoplasias Colorretais , Aprendizado Profundo , Humanos , Estudos Retrospectivos , Prognóstico , Fatores de Risco , Neoplasias Colorretais/diagnóstico , Neoplasias Colorretais/patologiaRESUMO
BACKGROUND: Tumour-associated fat cells without desmoplastic stroma reaction at the invasion front (Stroma AReactive Invasion Front Areas (SARIFA)) is a prognostic biomarker in gastric and colon cancer. The clinical utility of the SARIFA status in oesophagogastric cancer patients treated with perioperative chemotherapy is currently unknown. METHODS: The SARIFA status was determined in tissue sections from patients recruited into the MAGIC (n = 292) or ST03 (n = 693) trials treated with surgery alone (S, MAGIC) or perioperative chemotherapy (MAGIC, ST03). The relationship between SARIFA status, clinicopathological factors, overall survival (OS) and treatment was analysed. RESULTS: The SARIFA status was positive in 42% MAGIC trial S patients, 28% MAGIC and 48% ST03 patients after pre-operative chemotherapy. SARIFA status was related to OS in MAGIC trial S patients and was an independent prognostic biomarker in ST03 trial patients (HR 1.974, 95% CI 1.555-2.507, p < 0.001). ST03 patients with lymph node metastasis (ypN + ) and SARIFA-positive tumours had poorer OS than patients with ypN+ and SARIFA-negative tumours (plogrank < 0.001). CONCLUSIONS: The SARIFA status has clinical utility as prognostic biomarker in oesophagogastric cancer patients irrespective of treatment modality. Whilst underlying biological mechanisms warrant further investigation, the SARIFA status might be used to identify new drug targets, potentially enabling repurposing of existing drugs targeting lipid metabolism.
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Adenocarcinoma , Neoplasias Gástricas , Humanos , Prognóstico , Neoplasias Gástricas/tratamento farmacológico , Neoplasias Gástricas/cirurgia , Neoplasias Gástricas/patologia , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Adenocarcinoma/patologia , Medição de Risco , BiomarcadoresRESUMO
Objective: In this review, we aim to provide an overview of literature on lymph node (LN) histomorphological features and their relationship with the prognosis in colorectal cancer (CRC). Background: Lymph nodes play a crucial role in the treatment and prognosis of CRC. The presence of LN metastases considerably worsens the prognosis in CRC patients. Literature has shown that the total number of LNs and the number negative LNs (LNnegs) has prognostic value in CRC patients. In esophageal carcinoma, LN size seems to be surrogate of the host antitumor response and a potentially clinically useful new prognostic biomarker for (y)pN0 esophageal carcinoma. Methods: A comprehensive search was performed in Pubmed, Embase, Medline, CINAHL, and the Cochrane library in March 2021. The PRISMA guidelines were followed. Only studies focusing on histomorphological features and LN size and their relation to overall survival were selected. Results: A total of 9 unique articles met all inclusion criteria and were therefore included in this systematic review. Six of these studies investigated HMF (eg, paracortical hyperplasia, germinal center predominance, and sinus histiocytosis) and 4 studies LNneg size and their relationship with overall survival. The presence of paracortical hyperplasia and an increased number of large LNnegs is related to a more favorable prognosis in CRC. Conclusion: The results of this systematic review seem to support the hypothesis that there is a relationship between the host antitumor response reflected in different histomorphological reaction patterns visible in LNnegs and LNneg size related to survival in CRC patients.
