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.

Nomogram predicts cervical lymph node metastasis of pathological subtypes of papillary thyroid carcinoma.

BACKGROUND: Pathological subtypes of papillary thyroid carcinoma (PTC) are important factors in thyroid cancer. Some rare subtypes exhibit extensive lymph node metastasis. These pathological subtypes should receive more attention in clinical practice. METHODS: Patients with different pathological subtypes of PTC were selected from the SEER database. Logistic regression, random forest, and bootstrap aggregating (bagging) methods were employed to screen for risk factors associated with cervical lymph node metastasis in the training cohort. A nomogram was established based on the model with the largest area under the curve (AUC) and evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical benefit to patients. The nomogram was validated in depth by 200 iterations of tenfold cross-validation. RESULTS: A total of 7,882 patients were included in the analysis, with 5,516 patients in the training group and 2,366 patients in the testing group. The logistic regression model achieved the highest AUC of 0.7396. Sex, age, race, extension (extrathyroidal extension), pathological type, and primary tumour size were identified as independent risk factors for cervical lymph node metastasis (p < 0.05). The calibration curve indicated that the model was well calibrated. DCA indicated that the nomogram model had good clinical practicability. CONCLUSION: In clinical practice, it is important to consider the pathological subtypes of PTC. The established nomogram can serve as a predictive tool for assessing cervical lymph node metastasis.

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.

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 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.

Prognostic analysis of primary mucin-producing adenocarcinoma of the lung: a comprehensive retrospective study.

Although primary mucin-producing adenocarcinoma of the lung is uncommon, each subtype has distinct clinical, pathological, molecular, and prognostic characteristics. This study aimed to determine correlations between clinical and pathological features and genetic phenotypes with the prognosis. We immunohistochemically examined the protein levels of thyroid transcription factor 1 (TTF-1), Napsin A, and anaplastic lymphoma kinase (ALK) and genetically examined epidermal growth factor receptor (EGFR) and KRAS mutations in these mucin-producing tumors. A total of 75 cases of mucin-producing adenocarcinoma of the lung were examined. ALK protein positivity was 33.3 % (25/75), and primarily occurred in solid predominant with mucin production subtype (SA). KRAS mutations occurred in 22.7 % (17/75) of patients, predominantly in invasive mucinous adenocarcinoma (IMA). Positive TTF-1 and Napsin A expression was more common in SA, while they were both negative in IMA. The 1-, 3-, and 5-year progression-free survival rates of mucin-producing lung adenocarcinoma were 85, 64, and 38 %, respectively; the overall survival rates were 90, 67, and 50 %, respectively. Larger tumors, advanced stage, and lymph node metastasis were associated with poor prognosis. Mucinous minimally invasive adenocarcinoma (m-MIA) had the best prognosis, followed by IMA, SA, and acinar or papillary predominant adenocarcinoma with mucin production (A/P). KRAS mutations were an independent positive prognostic factor for postoperative progress.

Endoscopic and clinical analysis of primary T-cell lymphoma of the gastrointestinal tract according to pathological subtype.

