Colorectal medullary carcinoma: a pathological subtype with intense immune response and potential to benefit from immune checkpoint inhibitors.

INTRODUCTION: Different pathological types of colorectal cancer have distinguished immune landscape, and the efficacy of immunotherapy will be completely different. Colorectal medullary carcinoma, accounting for 2.2-3.2%, is characterized by massive lymphocyte infiltration. However, the attention to the immune characteristics of colorectal medullary carcinoma is insufficient. AREA COVERED: We searched the literature about colorectal medullary carcinoma on PubMed through November 2023to investigate the hallmarks of colorectal medullary carcinoma's immune landscape, compare medullary carcinoma originating from different organs and provide theoretical evidence for precise treatment, including applying immunotherapy and BRAF inhibitors. EXPERT OPINION: Colorectal medullary carcinoma is a pathological subtype with intense immune response, with six immune characteristics and has the potential to benefit from immunotherapy. Mismatch repair deficiency, ARID1A missing and BRAF V600E mutation often occurs. IFN-γ pathway is activated and PD-L1 expression is increased. Abundant lymphocyte infiltration performs tumor killing function. In addition, BRAF mutation plays an important role in the occurrence and development, and we can consider the combination of BRAF inhibitors and immunotherapy in patients with BRAF mutant. The exploration of colorectal medullary carcinoma will arouse researchers' attention to the correlation between pathological subtypes and immune response, and promote the process of precise immunotherapy.

Genetic landscape of breast cancer subtypes following radiation therapy: insights from comprehensive profiling.

Background: In breast cancer, in the era of precision cancer therapy, different patterns of genetic mutations dictate different treatments options. However, it is not clear whether the genetic profiling of breast cancer patients undergoing breast-conserving surgery is related to the adverse reactions caused by radiotherapy. Methods: We collected formalin-fixed paraffin-embedded (FFPE) tumor tissue samples from 54 breast cancer patients treated with radiation after breast-conserving surgery and identified comprehensive molecular information in hundreds of cancer-associated genes by FoundationOne CDx (F1CDx), a next-generation sequencing (NGS)-based assay. Results: Among our cohort of 54 breast cancer patients, we found high-frequency mutations in cancer-related genes such as TP53 (56%), RAD21 (39%), PIK3CA (35%), ERBB2 (24%), and MYC (22%). Strikingly, we detected that the WNT pathway appears to be a signaling pathway with specific high-frequency mutations in the HER2 subtype. We also compared the mutation frequencies of the two groups of patients with and without cutaneous radiation injury (CRI) after radiotherapy and found that the mutation frequencies of two genes, FGFR1 and KLHL6, were significantly higher in patients with CRI : No subgroup than in those with CRI : Yes. Conclusion: Different breast cancer subtypes have their own type-specific mutation patterns. FGFR1 and KLHL6 mutations are protective factors for radiation-induced skin toxicity in breast cancer patients.

Prognostic biomarker discovery based on proteome landscape of Chinese lung adenocarcinoma.

Despite recent innovations in imaging and genomic screening promotes advance in diagnosis and treatment of lung adenocarcinoma (LUAD), there remains high mortality of LUAD and insufficient understanding of LUAD biology. Our previous study performed an integrative multi-omic analysis of LUAD, filling the gap between genomic alterations and their biological proteome effects. However, more detailed molecular characterization and biomarker resources at proteome level still need to be uncovered. In this study, a quantitative proteomic experiment of patient-derived benign lung disease samples was carried out. After that, we integrated the proteomic data with previous dataset of 103 paired LUAD samples. We depicted the proteomic differences between non-cancerous and tumor samples and among diverse pathological subtypes. We also found that up-regulated mitophagy was a significant characteristic of early-stage LUAD. Additionally, our integrative analysis filtered out 75 potential prognostic biomarkers and validated two of them in an independent LUAD serum cohort. This study provided insights for improved understanding proteome abnormalities of LUAD and the novel prognostic biomarker discovery offered an opportunity for LUAD precise management.

A Nomogram Based on Consolidation Tumor Ratio Combined with Solid or Micropapillary Patterns for Postoperative Recurrence in Pathological Stage IA Lung Adenocarcinoma.