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BACKGROUND: Conflicting results about the prognostic relevance of signet ring cell histology in gastric cancer have been reported. We aimed to perform a meta-analysis focusing on the clinicopathological features and prognosis of this subgroup of cancer compared with other histologies. METHODS: A systematic literature search in the PubMed database was conducted, including all publications up to 1 October 2021. A meta-analysis comparing the results of the studies was performed. RESULTS: A total of 2062 studies referring to gastric cancer with signet ring cell histology were identified, of which 262 studies reported on its relationship with clinical information. Of these, 74 were suitable to be included in the meta-analysis. A slightly lower risk of developing nodal metastases in signet ring cell tumours compared to other histotypes was found (especially to undifferentiated/poorly differentiated/mucinous and mixed histotypes); the lower risk was more evident in early and slightly increased in advanced gastric cancer. Survival tended to be better in early stage signet ring cell cancer compared to other histotypes; no differences were shown in advanced stages, and survival was poorer in metastatic patients. In the subgroup analysis, survival in signet ring cell cancer was slightly worse compared to non-signet ring cell cancer and differentiated/well-to-moderately differentiated adenocarcinoma. CONCLUSIONS: Most of the conflicting results in signet ring cell gastric cancer literature could be derived from the lack of standardisation in their classification and the comparison with the different subtypes of gastric cancer. There is a critical need to strive for a standardised classification system for gastric cancer, fostering clarity and coherence in the forthcoming research and clinical applications.
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Introduction: Sentinel lymph node (SLN) metastasis is an important predictor of prognosis in breast cancer (BC) patients, guiding treatment decisions. However, patients with the same BC subtype and tumor negative SLN (SLNneg) can have different survival outcomes. We hypothesized that the host anti-tumor immune reaction in SLNneg is important and results in morphometrically measurable changes in SLN size or shape which are related to patient prognosis. Methods: Surface area, circumference, long axis and short axis were histologically measured in 694 SLNneg from 356 cases of invasive BC and 67 ductal carcinoma in situ cases. The area occupied by fat was categorized as less or more than 50%. The long to short axis (L/S) ratio was calculated. The relationship between SLNneg morphometries and clinicopathological variables like tumor-infiltrating lymphocytes (TILs) within the primary tumor, as well as prognosis at 10 years follow up were analyzed. Results: The mean SLNneg surface area was 78.7mm2, circumference 40.3mm, long axis 13.1mm, short axis 8.2mm and L/S ratio 1.7. Larger surface area, long axis and short axis, including age >55 years were associated with higher body mass index (BMI) and SLN fat over 50% (p<0.003). In invasive BC, a high SLNneg L/S ratio (≥1.9) was related to poorer disease-free (HR=1.805, 95%CI 1.182-2.755, p=0.006) and overall (HR=2.389, 95%CI 1.481-3.851, p<0.001) survival. A low SLNneg L/S ratio (<1.9) was associated with high TILs in the primary BC (≥10%) (p=0.005). However a high TIL count was not of prognostic relevance. Conclusions: This is the first study to suggest that morphometric characteristics of axillary SLNneg, like L/S ratio, could be used to predict prognosis in patients with SLNneg invasive BC of all subtypes. The association between low L/S ratio and high TILs suggest that SLN shape is related to immunological functioning of the SLN and could be used in addition to TIL evaluation. Regarding the dubious role of TILs in hormone receptor positive breast cancer, SLNneg morphometry to gain information about host immune status could especially be of benefit in this subtype. Further studies are warranted to better understand the underlying biological mechanisms.
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Neoplasias da Mama , Neoplasias Mamárias Animais , Linfonodo Sentinela , Humanos , Animais , Pessoa de Meia-Idade , Feminino , Prognóstico , Neoplasias da Mama/patologia , Biópsia de Linfonodo Sentinela/métodos , Metástase Linfática , Neoplasias Mamárias Animais/patologiaRESUMO
AIM: Gastric cancer (GC) is a tumour entity with highly variant outcomes. Lymph node metastasis is a prognostically adverse biomarker. We hypothesised that GC primary tissue contains information that is predictive of lymph node status and patient prognosis and that this information can be extracted using deep learning (DL). METHODS: Using three patient cohorts comprising 1146 patients, we trained and validated a DL system to predict lymph node status directly from haematoxylin and eosin-stained GC tissue sections. We investigated the concordance between the DL-based prediction from the primary tumour slides (aiN score) and the histopathological lymph node status (pN). Furthermore, we assessed the prognostic value of the aiN score alone and when combined with the pN status. RESULTS: The aiN score predicted the pN status reaching area under the receiver operating characteristic curves of 0.71 in the training cohort and 0.69 and 0.65 in the two test cohorts. In a multivariate Cox analysis, the aiN score was an independent predictor of patient survival with hazard ratios of 1.5 in the training cohort and of 1.3 and 2.2 in the two test cohorts. A combination of the aiN score and the pN status prognostically stratified patients by survival with p-values <0.05 in logrank tests. CONCLUSION: GC primary tumour tissue contains additional prognostic information that is accessible using the aiN score. In combination with the pN status, this can be used for personalised management of GC patients after prospective validation.
