Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackles this issue would be helpful for diagnoses and subtyping of lung carcinoma. In this study, we developed AI models to classify multinomial patterns of lung carcinoma; ADC, LCNEC, SCC, SCLC, and non-neoplastic lung tissue based on convolutional neural networks (CNN or ConvNet). Four CNNs that were pre-trained using transfer learning and one CNN built from scratch were used to classify patch images from pathology whole-slide images (WSIs). We first evaluated the diagnostic performance of each model in the test sets. The Xception model and the CNN built from scratch both achieved the highest performance with a macro average AUC of 0.90. The CNN built from scratch model obtained a macro average AUC of 0.97 on the dataset of four classes excluding LCNEC, and 0.95 on the dataset of three subtypes of lung carcinomas; NSCLC, SCLC, and non-tumor, respectively. Of particular note is that the relatively simple CNN built from scratch may be an approach for pathological image analysis.

Clinicopathologic characteristics of HER2-positive metaplastic squamous cell carcinoma of the breast.

AIMS: The aim of this study was to analyse the clinicopathological features and prognosis of human epidermal growth factor receptor-2 (HER2)-positive metaplastic squamous cell carcinoma (MSCC). METHODS: Fifty-eight patients with MSCC of the breast who were classified into 45 triple-negative and 13 HER2-positive subgroups diagnosed at the West China Hospital, Sichuan University, from 2004 to 2018, were enrolled. Clinicopathological features were collected and compared between HER2-positive MSCC, triple-negative MSCC, HER2-positive invasive breast carcinoma of no special type (NST) and triple-negative NST groups. In the prognostic survival analysis, HER2-positive MSCCs was compared with triple-negative MSCCs, HER2-positive NSTs and triple-negative NSTs. RESULTS: Compared with triple-negative MSCCs, more patients with Ki-67 low expression were in HER2-positive MSCCs (p<0.05). More patients with HER2-positive MSCC than patients with HER2-positive NST were postmenopausal (p<0.05). Compared among HER2-positive MSCCs, triple-negative MSCCs and triple-negative NSTs, patients of HER2-positive MSCCs with high Ki-67 expression were the least, and HER2-positive MSCCs had more strongly associated with postmenopausal disease status (p<0.05). In survival analyses, HER2-positive MSCCs had a high risk of recurrence and poor prognosis (p<0.05). Lymph node status was significantly associated with the disease-free survival of patients with HER2-positive MSCC. CONCLUSION: In conclusion, our study indicates that HER2-positive MSCC is an aggressive disease with unique clinicopathological characteristics. Both HER2-positive status and an SCC component are critical factors for poor prognosis. HER2-positive MSCC and triple-negative MSCC are distinct subgroups. Corresponding targeted therapy recommendations should be made for this HER2-positive MSCC group.

Deep learning data augmentation for Raman spectroscopy cancer tissue classification.

Recently, Raman Spectroscopy (RS) was demonstrated to be a non-destructive way of cancer diagnosis, due to the uniqueness of RS measurements in revealing molecular biochemical changes between cancerous vs. normal tissues and cells. In order to design computational approaches for cancer detection, the quality and quantity of tissue samples for RS are important for accurate prediction. In reality, however, obtaining skin cancer samples is difficult and expensive due to privacy and other constraints. With a small number of samples, the training of the classifier is difficult, and often results in overfitting. Therefore, it is important to have more samples to better train classifiers for accurate cancer tissue classification. To overcome these limitations, this paper presents a novel generative adversarial network based skin cancer tissue classification framework. Specifically, we design a data augmentation module that employs a Generative Adversarial Network (GAN) to generate synthetic RS data resembling the training data classes. The original tissue samples and the generated data are concatenated to train classification modules. Experiments on real-world RS data demonstrate that (1) data augmentation can help improve skin cancer tissue classification accuracy, and (2) generative adversarial network can be used to generate reliable synthetic Raman spectroscopic data.

Using a convolutional neural network for classification of squamous and non-squamous non-small cell lung cancer based on diagnostic histopathology HES images.