BACKGROUND: Patients with pathological stage IA lung adenocarcinoma (LUAD) are at risk of relapse. The value of the TNM staging system is limited in predicting recurrence. Our study aimed to develop a precise recurrence prediction model for stage IA LUAD. MATERIALS AND METHODS: Patients with pathological stage IA LUAD who received surgical treatment at Zhongshan Hospital Fudan University were retrospectively analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for recurrence-free survival (RFS). The predictive performance of the model was assessed using calibration plots and the concordance index (C-index). RESULTS: The multivariate Cox regression analysis revealed that CTR (0.75 < CTR ≤ 1; HR = 9.882, 95% CI: 2.036-47.959, p = 0.004) and solid/micropapillary-predominance (SMPP; >5% and the most dominant) (HR = 4.743, 95% CI: 1.506-14.933, p = 0.008) were independent prognostic factors of RFS. These risk factors were used to construct a nomogram to predict postoperative recurrence in these patients. The C-index of the nomogram for predicting RFS was higher than that of the eighth T-stage system (0.873 for the nomogram and 0.643 for the eighth T stage). The nomogram also achieved good predictive performance for RFS with a well-fitted calibration curve. CONCLUSIONS: We developed and validated a nomogram based on CTR and SMP patterns for predicting postoperative recurrence in pathological stage IA LUAD. This model is simple to operate and has better predictive performance than the eighth T stage system, making it suitable for selecting further adjuvant treatment and follow-up.

Preoperative levels of folate receptor-positive circulating tumor cells in different subtypes of early-stage lung adenocarcinoma: Predictive value for determining extent of surgical resection.

Background: The objective was to measure the correlations of preoperative levels of folate receptor-positive circulating tumor cells (FR+CTCs) with clinical characteristics and histologic subtype in early-stage lung adenocarcinoma, and to determine the predictive value of FR+CTC level in preoperative determination of the extent of surgical resection. Patients and methods: In this retrospective, single-institution, observational study, preoperative FR+CTC levels were measured via ligand-targeted enzyme-linked polymerization in patients with early-stage lung adenocarcinoma. Receiver operating characteristic (ROC) analysis was used to identify the optimal cutoff value of FR+CTC level for prediction of various clinical characteristics and histologic subtypes. Results: No significant difference in FR+CTC level was observed among patients with adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) (P = 0.813). Within the non-mucinous adenocarcinoma group, no difference was observed among patients with tumors whose predominant growth patterns were lepidic, acinar, papillary, micropapillary, solid, and complex gland (P = 0.053). However, significant differences in FR+CTC level were observed between patients with and without the micropapillary subtype [11.21 (8.22-13.61) vs. 9.85 (7.43-12.63), P = 0.017], between those with and without the solid subtype [12.16 (8.27-14.90) vs. 9.87 (7.50-12.49), P = 0.022], and between those with any of the advanced subtypes (micropapillary, solid, or complex glands) vs. none of these [10.48 (7.83-13.67) vs. 9.76 (7.42-12.42), P = 0.032]. FR+CTC level was also correlated with degree of differentiation of lung adenocarcinoma (P = 0.033), presence of visceral pleural invasion (VPI) of lung carcinoma (P = 0.003), and lymph node metastasis of lung carcinoma (P = 0.035). Conclusion: FR+CTC level is of potential predictive value in determining the presence of aggressive histologic patterns (micropapillary, solid, and advanced subtypes), degree of differentiation, and occurrence of VPI and lymph node metastasis in IAC. Measurement of FR+CTC level combined with intraoperative frozen sections may represent a more effective method of guiding resection strategy in cases of cT1N0M0 IAC with high-risk factors.

Deep learning-based classification and spatial prognosis risk score on whole-slide images of lung adenocarcinoma.

AIMS: Classification of histological patterns in lung adenocarcinoma (LUAD) is critical for clinical decision-making, especially in the early stage. However, the inter- and intraobserver subjectivity of pathologists make the quantification of histological patterns varied and inconsistent. Moreover, the spatial information of histological patterns is not evident to the naked eye of pathologists. METHODS AND RESULTS: We establish the LUAD-subtype deep learning model (LSDLM) with optimal ResNet34 followed by a four-layer Neural Network classifier, based on 40 000 well-annotated path-level tiles. The LSDLM shows robust performance for the identification of histopathological subtypes on the whole-slide level, with an area under the curve (AUC) value of 0.93, 0.96 and 0.85 across one internal and two external validation data sets. The LSDLM is capable of accurately distinguishing different LUAD subtypes through confusion matrices, albeit with a bias for high-risk subtypes. It possesses mixed histology pattern recognition on a par with senior pathologists. Combining the LSDLM-based risk score with the spatial K score (K-RS) shows great capacity for stratifying patients. Furthermore, we found the corresponding gene-level signature (AI-SRSS) to be an independent risk factor correlated with prognosis. CONCLUSIONS: Leveraging state-of-the-art deep learning models, the LSDLM shows capacity to assist pathologists in classifying histological patterns and prognosis stratification of LUAD patients.