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Aprendizado Profundo , Neoplasias Gástricas , Humanos , Estudos Retrospectivos , Neoplasias Gástricas/patologia , Linfonodos/patologia , PrognósticoRESUMO
BACKGROUND: The status of regional tumour draining lymph nodes (LN) is crucial for prognostic evaluation in gastric cancer (GaC) patients. Changes in lymph node microarchitecture, such as follicular hyperplasia (FH), sinus histiocytosis (SH), or paracortical hyperplasia (PH), may be triggered by the anti-tumour immune response. However, the prognostic value of these changes in GaC patients is unclear. METHODS: A systematic search in multiple databases was conducted to identify studies on the prognostic value of microarchitecture changes in regional tumour-negative and tumour-positive LNs measured on histopathological slides. Since the number of GaC publications was very limited, the search was subsequently expanded to include junctional and oesophageal cancer (OeC). RESULTS: A total of 28 articles (17 gastric cancer, 11 oesophageal cancer) met the inclusion criteria, analyzing 26,503 lymph nodes from 3711 GaC and 1912 OeC patients. The studies described eight different types of lymph node microarchitecture changes, categorized into three patterns: hyperplasia (SH, FH, PH), cell-specific infiltration (dendritic cells, T cells, neutrophils, macrophages), and differential gene expression. Meta-analysis of five GaC studies showed a positive association between SH in tumour-negative lymph nodes and better 5-year overall survival. Pooled risk ratios for all LNs showed increased 5-year overall survival for the presence of SH and PH. CONCLUSIONS: This systematic review suggests that sinus histiocytosis and paracortical hyperplasia in regional tumour-negative lymph nodes may provide additional prognostic information for gastric and oesophageal cancer patients. Further studies are needed to better understand the lymph node reaction patterns and explore their impact of chemotherapy treatment and immunotherapy efficacy.
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Neoplasias Esofágicas , Histiocitose Sinusal , Neoplasias Gástricas , Humanos , Neoplasias Gástricas/cirurgia , Neoplasias Gástricas/patologia , Hiperplasia/patologia , Histiocitose Sinusal/patologia , Relevância Clínica , Linfonodos/cirurgia , Linfonodos/patologia , Prognóstico , Neoplasias Esofágicas/patologia , Estadiamento de NeoplasiasRESUMO
Deep learning (DL) can accelerate the prediction of prognostic biomarkers from routine pathology slides in colorectal cancer (CRC). However, current approaches rely on convolutional neural networks (CNNs) and have mostly been validated on small patient cohorts. Here, we develop a new transformer-based pipeline for end-to-end biomarker prediction from pathology slides by combining a pre-trained transformer encoder with a transformer network for patch aggregation. Our transformer-based approach substantially improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training and evaluating on a large multicenter cohort of over 13,000 patients from 16 colorectal cancer cohorts, we achieve a sensitivity of 0.99 with a negative predictive value of over 0.99 for prediction of microsatellite instability (MSI) on surgical resection specimens. We demonstrate that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem.