Histological stratification in metastatic non-small cell lung cancer (NSCLC) is essential to properly guide therapy. Morphological evaluation remains the basis for subtyping and is completed by additional immunohistochemistry labelling to confirm the diagnosis, which delays molecular analysis and utilises precious sample. Therefore, we tested the capacity of convolutional neural networks (CNNs) to classify NSCLC based on pathologic HES diagnostic biopsies. The model was estimated with a learning cohort of 132 NSCLC patients and validated on an external validation cohort of 65 NSCLC patients. Based on image patches, a CNN using InceptionV3 architecture was trained and optimized to classify NSCLC between squamous and non-squamous subtypes. Accuracies of 0.99, 0.87, 0.85, 0.85 was reached in the training, validation and test sets and in the external validation cohort. At the patient level, the CNN model showed a capacity to predict the tumour histology with accuracy of 0.73 and 0.78 in the learning and external validation cohorts respectively. Selecting tumour area using virtual tissue micro-array improved prediction, with accuracy of 0.82 in the external validation cohort. This study underlines the capacity of CNN to predict NSCLC subtype with good accuracy and to be applied to small pathologic samples without annotation.

Human Papillomavirus-Related Multiphenotypic Sinonasal Carcinoma-An Even Broader Tumor Entity?

Human papillomavirus (HPV)-related multiphenotypic sinonasal carcinoma (HMSC) is a recently defined tumor subtype with apparent favorable clinical outcome despite aggressive histomorphology. However, in recent years, additional numbers of cases, with more variable features and at locations outside the sinonasal region, have complicated the definition of HMSC. Here, we have performed a systematic review of all cases described so far in order to accumulate more knowledge. We identified 127 articles published between 2013 and 2021, of which 21 presented unique cases. In total, 79 unique patient cases were identified and their clinical and micromorphological nature are herein summarized. In our opinion, better clinical follow-up data and a more detailed tumor characterization are preferably needed before HMSC can finally be justified as its own tumor entity.

Expression Profiles of ASIC1/2 and TRPV1/4 in Common Skin Tumors.

The acid-sensing ion channels ASIC1 and ASIC2, as well as the transient receptor potential vanilloid channels TRPV1 and TRPV4, are proton-gated cation channels that can be activated by low extracellular pH (pHe), which is a hallmark of the tumor microenvironment in solid tumors. However, the role of these channels in the development of skin tumors is still unclear. In this study, we investigated the expression profiles of ASIC1, ASIC2, TRPV1 and TRPV4 in malignant melanoma (MM), squamous cell carcinoma (SCC), basal cell carcinoma (BCC) and in nevus cell nevi (NCN). We conducted immunohistochemistry using paraffin-embedded tissue samples from patients and found that most skin tumors express ASIC1/2 and TRPV1/4. Striking results were that BCCs are often negative for ASIC2, while nearly all SCCs express this marker. Epidermal MM sometimes seem to lack ASIC1 in contrast to NCN. Dermal portions of MM show strong expression of TRPV1 more frequently than dermal NCN portions. Some NCN show a decreasing ASIC1/2 expression in deeper dermal tumor tissue, while MM seem to not lose ASIC1/2 in deeper dermal portions. ASIC1, ASIC2, TRPV1 and TRPV4 in skin tumors might be involved in tumor progression, thus being potential diagnostic and therapeutic targets.

Implication of the New AJCC pT Classification of SCC of the Lip Comparing With Other Oral Subsites.

OBJECTIVE: To determine the implication of the new AJCC staging system for pT classification in a cohort of patients with SCC of the lip mucosa and compare it to other oral cavity sites. METHODS: Retrospective cohort of 744 patients treated between 2002 and 2017, by the Head and Neck Surgery Department of the University of Sao Paulo. RESULTS: Of 95 lip patients, 42 had pT upstage (58.1% of pT1 to pT2-3 and 50% of pT2 to pT3). Similar DFS/OS observed for those pT1 maintained or upstaged to pT2-3, pT2 patients upstaged to pT3 presented worse OS (49.4% versus 92.3%, P = .032). The comparison between lip and other mouth topographies, denoted better prognosis for pT1-2, but not for pT3-4a. Lip tumors had lower DOI, rates of perineural/angiolymphatic invasion, nodal metastasis, recurrence, and death. CONCLUSION: The inclusion of DOI to the new pT classification better stratifies patients with SCC of the lip mucosa upstaged to pT3 by assessing inferior OS. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E2770-E2776, 2021.

Comparison of Two Proposed Changes to the Current Nodal Classification for Non--small Cell Lung Cancer Based on the Number and Ratio of Metastatic Lymph Nodes.