The predictive value of CT signs of mixed ground-glass nodules in pathological subtypes and differentiation of lung adenocarcinoma / 中国胸心血管外科临床杂志

@#Objective    To explore the predictive value of CT signs of mixed ground-glass nodules in the pathological subtype and differentiation of lung adenocarcinoma. Methods    The clinical data of 66 patients with mixed ground-glass nodules pathologically diagnosed as invasive adenocarcinoma (IAC) in the Second Department of Thoracic Surgery, the First Affiliated Hospital of Xiamen University from May to December 2021 were retrospectively analyzed, including 20 males and 46 females, aged 35-75 years. The CT findings were analyzed before operation, and the lesion profile was cut after operation to distinguish the ground-glass and solid components, and the pathological results of different positions were obtained. According to the postoperative pathological results, the patients were divided into a low-risk group (containing adherent type and no components of micropapillary subtype and solid subtype, n=16), a medium-risk group (containing niple or acinar type and no components of micropapillary subtype and solid subtype, n=38), and a high-risk group (containing micropapillary or solid subtype, n=12). The relationships between CT features and the pathological subtype and degree of differentiation were analyzed and compared. Results    In 66 patients with IAC, the infiltration degree of solid components was greater than that of ground-glass components. When the solid component ratio (CTR) was≥25% (sensitivity 90.2%, specificity 64.0%, P=0.005), and the average CT value was>−283.95 HU (sensitivity 82.9%, specificity 64.0%, P=0.000), the histological grade was more inclined to medium and low differentiation. The CTR, Ki-67, average CT value and histological grade of IAC in the medium- and high-risk groups were higher than those of nodules in the low-risk group. Conclusion    The infiltration degree of solid components is higher than that of ground-glass components in IAC mixed ground-glass nodules. The pathological subtype, Ki-67 expression and histological grade of lung adenocarcinoma can be predicted according to its CT characteristics, which has important clinical significance for determining the timing of surgery.

Comparison of somatic gene mutation between 114 cases with different subtypes of thyroid papillary carcinoma and the TCGA database / 预防医学

Objective@#To compare the difference in somatic gene mutation of PTC subtypes between 114 patients with papillary thyroid carcinoma (PTC) and The Cancer Genome Atlas (TCGA) database.@*Methods@#Totally 114 PTC patients admitted to The First Affiliated Hospital of Nanjing Medical University were recruited. The 18 hotspot genes associated with thyroid cancer were detected in thyroidectomy specimens were using next generation sequencing. PTC data were downloaded from the TCGA database in the cBioPortal website, and the difference in the somatic gene mutation was compared between 114 PTC patients and the TCGA database@*Results@#The 114 PTC patients included 73 women (64.04%) and had a mean age of (39.23±13.18) years. The prevalence of BRAF V600E (66.67% vs. 48.68%), TERTp (3.51% vs. 0.41%), PDGFRA (1.75% vs. 0%), PTEN (3.51% vs. 0.41%) and TP53 gene mutations (4.39% vs. 0.61%) was significantly higher among the 114 PCT patients than in the TCGA database (P<0.05). The prevalence of BRAF V600E (80.88% vs. 54.99%), TP53 (7.35% vs. 0.57%) and TSHR gene mutations (2.94% vs. 0%) was significantly higher in classical PTC(CPTC) patients than in the TCGA database, and the prevalence of BRAF V600E (36.84% vs.13.86%) and TERTp gene mutations (10.53% vs. 0%) was significantly higher in follicular variant PTC (FVPTC) patients than in the TCGA database. According to the American Thyroid Association Risk Stratification of Thyroid Cancer Recurrence, the prevalence of BRAF V600E and TP53 gene mutations was 77.14% and 8.57% among moderate-risk CPTC patients, the prevalence of BRAF V600E gene mutation was 27.27% among low-risk FVPTC patients, and the prevalence of TERTp gene mutation was 33.33% among moderate-risk FVPTC patients, which were all higher than in the TCGA database (55.10%, 0%, 3.28%, and 0%, respectively; P<0.05).@*Conclusion@#There are significant differences in the type and rate of somatic gene mutations between 114 PTC patients and the TCGA database.