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Algoritmos , Neoplasias Colorretais , Humanos , Biomarcadores , Biópsia , Instabilidade de Microssatélites , Neoplasias Colorretais/genéticaRESUMO
BACKGROUND: Combination of immunotherapy and chemotherapy is recommended for first line treatment of gastric adenocarcinoma (GC) patients with locally advanced unresectable disease or metastatic disease. However, data regarding the concordance rate between PD-L1 combined positive score (CPS) in primary GC and matched regional lymph node metastasis (LNmet) or matched distant metastasis (Dmet) is limited. METHODS: Tissue microarray sections from primary resected GC, LNmet and Dmet were immunohistochemically stained with anti-PD-L1 (clone SP263). PD-L1 expression was scored separately in tumour cells and immune cells and compared between matched primary GC, LNmet and/or Dmet. CPS was calculated and results for CPS cut-offs 1 and 5 were compared between matched samples. RESULTS: 275 PD-L1 stained GC were analysed. 189 primary GC had matched LNmet. CPS cut-off 1 concordance rate between primary GC and LNmet was 77%. 23 primary GC had matched Dmet but no matched LNmet, CPS cut-off 1 concordance rate was 70%. 63 primary GC had both matched LNmet and matched Dmet, CPS cut-off 1 concordance rate of 67%. CPS cut-off 5 results were similar. The proportion of PD-L1 positive tumour cells increased from primary GC (26%) to LNmet (42%) and was highest in Dmet (75%). CONCLUSION: Our study showed up to 33% discordance of PD-L1 CPS between primary GC and LNmet and/or Dmet suggesting that multiple biopsies of primary GC and metastatic sites might need to be tested before considering treatment options. Moreover, this is the first study that seems to suggest that tumour cells acquire PD-L1 expression during disease progression.
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PURPOSE: There is limited evidence regarding the prognostic effects of pathologic lymph node (LN) regression after neoadjuvant chemotherapy for esophageal adenocarcinoma, and a definition of LN response is lacking. This study aimed to evaluate how LN regression influences survival after surgery for esophageal adenocarcinoma. METHODS: Multicenter cohort study of patients with esophageal adenocarcinoma treated with neoadjuvant chemotherapy followed by surgical resection at five high-volume centers in the United Kingdom. LNs retrieved at esophagectomy were examined for chemotherapy response and given a LN regression score (LNRS)-LNRS 1, complete response; 2, <10% residual tumor; 3, 10%-50% residual tumor; 4, >50% residual tumor; and 5, no response. Survival analysis was performed using Cox regression adjusting for confounders including primary tumor regression. The discriminatory ability of different LN response classifications to predict survival was evaluated using Akaike information criterion and Harrell C-index. RESULTS: In total, 17,930 LNs from 763 patients were examined. LN response classified as complete LN response (LNRS 1 ≥1 LN, no residual tumor in any LN; n = 62, 8.1%), partial LN response (LNRS 1-3 ≥1 LN, residual tumor ≥1 LN; n = 155, 20.3%), poor/no LN response (LNRS 4-5; n = 303, 39.7%), or LN negative (no tumor/regression; n = 243, 31.8%) demonstrated superior discriminatory ability. Mortality was reduced in patients with complete LN response (hazard ratio [HR], 0.35; 95% CI, 0.22 to 0.56), partial LN response (HR, 0.72; 95% CI, 0.57 to 0.93) or negative LNs (HR, 0.32; 95% CI, 0.25 to 0.42) compared with those with poor/no LN response. Primary tumor regression and LN regression were discordant in 165 patients (21.9%). CONCLUSION: Pathologic LN regression after neoadjuvant chemotherapy was a strong prognostic factor and provides important information beyond pathologic TNM staging and primary tumor regression grading. LN regression should be included as standard in the pathologic reporting of esophagectomy specimens.