BACKGROUND: The current nodal classification is unsatisfactory in distinguishing the prognostically heterogeneous N1 or N2 non-small cell lung cancer (NSCLC). RESEARCH QUESTION: Is the combination of the current N category and the number of metastatic lymph nodes (N-#number) or the combination of the current N category and the ratio of the number of positive to resected lymph nodes (N-#ratio) better than the current N category alone? STUDY DESIGN AND METHODS: We identified 2,162 patients with N1 or N2 NSCLC from the Surveillance, Epidemiology, and End Results database (2004-2016). We classified these patients into three N-#number categories (N-#number-1, N-#number-2a, N-#number-2b) and three N-#ratio categories (N-#ratio-1, N-#ratio-2a, N-#ratio-2b). Lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method. The prognostic significance of the new nodal classifications was validated across each tumor size category (≤3 cm, 3-5 cm, 5-7cm, >7 cm). Cox proportional hazards regression was used to evaluate the association between each nodal classification and LCSS. RESULTS: The survival curves showed clear differences between each pair of N-#number and N-#ratio categories. A significant tendency toward the deterioration of LCSS from N-#number-1 to N-#number-2b was observed in all tumor size categories. However, the differences between each pair of N-#ratio categories were significant only in tumors from 3 to 7 cm. Although all three nodal classifications were independent prognostic indicators, the N-#number classification provided more accurate prognostic stratifications compared with the N-#ratio classification and the current nodal classification. INTERPRETATION: The N-#number classification followed by the N-#ratio classification might be better prognostic determinants than the current nodal classification in prognostically heterogeneous N1 or N2 NSCLC.

A deep learning model for the classification of indeterminate lung carcinoma in biopsy whole slide images.

The differentiation between major histological types of lung cancer, such as adenocarcinoma (ADC), squamous cell carcinoma (SCC), and small-cell lung cancer (SCLC) is of crucial importance for determining optimum cancer treatment. Hematoxylin and Eosin (H&E)-stained slides of small transbronchial lung biopsy (TBLB) are one of the primary sources for making a diagnosis; however, a subset of cases present a challenge for pathologists to diagnose from H&E-stained slides alone, and these either require further immunohistochemistry or are deferred to surgical resection for definitive diagnosis. We trained a deep learning model to classify H&E-stained Whole Slide Images of TBLB specimens into ADC, SCC, SCLC, and non-neoplastic using a training set of 579 WSIs. The trained model was capable of classifying an independent test set of 83 challenging indeterminate cases with a receiver operator curve area under the curve (AUC) of 0.99. We further evaluated the model on four independent test sets-one TBLB and three surgical, with combined total of 2407 WSIs-demonstrating highly promising results with AUCs ranging from 0.94 to 0.99.

New proposal to revise the classification for squamous cell carcinoma of the external auditory canal and middle ear.

BACKGROUND: The prognosis of patients with advanced squamous cell carcinoma of the external auditory canal and middle ear has been improved by advances in skull base surgery and multidrug chemoradiotherapy during the last two decades. METHODS: Ninety-five patients with squamous cell carcinoma of the external auditory canal and middle ear who were treated between 1998 and 2017 were enrolled. The number of patients with tumour stages T1, T2, T3 and T4 was 15, 22, 24 and 34, respectively. Oncological outcomes and prognostic factors were retrospectively investigated. RESULTS: Among patients with T4 disease, invasion of the brain (p = 0.024), carotid artery (p = 0.049) and/or jugular vein (p = 0.040) were significant predictors of poor prognosis. The five-year overall survival rate of patients with at least one of these factors (T4b) was significantly lower than that of patients without these factors (T4a) (25.5 vs 65.5 per cent, p = 0.049). CONCLUSION: It is proposed that stage T4 be subclassified into T4a and T4b according to the prognostic factors.

A systematic review of the primary squamous cell carcinoma of the external auditory canal: survival outcome based on T-staging and proposal of a new classification.

OBJECTIVE: This study aimed to provide a systematic review on survival outcome based on Pittsburgh T-staging for patients with primary external auditory canal squamous cell carcinoma. METHOD: This study was a systematic review in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines performed until January 2018; pertinent studies were screened. Quality of evidence was assessed using the grading of recommendation, assessment, development and evaluation working group system. RESULTS: Eight articles were chosen that reported on 437 patients with external auditory carcinoma. The 5-year overall survival rate was 53.0 per cent. The pooled proportion of survivors at 5 years for T1 tumours was 88.4 per cent and for T2 tumours was 88.6 per cent. For the combined population of T1 and T2 cancer patients, it was 84.5 per cent. For T3 and T4 tumours, it was 53.3 per cent and 26.8 per cent, respectively, whereas for T3 and T4 tumours combined, it was 40.4 per cent. Individual analysis of 61 patients with presence of cervical nodes showed a poor survival rate. CONCLUSION: From this review, there was not any significant difference found in the survival outcome between T1 and T2 tumours. A practical classification incorporating nodal status that accurately stratifies patients was proposed.