Advances in indications of anatomical pulmonary segmentectomy for early-stage lung cancer / 中国胸心血管外科临床杂志

@#Whether anatomical segmentectomy can replace lobectomy in the treatment of early-stage lung cancer remains controversial. A large number of studies have been conducted for decades to explore whether pulmonary segmentectomy can treat early-stage lung cancer, which is actually to explore the indications of intentional segment-ectomy. With the development of scientific researches, it is found that many characteristics affect the malignancy of lung cancer, and the different grades of each characteristic affect the prognosis of patients. It is worth exploring whether different surgical approaches can be used for early-stage lung cancer with different characteristics and different grades. This article reviews the literature and studies to discuss the advances in indications of segmentectomy for early-stage lung in terms of tumor size, consolidation-to-tumor ratio, pathological classification and tumor location, respectively. The objective of this review is to help thoracic surgeons to objectively and scientifically select the surgical method according to the clinical characteristics of early-stage lung cancer.

Prognostic factors of malignant peritoneal mesothelioma: a retrospective study of 52 female patients.

BACKGROUND: Prognosis in malignant peritoneal mesothelioma (MPM) remains poor, and the associated factors are unclear. Therefore, this study aimed to investigate the prognostic factors of MPM. METHODS: A total of 52 female MPM patients treated in 2012-2017 were retrospectively analyzed. Kaplan-Meier survival curves were generated for survival analysis by the log-rank test. The Cox regression model was used for univariate and multivariate analyses. RESULTS: Univariate analysis showed that median survival time (MST) was longer in the epithelioid type compared with the sarcomatoid type (12 months vs 5 months); cumulative survival rates at 12 months were 45.7% and 0%, respectively (P=0.005). MST was longer in patients with proliferating cell nuclear antigen (Ki67) ≤ 10% compared with those with Ki67 > 10% (15 months vs 11 months). Cumulative survival rates at 12 months were 60.0% and 28.1%, respectively (P=0.036). MSTs in patients administered peritoneal biopsy or adnexectomy + paclitaxel + platinum perfusion, peritoneal biopsy (or adnexectomy) + pemetrexed + platinum perfusion, cytoreductive surgery + paclitaxel + platinum perfusion, and cytoreductive surgery + pemetrexed + platinum perfusion were 6, 11, 12, and 24 months, respectively, with cumulative survival rates at 12 months of 0%, 35.7%, 45.5%, and 73.3%, respectively. Survival time after cytoreductive surgery combined with pemetrexed + platinum was the longest. In multivariate analysis, pathological type, T staging, and therapeutic regimen were independent prognostic factors of MPM (P < 0.05). CONCLUSIONS: Prognosis in MPM is associated with pathological subtype, clinical staging, cytoreductive surgery, and subsequent pemetrexed use. Radical cytoreductive surgery and postoperative use of pemetrexed prolong survival.

Epidemiological and Clinical Characteristics of Five Rare Pathological Subtypes of Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor with several rare pathological subtypes and which is still poorly understood. This study aimed to describe the epidemiological and clinical spectrum of five rare HCC subtypes and develop a competing risk nomogram for cancer-specific survival prediction. Methods: The study cohort was recruited from the Surveillance, Epidemiology, and End Results database. The clinicopathological data of 50,218 patients histologically diagnosed with classic HCC and five rare subtypes (ICD-O-3 Histology Code = 8170/3-8175/3) between 2004 and 2018 were reviewed. The annual percent change (APC) was calculated utilizing Joinpoint regression. The nomogram was developed based on multivariable competing risk survival analyses. Akaike information criterion, Bayesian information criterion, C-index, calibration curve, and area under the receiver operating characteristic curve were obtained to evaluate the prognostic performance. A decision curve analysis was introduced to examine the clinical value of the models. Results: Despite scirrhous carcinoma, which showed a decreasing trend (APC = -6.8%, P = 0.025), the morbidity of other rare subtypes remained stable from 2004 to 2018. The incidence-based mortality was plateau in all subtypes during the period. Clear cell carcinoma is the most common subtype (n = 551, 1.1%), followed by subtypes of fibrolamellar (n = 241, 0.5%), scirrhous (n = 82, 0.2%), spindle cell (n = 61, 0.1%), and pleomorphic (n = 17, ~0%). The patients with fibrolamellar carcinoma were younger and more likely to have a non-cirrhotic liver and better prognoses. Scirrhous carcinoma shared almost the same macro-clinical characteristics and outcomes as the classic HCC. Clear cell carcinoma tended to occur in the Asia-Pacific elderly male population, and more than half of them were large HCC (Size>5cm). Sarcomatoid (including spindle cell and pleomorphic) carcinoma was associated with a larger tumor size, poorer differentiation, and more dismal prognoses. The pathological subtype, T stage, M stage, surgery, alpha-fetoprotein, and cancer history were confirmed as the independent predictors in patients with rare subtypes. The nomogram showed good calibration, discrimination, and net benefits in clinical practice. Conclusion: The rare subtypes had unique clinicopathological features and biological behaviors compared with the classic HCC. Our findings could provide a valuable reference for clinicians. The constructed nomogram could predict the prognoses with good performance, which is meaningful to individualized management.