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Adenocarcinoma , Neoplasias Esofágicas , Linfonodos , Humanos , Adenocarcinoma/tratamento farmacológico , Adenocarcinoma/cirurgia , Adenocarcinoma/patologia , Estudos de Coortes , Neoplasias Esofágicas/tratamento farmacológico , Neoplasias Esofágicas/cirurgia , Esofagectomia , Linfonodos/cirurgia , Linfonodos/patologia , Terapia Neoadjuvante , Estadiamento de Neoplasias , Neoplasia Residual/patologia , Prognóstico , Reino UnidoRESUMO
INTRODUCTION: The Laurén classification is widely used for Gastric Cancer (GC) histology subtyping. However, this classification is prone to interobserver variability and its prognostic value remains controversial. Deep Learning (DL)-based assessment of hematoxylin and eosin (H&E) stained slides is a potentially useful tool to provide an additional layer of clinically relevant information, but has not been systematically assessed in GC. OBJECTIVE: We aimed to train, test and externally validate a deep learning-based classifier for GC histology subtyping using routine H&E stained tissue sections from gastric adenocarcinomas and to assess its potential prognostic utility. METHODS: We trained a binary classifier on intestinal and diffuse type GC whole slide images for a subset of the TCGA cohort (N = 166) using attention-based multiple instance learning. The ground truth of 166 GC was obtained by two expert pathologists. We deployed the model on two external GC patient cohorts, one from Europe (N = 322) and one from Japan (N = 243). We assessed classification performance using the Area Under the Receiver Operating Characteristic Curve (AUROC) and prognostic value (overall, cancer specific and disease free survival) of the DL-based classifier with uni- and multivariate Cox proportional hazard models and Kaplan-Meier curves with log-rank test statistics. RESULTS: Internal validation using the TCGA GC cohort using five-fold cross-validation achieved a mean AUROC of 0.93 ± 0.07. External validation showed that the DL-based classifier can better stratify GC patients' 5-year survival compared to pathologist-based Laurén classification for all survival endpoints, despite frequently divergent model-pathologist classifications. Univariate overall survival Hazard Ratios (HRs) of pathologist-based Laurén classification (diffuse type versus intestinal type) were 1.14 (95% Confidence Interval (CI) 0.66-1.44, p-value = 0.51) and 1.23 (95% CI 0.96-1.43, p-value = 0.09) in the Japanese and European cohorts, respectively. DL-based histology classification resulted in HR of 1.46 (95% CI 1.18-1.65, p-value < 0.005) and 1.41 (95% CI 1.20-1.57, p-value < 0.005), in the Japanese and European cohorts, respectively. In diffuse type GC (as defined by the pathologist), classifying patients using the DL diffuse and intestinal classifications provided a superior survival stratification, and demonstrated statistically significant survival stratification when combined with pathologist classification for both the Asian (overall survival log-rank test p-value < 0.005, HR 1.43 (95% CI 1.05-1.66, p-value = 0.03) and European cohorts (overall survival log-rank test p-value < 0.005, HR 1.56 (95% CI 1.16-1.76, p-value < 0.005)). CONCLUSION: Our study shows that gastric adenocarcinoma subtyping using pathologist's Laurén classification as ground truth can be performed using current state of the art DL techniques. Patient survival stratification seems to be better by DL-based histology typing compared with expert pathologist histology typing. DL-based GC histology typing has potential as an aid in subtyping. Further investigations are warranted to fully understand the underlying biological mechanisms for the improved survival stratification despite apparent imperfect classification by the DL algorithm.
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Adenocarcinoma , Aprendizado Profundo , Neoplasias Gástricas , Humanos , Neoplasias Gástricas/patologia , Estudos Retrospectivos , Prognóstico , Modelos de Riscos Proporcionais , Adenocarcinoma/patologiaRESUMO
BACKGROUND/AIM: To determine the clinical significance of pre-treatment circumferential tumor location within the esophageal wall in patients with locally advanced esophageal squamous cell carcinoma who underwent curative resection after neoadjuvant chemotherapy. PATIENTS AND METHODS: Patients (n=96) with cStage IB-III esophageal squamous cell carcinoma who underwent curative resection after neoadjuvant chemotherapy were categorized into two groups based on the circumferential tumor location within the esophageal wall on diagnostic endoscopy: left or anterior wall group (L/A patients, n=49); right or posterior wall group (R/P patients, n=47). Analyses were conducted to examine the relationship between circumferential tumor location, clinicopathological characteristics, 3-year overall survival (3Y-OS), and 3-year recurrence-free survival (3Y-RFS). RESULTS: The lymph node status and recurrence rates of mediastinal lymph node metastases were significantly higher in patients with L/A than in patients with R/P. Furthermore, patients with L/A had significantly poorer 3Y-OS and 3Y-RFS than those with R/P. Tumor location within the esophageal wall was identified in multivariate analysis as an independent risk factor for 3Y-RFS (hazard ratio=2.92, 95% confidence interval=1.35-6.32, p=0.0064). CONCLUSION: Pre-treatment of circumferential tumor located within the esophageal wall may be a useful prognostic factor in patients with cStageIB-III esophageal squamous cell carcinoma who underwent curative resection after neoadjuvant chemotherapy.