Identification of potential serum biomarkers for simultaneously classifying lung adenocarcinoma, squamous cell carcinoma and small cell carcinoma.

BACKGROUND: Histological subtypes of lung cancer are crucial for making treatment decisions. However, multi-subtype classifications including adenocarcinoma (AC), squamous cell carcinoma (SqCC) and small cell carcinoma (SCLC) were rare in the previous studies. This study aimed at identifying and screening potential serum biomarkers for the simultaneous classification of AC, SqCC and SCLC. PATIENTS AND METHODS: A total of 143 serum samples of AC, SqCC and SCLC were analyzed by 1HNMR and UPLC-MS/MS. The stepwise discriminant analysis (DA) and multilayer perceptron (MLP) were employed to screen the most efficient combinations of markers for classification. RESULTS: The results of non-targeted metabolomics analysis showed that the changes of metabolites of choline, lipid or amino acid might contribute to the classification of lung cancer subtypes. 17 metabolites in those pathways were further quantified by UPLC-MS/MS. DA screened out that serum xanthine, S-adenosyl methionine (SAM), carcinoembryonic antigen (CEA), neuron-specific enolase (NSE) and squamous cell carcinoma antigen (SCC) contributed significantly to the classification of AC, SqCC and SCLC. The average accuracy of 92.3% and the area under the receiver operating characteristic curve of 0.97 would be achieved by MLP model when a combination of those five variables as input parameters. CONCLUSION: Our findings suggested that metabolomics was helpful in screening potential serum markers for lung cancer classification. The MLP model established can be used for the simultaneous diagnosis of AC, SqCC and SCLC with high accuracy, which is worthy of further study.

Mapping of Lymph Node Metastasis From Esophagogastric Junction Tumors: A Prospective Nationwide Multicenter Study.

OBJECTIVE: The aim of the study was to determine the optimal extent of lymph node dissection for the 2 histological types of esophagogastric junction (EGJ) tumors based on the incidence of metastasis in a prospective nationwide multicenter study. BACKGROUND: Because most previous studies were retrospective, the optimal surgical procedure for EGJ tumors has not been standardized. METHODS: Patients with cT2-T4 adenocarcinoma or squamous cell carcinoma located within 2.0 cm of the EGJ were enrolled before surgery. Surgeons dissected all lymph nodes prespecified in the protocol, using either the abdominal transhiatal or right transthoracic approach. The primary endpoint was the metastasis rate of each lymph node. Lymph nodes were classified according to metastasis rate, as follows: category-1 (strongly recommended for dissection), rate more than 10%; category-2 (weakly recommended for dissection), rate from 5% to 10%; and category-3 (not recommended for dissection), rate less than 5%. RESULTS: Between 2014 and 2017, 1065 patients with EGJ tumor were screened, and 371 were enrolled. Among 358 patients who underwent surgical resection, category-1 nodes included abdominal stations 1, 2, 3, 7, 9, and 11p, whereas category-2 nodes included abdominal stations 8a, 19, and lower mediastinal station 110. If esophageal involvement exceeded 2.0 cm, station 110 was assigned to category-1. Among 98 patients who had either adenocarcinoma with esophageal involvement over 3.0 cm or squamous cell carcinoma, there were no category-1 nodes in the upper/middle mediastinal field, whereas category-2 nodes included upper mediastinal station 106recR and middle mediastinal station 108. When esophageal involvement exceeded 4.0 cm, station 106recR was assigned to category-1. CONCLUSION: The study accurately identified the distribution of lymph node metastases from EGJ tumors and the optimal extent of subsequent lymph node dissection.

Volume and Mass Doubling Time of Lung Adenocarcinoma according to WHO Histologic Classification.