BACKGROUND AND AIM: Little is known about the clinicopathological characteristics of primary gastrointestinal T-cell lymphomas (PGITL). This study evaluated the clinical and endoscopic features of the pathological subtypes of PGITL. METHODS: Forty-two lesions in 36 patients with PGITL were assessed, including 15 enteropathy-associated T-cell lymphomas (EATL), 13 peripheral T-cell lymphomas (PTCL), 10 NK/T-cell lymphomas (NK/TL), and four anaplastic large cell lymphomas (ALCL). RESULTS: PTCL occurred more frequently in the stomach and duodenum and NK/TL more frequently in the small and large intestines (P = 0.009). The endoscopic features of the four subtypes were similar (P = 0.124). Fifteen of 41 lesions (36.6%) were Epstein-Barr virus (EBV) positive, with NK/TL more likely to be EBV positive than the other types (P < 0.001). First endoscopy and first computed tomography (CT) scan indicated that 65.4% and 51.4% of the lesions, respectively, were malignant, and that 43.2% and 42.3%, respectively, were GI lymphomas. The two modalities together correctly diagnosed about half of the lesions before biopsy. Intestinal perforation was associated with small bowel location (P < 0.001) and infiltrative type (P = 0.009), and was more common in NK/TL than in the other subtypes (P = 0.015). Multivariate analysis showed that higher international prognosis index (P = 0.008) and the presence of complications (P = 0.006) were associated with poor prognosis. Survival was poorer in patients with small bowel lesions than with lesions at other locations (P = 0.048). CONCLUSIONS: The four main pathological types of PGITL differed in clinical characteristics. As PGITL was often not diagnosed by initial endoscopic or radiological examination, a high index of suspicion is necessary to ensure its early diagnosis.

Impact of lung adenocarcinoma subtypes on survival and timing of brain metastases.

Purpose: Lung cancer is a devastating disease, with brain metastasis being one of the most common distant metastases of lung adenocarcinoma. This study aimed to investigate the prognostic characteristics of individuals with brain metastases originating from invasive lung adenocarcinoma of distinct pathological subtypes, providing a reference for the management of these patients. Methods: Clinical data from 156 patients with lung adenocarcinoma-derived brain metastases were collected, including age, sex, smoking status, Karnofsky Performance Status scores, pathological subtype, lymph node metastasis, tumor site, treatment mode, T stage, and N stage. Patients were classified into two groups (highly differentiated and poorly differentiated) based on their pathological subtypes. Propensity score matching was used to control for confounding factors. The prognostic value of pathological subtypes was assessed using Kaplan-Meier analysis and Cox proportional hazards regression modeling. Results: Kaplan-Meier analysis indicated that patients in the moderately to highly differentiated group had better prognoses. Multivariate analysis revealed that being in the poorly differentiated group was a risk factor for poorer prognosis. Thoracic tumor radiation therapy, chemotherapy, and surgery positively influenced the time interval between lung cancer diagnosis and brain metastasis. Conclusions: The pathological subtypes of lung adenocarcinoma-derived brain metastases are associated with patient prognosis. Patients in the poorly differentiated group have worse prognoses compared to those in the moderately to highly differentiated group. Therefore, patients in the poorly differentiated group may require more frequent follow-ups and aggressive treatment.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Correlation between computed tomography imaging and pathological stages and subtypes in early lung adenocarcinoma.

BACKGROUND: Detection of early-stage lung cancers has increased due to computed tomography (CT). The pathological stages and subtypes of early lung cancer determine the treatment strategy. We aimed to investigate the correlation between CT characteristics and pathological status in early lung adenocarcinoma (ADC). SUBJECTS AND METHODS: Between June 2018 and December 2019, 415 consecutive patients who underwent surgery for lung ADC with pathological atypical adenomatous hyperplasia (AAH) and ADC in situ (AIS), T1a (mi) N0M0, and T1a-cN0M0 were analyzed. The relationship between CT imaging and pathological status was investigated using Chi-squared or Kruskal-Wallis test and binary logistic regression. RESULTS: When cases of AAH, AIS, and T1a (mi) N0M0 were used as the control group, the lesion size, solid component ratio (SCR), and spiculation were significantly and independently associated with T1a-cN0M0 (P < 0.01). SCR >50% (P < 0.01) and spiculation (P < 0.05) were significantly and independently associated with T1aN0M0. In cases of pathological T1a-cN0M0, SCR >50% was significantly different between adherent wall growth ADC and mucinous ADC (P < 0.01). CONCLUSIONS: Some CT characteristics are related to the pathological stage and subtypes of early lung ADC. Larger diameter, spiculation, and SCR >50% are associated with invasive ADC. SCR >50% is positively correlated with mucinous ADC and negatively with adherent growth ADC.