Which will carry more weight when CTR > 0.5, solid component size, CTR, tumor size or SUVmax?

BACKGROUND: This study was conducted to explore the clinical significance of the maximum standard uptake value (SUVmax) in the clinical stage IA lung adenocarcinoma with tumor size ≤ 2 cm and consolidation to tumor ratio (CTR) > 0.5. METHODS: We retrospectively reviewed non-small cell lung cancer patients who underwent surgeries between January 2014 and March 2017. Clinical stage IA lung adenocarcinoma patients with tumor of size ≤ 2 cm and CTR > 0.5 were enrolled. The patients were divided into two groups: part-solid and pure-solid based on whether CTR = 1.0 or not. Nodules with any amount of solid or micropapillary components were regarded as the high-risk subtype. Time-dependent ROC curve was used to determine the best cut-off value. Finally, we analyzed the relationship between SUVmax, high-risk subtypes, node metastasis and 5-year relapse-free survival and overall survival. RESULTS: Totally, 270 patients were included. The distribution of pathological subtypes (p < 0.001), SUVmax (p < 0.001), and pathological N stage (p < 0.001) were different between the two groups. Multivariable analysis indicated that SUVmax could predict high-risk subtypes in cases of part-solid nodules (p < 0.001) and both high-risk subtypes (p = 0.022) and node metastasis (p < 0.001) in cases of pure-solid ones. SUVmax ≥ 2.6 and SUVmax ≥ 5.1 were strongly associated with 5-year relapse-free survival (p < 0.001) and 5-year overall survival (p < 0.001) among all the patients, respectively. CONCLUSION: Part-solid nodules with 0.5 < CTR < 1 and pure-solid nodules in lung adenocarcinoma show different clinicopathological characteristics, especially in SUVmax. SUVmax is significantly associated with high-risk subtypes, node metastasis, 5-year relapse-free survival and overall survival.

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone. METHODS: A dataset of chest computed tomography scans containing lung nodules was collected with their pathologic diagnosis from several sources. The dataset was split randomly into training (70%), internal validation (15%), and independent test sets (15%) at the patient level. Two machine learning algorithms were developed, trained, and validated. The first algorithm used the support vector machine model, and the second used deep learning technology: a convolutional neural network. Receiver operating characteristic analysis was used to evaluate the performance of the classification on the test dataset. RESULTS: The support vector machine/convolutional neural network-based models classified nodules into 6 categories resulting in an area under the curve of 0.59/0.65 when differentiating atypical adenomatous hyperplasia versus adenocarcinoma in situ, 0.87/0.86 with minimally invasive adenocarcinoma versus invasive adenocarcinoma, 0.76/0.72 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma, 0.89/0.87 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma + invasive adenocarcinoma, and 0.93/0.92 atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma. Classifying benign versus atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma resulted in a micro-average area under the curve of 0.93/0.94 for the support vector machine/convolutional neural network models, respectively. The convolutional neural network-based methods had higher sensitivities than the support vector machine-based methods but lower specificities and accuracies. CONCLUSIONS: The machine learning algorithms demonstrated reasonable performance in differentiating benign versus preinvasive versus invasive adenocarcinoma from computed tomography images alone. However, the prediction accuracy varies across its subtypes. This holds the potential for improved diagnostic capabilities with less-invasive means.

Artificial intelligence for histological subtype classification of breast cancer: combining multi-scale feature maps and the recurrent attention model.