OBJECTIVE: This study aimed to evaluate the tumor doubling time of invasive lung adenocarcinoma according to the International Association of the Study for Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) histologic classification. MATERIALS AND METHODS: Among the 2905 patients with surgically resected lung adenocarcinoma, we retrospectively included 172 patients (mean age, 65.6 ± 9.0 years) who had paired thin-section non-contrast chest computed tomography (CT) scans at least 84 days apart with the same CT parameters, along with 10 patients with squamous cell carcinoma (mean age, 70.9 ± 7.4 years) for comparison. Three-dimensional semiautomatic segmentation of nodules was performed to calculate the volume doubling time (VDT), mass doubling time (MDT), and specific growth rate (SGR) of volume and mass. Multivariate linear regression, one-way analysis of variance, and receiver operating characteristic curve analyses were performed. RESULTS: The median VDT and MDT of lung cancers were as follows: acinar, 603.2 and 639.5 days; lepidic, 1140.6 and 970.1 days; solid/micropapillary, 232.7 and 221.8 days; papillary, 599.0 and 624.3 days; invasive mucinous, 440.7 and 438.2 days; and squamous cell carcinoma, 149.1 and 146.1 days, respectively. The adjusted SGR of volume and mass of the solid-/micropapillary-predominant subtypes were significantly shorter than those of the acinar-, lepidic-, and papillary-predominant subtypes. The histologic subtype was independently associated with tumor doubling time. A VDT of 465.2 days and an MDT of 437.5 days yielded areas under the curve of 0.791 and 0.795, respectively, for distinguishing solid-/micropapillary-predominant subtypes from other subtypes of lung adenocarcinoma. CONCLUSION: The tumor doubling time of invasive lung adenocarcinoma differed according to the IASCL/ATS/ERS histologic classification.

The impact of nodal status in major salivary gland carcinoma: A multicenter experience and proposal of a novel N-classification.

OBJECTIVES: Despite differences in oncological behavior, the 8th edition of AJCC TNM staging currently proposes the same N-classification for major salivary glands (MSG) carcinoma and squamous cell carcinoma of the upper aerodigestive tract. The present study aims to investigate a more reliable definition of N-categories for MSG carcinoma. MATERIALS AND METHODS: A retrospective multicenter study was performed, including 307 patients treated for primary MSG carcinoma from 1995 to 2019. Outcome measures included overall survival (OS), disease specific survival, and local, regional, and distant recurrence. Survival analysis was performed using log-rank test and Cox proportional-hazards model. Overall number (ON) and largest diameter (LD) of nodal metastases, including intra-parotid metastases, were considered to develop three novel proposals of N-classification; their performance were compared with the current TNM staging using Akaike information criterion (AIC), Bayesian information criterion (BIC), and Nagelkerke pseudo-R2. RESULTS: Intra-parotid nodes, ON and LD of nodal metastases emerged as major prognosticators for OS, while extra-nodal extension did not impact on any survival. The current N-classification did not show a satisfactory OS stratification. Three novel N-classifications were developed according to number of metastatic nodes (0 vs 1-3 vs ≥ 4) and/or their maximum diameter (<20 mm vs ≥ 20 mm). They all showed better accuracy in OS stratification, and achieved better AIC, BIC and Nagelkerke pseudo-R2 indices when compared to current N-classification. CONCLUSION: All the proposed N-classifications improved OS stratification and could help in defining a specific N-classification for MSG carcinoma. Their validation and assessment in an external cohort is needed.

Assessment of apoptotic index in various grades of oral epithelial dysplasia: A cross-sectional study.

CONTEXT: : Oral cancer is a major health problem worldwide. In cancer, the equilibrium between cell proliferation and apoptosis is disturbed. The defect in the apoptotic pathway allows cells to proliferate with genetic abnormalities. Thus, the apoptotic index (AI) can be used to assess the significance of apoptosis as a proliferative marker in oral epithelial dysplasia. AIMS: To assess the apoptotic index in various grades of epithelial dysplasia. OBJECTIVES: 1) To calculate the apoptotic index in various grades of oral epithelial dysplasia, 2) To compare the apoptotic index between various grades of oral epithelial dysplasia, 3) To predict the biologic behavior of oral epithelial dysplasia based on an apoptotic index. SETTINGS AND DESIGN: Cross-sectional tissue analyzing study. METHODS AND MATERIALS: This study constituted 30 cases, previously diagnosed with various grades of oral epithelial dysplasia (OED). AI was calculated as the number of apoptotic bodies/cells expressed as a percentage of the total number of cells counted in each case. STATISTICAL ANALYSIS USED: Statistical analysis was carried out using ANOVA test. RESULTS: A statistically significant difference was observed between mild dysplasia and severe dysplasia where P = 0.002. The mean AI was increased progressively with increasing grades of OED. CONCLUSIONS: This study demonstrated the clinical significance of apoptosis in assessing disease progression in Oral Potentially Malignant Disorder (OPMD) which may be used as a prognostic indicator in OED. This would, in turn, help in knowing the prognosis of the disease and to develop targeted drug therapy.