AIMS: The aim of this study was to apply a two-stage deep model combining multi-scale feature maps and the recurrent attention model (RAM) to assist with the pathological diagnosis of breast cancer histological subtypes by the use of whole slide images (WSIs). METHODS AND RESULTS: In this article, we propose an integrated framework combining multi-scale feature maps from Inception V3 and the recurrent attention model to classify the six histological subtypes of breast cancer. This model was trained with 194 WSIs, and on 63 validation WSIs the model achieved accuracies of 0.9030 for patch-level classification and 0.8889 for WSI-level classification. In the testing stage, a total of 65 WSIs were used to achieve an accuracy of 0.8462 without any form of data curation. The t-distributed stochastic neighbour embedding showed that features extracted by the feature network of the RAM from WSIs of the same category can cluster together after training, and the visualization of decision steps showed that the decision-making glimpses are focused on the middle tumour area of an example from test WSIs. Finally, the false classification patches indicated that the morphological similarities between tumour tissues of different subtypes or non-tumour tissues and tumour tissues in patches might contribute to misclassification. CONCLUSIONS: This model can imitate the diagnostic process of pathologists, pay attention to a series of local features on the pathology image, and summarize related information, in order to accurately classify breast cancer into its histological subtypes, which is important for treatment and prognosis.

Prognostic Nomograms Based on Ground Glass Opacity and Subtype of Lung Adenocarcinoma for Patients with Pathological Stage IA Lung Adenocarcinoma.

The value of lung adenocarcinoma (LUAD) subtypes and ground glass opacity (GGO) in pathological stage IA invasive adenocarcinoma (IAC) has been poorly understood, and reports of their association with each other have been limited. In the current study, we retrospectively reviewed 484 patients with pathological stage IA invasive adenocarcinoma (IAC) at Sun Yat-sen University Cancer Center from March 2011 to August 2018. Patients with at least 5% solid or micropapillary presence were categorized as high-risk subtypes. Independent indicators for disease-free survival (DFS) and overall survival (OS) were identified by multivariate Cox regression analysis. Based on these indicators, we developed prognostic nomograms of OS and DFS. The predictive performance of the two nomograms were assessed by calibration plots. A total of 412 patients were recognized as having the low-risk subtype, and 359 patients had a GGO. Patients with the low-risk subtype had a high rate of GGO nodules (p < 0.001). Multivariate Cox regression analysis showed that the high-risk subtype and GGO components were independent prognostic factors for OS (LUAD subtype: p = 0.002; HR 3.624; 95% CI 1.263-10.397; GGO component: p = 0.001; HR 3.186; 95% CI 1.155-8.792) and DFS (LUAD subtype: p = 0.001; HR 2.284; 95% CI 1.448-5.509; GGO component: p = 0.003; HR 1.877; 95% CI 1.013-3.476). The C-indices of the nomogram based on the LUAD subtype and GGO components to predict OS and DFS were 0.866 (95% CI 0.841-0.891) and 0.667 (95% CI 0.586-0.748), respectively. Therefore, the high-risk subtype and GGO components were potential prognostic biomarkers for patients with stage IA IAC, and prognostic models based on these indicators showed good predictive performance and satisfactory agreement between observational and predicted survival.

Usefulness of the Texture Signatures Based on Multiparametric MRI in Predicting Growth Hormone Pituitary Adenoma Subtypes.

OBJECTIVE: To explore the usefulness of texture signatures based on multiparametric magnetic resonance imaging (MRI) in predicting the subtypes of growth hormone (GH) pituitary adenoma (PA). METHODS: Forty-nine patients with GH-secreting PA confirmed by the pathological analysis were included in this retrospective study. Texture parameters based on T1-, T2-, and contrast-enhanced T1-weighted images (T1C) were extracted and compared for differences between densely granulated (DG) and sparsely granulated (SG) somatotroph adenoma by using two segmentation methods [region of interest 1 (ROI1), excluding the cystic/necrotic portion, and ROI2, containing the whole tumor]. Receiver operating characteristic (ROC) curve analysis was performed to determine the differentiating efficacy. RESULTS: Among 49 included patients, 24 were DG and 25 were SG adenomas. Nine optimal texture features with significant differences between two groups were obtained from ROI1. Based on the ROC analyses, T1WI signatures from ROI1 achieved the highest diagnostic efficacy with an AUC of 0.918, the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 85.7, 72.0, 100.0, 100.0, and 77.4%, respectively, for differentiating DG from SG. Comparing with the T1WI signature, the T1C signature obtained relatively high efficacy with an AUC of 0.893. When combining the texture features of T1WI and T1C, the radiomics signature also had a good performance in differentiating the two groups with an AUC of 0.908. In addition, the performance got in all the signatures from ROI2 was lower than those in the corresponding signature from ROI1. CONCLUSION: Texture signatures based on MR images may be useful biomarkers to differentiate subtypes of GH-secreting PA patients.