Tumor budding score predicts lymph node status in oral tongue squamous cell carcinoma and should be included in the pathology report.

BACKGROUND: The majority of oral cavity cancers arise in the oral tongue. The aim of this study was to evaluate the prognostic value of tumor budding in oral tongue squamous cell carcinoma, both as a separate variable and in combination with depth of invasion. We also assessed the prognostic impact of the 8th edition of the American Joint Committee on Cancer's TNM classification (TNM8), where depth of invasion (DOI) supplements diameter in the tumor size (T) categorization. METHODS: Patients diagnosed with primary oral tongue squamous cell carcinoma were evaluated retrospectively. Spearman bivariate correlation analyses with bootstrapping were used to identify correlation between variables. Prognostic value of clinical and histopathological variables was assessed by Log rank and Cox regression analyses with bootstrapping using 5-year disease specific survival as outcome. The significance level for the hypothesis test was 0.05. RESULTS: One-hundred and fifty patients had available material for microscopic evaluation on Hematoxylin and Eosin-stained slides and were included in the analyses. Reclassification of tumors according to TNM8 caused a shift towards a higher T status compared to the previous classification. The tumor budding score was associated with lymph node metastases where 23% of the patients with low-budding tumors had lymph node metastases, compared with 43% of those with high-budding tumors. T-status, lymph node status, tumor budding, depth of invasion, and the combined tumor budding/depth of invasion score were all significantly associated with survival in univariate analyses. In multivariate analyses only N-status was an independent prognosticator of survival. CONCLUSION: Reclassification according to TNM8 shifted many tumors to a higher T-status, and also increased the prognostic value of the T-status. This supports the implementation of depth of invasion to the T-categorization in TNM8. Tumor budding correlated with lymph node metastases and survival. Therefore, information on tumor budding can aid clinicians in treatment planning and should be included in pathology reports of oral tongue squamous cell carcinomas.

Role of gene signatures combined with pathology in classification of oropharynx head and neck cancer.

Treatment personalisation remains an unmet need in oropharynx cancer (OPC). We aimed to determine whether gene expression signatures improved upon clinico-pathological predictors of outcome in OPC. The clinico-pathological predictors, AJCC version 7 (AJCC 7), AJCC 8, and a clinical algorithm, were assessed in 4 public series of OPC (n = 235). Literature review identified 16 mRNA gene expression signatures of radiosensitivity, HPV status, tumour hypoxia, and microsatellite instability. We quality tested signatures using a novel sigQC methodology, and added signatures to clinico-pathological variables as predictors of survival, in univariate and multivariate analyses. AJCC 7 Stage was not predictive of recurrence-free survival (RFS) or overall survival (OS). AJCC 8 significantly predicted RFS and OS. Gene signature quality was highly variable. Among HPV-positive cases, signatures for radiosensitivity, hypoxia, and microsatellite instability revealed significant underlying inter-tumour biological heterogeneity, but did not show prognostic significance when adjusted for clinical covariates. Surprisingly, among HPV-negative cases, a gene signature for HPV status was predictive of survival, even after adjustment for clinical covariates. Across the whole series, several gene signatures representing HPV and microsatellite instability remained significant in multivariate analysis. However, quality control and independent validation remain to be performed to add prognostic information above recently improved clinico-pathological variables.

Revisión de los términos que definen un carcinoma epidermoide cutáneo asociado a mal pronóstico / A review of terms used to define cutaneous squamous cell carcinoma with a poor prognosis

El carcinoma epidermoide cutáneo (CEC) es el segundo tumor más frecuente en humanos y tiene una incidencia creciente e infraestimada. En la literatura nos encontramos con términos como CEC de alto riesgo, CEC localmente avanzado, CEC metastásico, CEC avanzado y CEC agresivo, que pueden dar lugar a confusión y que en algunas ocasiones no se encuentran del todo bien definidos. En esta revisión pretendemos aclarar estos conceptos con la idea de lograr homogeneidad en su descripción, algo que parece necesario a la luz de los nuevos fármacos aprobados y en desarrollo para este tumor

Cutaneous squamous cell carcinoma (cSCC) is the second most common cancer in humans and its incidence is both underestimated and on the rise. cSCC is referred to in the literature as high-risk cSCC, locally advanced cSCC, metastatic cSCC, advanced cSCC, and aggressive cSCC. These terms can give rise to confusion and are not always well defined. In this review, we aim to clarify the concepts underlying these terms with a view to standardizing the description of this tumor, something we believe is necessary in light of the new drugs that have been approved or are in development for cSCC