Prognostic Analysis of Patients with Primary Extranodal Lymphoma: A Retrospective Study.

BACKGROUND: Originating from extranodal organs or tissues, primary extranodal lymphoma (PENL) acts in different primary sites with diverse clinical performances and PENL has remarkable geographical differences and lacks the relevant reports in each region. PATIENTS AND METHODS: Two hundred and twenty PENL patients were enrolled, and the relevant clinical and laboratory indicators were analyzed. In addition, statistical methods were applied to analyze the effects of different factors on overall survival (OS) and progression-free survival (PFS) of patients. RESULTS: The three most frequent primary sites of PENL are the digestive system, head and neck, and central nervous system. The patients were classified into groups based on their risk status, resulting in low-risk, medium-low-risk, medium-high-risk, and high-risk, and their respective 3-year OS values were calculated, which showed that 121 patients (55%) were in the low-risk group and 3-year OS was 85.2% (25.9% medium-low-risk, 3-year OS 66.6%; 15% medium-high-risk, 3-year OS 61.9%; 4.09% high risk, 3-year OS 45.7%). A multivariate analysis of the Cox regression demonstrated that serum beta 2-microglobulin (ß2-MG) and lactate dehydrogenase (LDH) were independent prognostic factors for OS and PFS, respectively. Both the performance status and pathological subtypes were independent prognostic factors for OS and PFS. CONCLUSION: The correlated independent risk factors such as ß2-MG, LDH, performance status, and pathological subtypes, were helpful for effectively determining the prognosis of PENL patients and guiding treatment.

Prediction of tumour pathological subtype from genomic profile using sparse logistic regression with random effects.

The purpose of this study is to highlight the application of sparse logistic regression models in dealing with prediction of tumour pathological subtypes based on lung cancer patients' genomic information. We consider sparse logistic regression models to deal with the high dimensionality and correlation between genomic regions. In a hierarchical likelihood (HL) method, it is assumed that the random effects follow a normal distribution and its variance is assumed to follow a gamma distribution. This formulation considers ridge and lasso penalties as special cases. We extend the HL penalty to include a ridge penalty (called 'HLnet') in a similar principle of the elastic net penalty, which is constructed from lasso penalty. The results indicate that the HL penalty creates more sparse estimates than lasso penalty with comparable prediction performance, while HLnet and elastic net penalties have the best prediction performance in real data. We illustrate the methods in a lung cancer study.

Clinic diagnostic value of MSCT imaging features in nodular lung adenocarcinoma subtype / 国际肿瘤学杂志

Objective:To investigate the clinic diagnostic value of multi-slice CT (MSCT) imaging features in various subtypes of nodular lung adenocarcinoma.Methods:The imaging information and general clinical data of 160 patients with nodular lung adenocarcinoma who were admitted to Yantai Affiliated Hospital of Binzhou Medical University and received surgical treatment from January 2017 to May 2019 were retrospectively analyzed. Univariate analysis was used to screen statistically significant imaging features of each pathological subtype, and binary logistic regression analysis was performed. The diagnostic value was analyzed using the receiver operating characteristic (ROC) curve, the area under the curve (AUC) was calculated, and the diagnostic efficacy was compared.Results:The age of patients with atypical adenomatous hyperplasia and adenocarcinoma in situ (AAH+ AIS), minimally invasive ademocarcinoma (MIA), invasive adenocarcinoma cancer (IAC) and variant of invasive adenocarcinoma cancer (VIAC) were (57.07±7.92), (59.37±6.96), (60.68±8.83), (63.33±6.89) years old, with no statistically significant difference ( F=1.221, P=0.304). The age of patients with VIAC, IAC, MIA and AAH+ AIS decreased in turn. The imaging features of AAH+ AIS, MIA, IAC and VIAC that exhibited statistically significant differences were as following in turn: the maximum diameter of lesion [6.85 (3.73) mm vs. 8.00 (5.00) mm vs. 16.00 (11.90) mm vs. 17.20 (9.08) mm, H=55.107, P<0.001], CT value [-563.50 (176.63) HU vs. -536.00 (293.50) HU vs. -235.50 (346.50) HU vs. -23.00 (30.50) HU, H=47.499, P<0.001], solid ratio [0 (0) vs. 0 (0) vs. 49.00% (100.00%) vs. 100.00% (0), H=44.242, P<0.001], vacuolar sign [14 (87.50%) vs. 35 (100.00%) vs. 84 (81.55%) vs. 3 (50.00%), χ2=13.925, P=0.002], inflatable bronchus sign [1 (6.25%) vs. 2 (5.71%) vs. 36 (34.95%) vs. 2 (33.33%), χ2=16.578, P=0.001], intratumoral vascular sign [13 (81.25%) vs. 28 (80.00%) vs. 64 (62.14%) vs. 1 (16.67%), χ2=11.168, P=0.009], vessel convergence sign [1 (6.25%) vs. 3 (8.57%) vs. 66 (64.08%) vs. 6 (100.00%), χ2=54.232, P<0.001], short burr sign [3 (18.75%) vs. 11 (31.43%) vs. 77 (74.76%) vs. 6 (100.00%), χ2=36.218, P<0.001], lobulation sign [4 (25.00%) vs. 18 (51.43%) vs. 93 (90.29%) vs. 6 (100.00%), χ2=43.302, P<0.001], pleural traction sign [0 (0) vs. 6 (17.14%) vs. 70 (67.96%) vs. 5 (83.33%), χ2=50.794, P<0.001]. The maximum diameter of lesion ( OR=0.858, 95% CI: 0.754-0.977, P=0.021) and pleural traction sign ( OR=0.288, 95% CI: 0.084-0.993, P=0.049) were independent influencing factors of MIA. The maximum diameter of lesion ( OR=1.131, 95% CI: 1.030-1.241, P=0.010) and pleural traction sign ( OR=3.441, 95% CI: 1.279-9.254, P=0.014) were independent influencing factors of IAC. The optimum threshold of the maximum diameter of lesion in diagnosis of MIA was 11.05 mm, AUC was 0.798 (95% CI: 0.724-0.872) sensitivity was 68.00%, and specificity was 85.70%. The AUC of pleural traction sign in diagnosis of MIA was 0.714 (95% CI: 0.623-0.806). The diagnostic efficacy exhibited no statistically significant difference between the maximum diameter of lesion and pleural traction sign in diagnosis of MIA ( Z=1.838, P=0.066). The optimum threshold of the maximum diameter of lesion in diagnosis of IAC was 11.05 mm, AUC was 0.827 (95% CI: 0.759-0.895), sensitivity was 75.70%, and specificity was 78.90%. The AUC of pleural traction sign in diagnosis of IAC was 0.743 (95% CI: 0.663-0.823). The diagnostic efficacy exhibited statistically significant difference between the maximum diameter of lesion and pleural traction sign in diagnosis of IAC ( Z=2.114, P=0.035), and the maximum diameter of lesion > 11.05 mm was better for the diagnosis of IAC. Conclusion:The maximum diameter of lesion and pleural traction sign are independent influence factors in diagnosis of MIA and IAC, and the maximum diameter of lesion > 11.05 mm is better for the diagnosis of IAC.

Comparative Analysis of the MRI Characteristics of Meningiomas According to the 2016 WHO Pathological Classification.

OBJECTS: To evaluate the performance of preoperative magnetic resonance imaging (MRI) in evaluating diagnoses, operation methods and recurrence of meningiomas according to the World health organization (WHO) pathological classification. METHODS: MRI characteristics of 127 meningioma patients were retrospectively analysed according to pathological results (WHO grade) and their association with Simpson's grades (resection) and recurrence. RESULTS: The T1-weighted imaging (T1WI) signal intensity of WHO grade I meningiomas was slightly hypointense or isointense gray, while the T2-weighted imaging (T2WI) signal intensity was isointense or slightly hyperintense. The T1WI and T2WI signal intensity in WHO grade II and III meningiomas was isointense gray. The enhancement degree and patterns, lobulation, flowing voids, dural tail, maximum diameter, peritumoural oedema, ADC values and margin were significantly different between any 2 grades (P < 0.05). The ADC values were higher for WHO grade I tumors than for WHO grade II and III tumors (P < 0.001). Among all the analyzed characteriscs, ADC values, peritumoural oedema, and margin effectively predicted the diagnosis according to the WHO classification. The operation method and surgical resection were different between WHO grade Ⅰ and WHO grade Ⅱ/Ⅲ meningiomas (P < 0.05). The recurrence rate increased with tumor grade, but there was no statistical difference among the 3 types(P> 0.05). CONCLUSIONS: WHO grades and pathological subtypes of meningiomas can generally be determined based on their MRI characteristics. In addition, MRI provides significant guidance for the grading of surgical success and prognosis.