Tumor-related Features Research Articles

Clinical Nomogram Model for Pre-Operative Prediction of Microvascular Invasion of Hepatocellular Carcinoma before Hepatectomy.

Background and Objectives: Microvascular invasion (MVI) significantly impacts recurrence and survival rates after liver resection in hepatocellular carcinoma (HCC). Pre-operative prediction of MVI is crucial in determining the treatment strategy. This study aims to develop a nomogram model to predict the probability of MVI based on clinical features in HCC patients. Materials and Methods: A total of 489 patients with a pathological diagnosis of HCC were enrolled from our hospital. Those registered from 2012-2015 formed the derivation cohort, and those from 2016-2019 formed the validation cohort for pre-operative prediction of MVI. A nomogram model for prediction was created using a regression model, with risk factors derived from clinical and tumor-related features before surgery. Results: Using the nomogram model to predict the odds ratio of MVI before hepatectomy, the AFP, platelet count, GOT/GPT ratio, albumin-alkaline phosphatase ratio, ALBI score, and GNRI were identified as significant variables for predicting MVI. The Youden index scores for each risk variable were 0.287, 0.276, 0.196, 0.185, 0.115, and 0.112, respectively, for the AFP, platelet count, GOT/GPT ratio, AAR, ALBI, and GNRI. The maximum value of the total nomogram scores was 220. An increase in the number of nomogram points indicated a higher probability of MVI occurrence. The accuracy rates ranged from 55.9% to 64.4%, and precision rates ranged from 54.3% to 68.2%. Overall survival rates were 97.6%, 83.4%, and 73.9% for MVI(-) and 80.0%, 71.8%, and 41.2% for MVI(+) (p < 0.001). The prognostic effects of MVI(+) on tumor-free survival and overall survival were poor in both the derivation and validation cohorts. Conclusions: Our nomogram model, which integrates clinical factors, showed reliable calibration for predicting MVI and provides a useful tool enabling surgeons to estimate the probability of MVI before resection. Consequently, surgical strategies and post-operative care programs can be adapted to improve the prognosis of HCC patients where possible.

Spliceosomic dysregulation in pancreatic cancer uncovers splicing factors PRPF8 and RBMX as novel candidate actionable targets.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal cancer, characterized by late diagnosis and poor treatment response. Surgery is the only curative approach, only available to early-diagnosed patients. Current therapies have limited effects, cause severe toxicities, and minimally improve overall survival. Understanding of splicing machinery alterations in PDAC remains incomplete. Here, we comprehensively examined 59 splicing machinery components, uncovering dysregulation in pre-mRNA processing factor 8 (PRPF8) and RNA-binding motif protein X-linked (RBMX). Their downregulated expression was linked to poor prognosis and malignancy features, including tumor stage, invasion and metastasis, and associated with poorer survival and the mutation of key PDAC genes. Experimental modulation of these splicing factors in pancreatic cancer cell lines reverted their expression to non-tumor levels and resulted in decreased key tumor-related features. These results provide evidence that the splicing machinery is altered in PDAC, wherein PRPF8 and RBMX emerge as candidate actionable therapeutic targets.

Prognostic significance of systemic immune-inflammation index in patients with nonfunction pancreatic neuroendocrine tumor undergoing surgical resection.

The purpose of our study was to investigate the clinical significance and prognostic role of the systemic immune-inflammation index (SII) in patients who underwent surgical resection for nonfunctioning pancreatic neuroendocrine tumors (pNETs). We conducted a retrospective analysis of 364 patients with nonfunctioning pNETs. The association between the SII level and clinical parameters was investigated. The receiver operating characteristic (ROC) curve was used to calculate the optimal SII value. Cox proportional hazard analysis was performed to evaluate the prognostic factors. Our study included 364 patients with nonfunctioning pNETs who underwent surgery. The median age was 51.0 (43.0, 59.3), and 164 (45.1%) were male. The optimal threshold of SII determined by ROC analysis was 523.95. Higher SII levels were significantly associated with older age (p = 0.001), sex (p = 0.011), tumor size (p = 0.032), and tumor grade (p = 0.002). Recurrence was observed in 70 (19.2%) patients following a median follow-up of 98 months. Univariate analysis showed that higher SII (p < 0.0001), tumor size >4 cm (p = 0.015), and G2/G3 grade (p = 0.002) were significantly associated with disease-free survival (DFS). Multivariate analysis revealed that higher SII (HR: 7.35; 95% CI: 3.44, 15.70; p < 0.0001) and G2/G3 grade (HR: 3.11; 95% CI: 1.42, 6.82; p = 0.005) remained significantly associated with tumor recurrence. Furthermore, 46 (12.6%) patients died during the follow-up. Higher SII (HR: 8.43; 95% CI: 3.19, 22.72; p < 0.0001) and G2/G3 grade (HR: 3.16; 95% CI: 1.01, 9.86; p = 0.048) were independent predictors of overall survival (OS) by multivariate analysis. In conclusion, our study revealed that a higher SII level was associated with tumor-related features (larger tumor size and advanced grade) and subsequent shorter DFS and OS in patients with nonfunctioning pNETs. These results indicated that the SII could serve as an efficient prognostic biomarker for nonfunctioning pNETs.

Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions

The work presented in this paper is in the area of brain tumor detection. We propose a fast detection system with 3D MRI scans of Flair modality. It performs 2 functions, predicting the gray level distribution and location distribution of the pixels in the tumor regions and generating tumor masks with pixel-wise precision. To facilitate 3D data analysis and processing, we introduce a 2D histogram presentation encompassing the gray-level distribution and pixel-location distribution of a 3D object. In the proposed system, specific 2D histograms highlighting tumor-related features are established by exploiting the left-right asymmetry of a brain structure. A modulation function, generated from the input data of each patient case, is applied to the 2D histograms to transform them into coarsely or finely predicted distributions of tumor pixels. The prediction result helps to identify/remove tumor-free slices. The prediction and removal operations are performed to the axial, coronal and sagittal slice series of a brain image, transforming it into a 3D minimum bounding box of its tumor region. The bounding box is utilized to finalize the prediction and generate a 3D tumor mask. The proposed system has been tested extensively with the data of more than 1200 patient cases in BraTS2018∼2021 datasets. The test results demonstrate that the predicted 2D histograms resemble closely the true ones. The system delivers also very good tumor detection results, comparable to those of state-of-the-art CNN systems with mono-modality inputs. They are reproducible and obtained at an extremely low computation cost and without need for training.

Multimodal Brain Tumor Segmentation Boosted by Monomodal Normal Brain Images.

Many deep learning based methods have been proposed for brain tumor segmentation. Most studies focus on deep network internal structure to improve the segmentation accuracy, while valuable external information, such as normal brain appearance, is often ignored. Inspired by the fact that radiologists often screen lesion regions with normal appearance as reference in mind, in this paper, we propose a novel deep framework for brain tumor segmentation, where normal brain images are adopted as reference to compare with tumor brain images in a learned feature space. In this way, features at tumor regions, i.e., tumor-related features, can be highlighted and enhanced for accurate tumor segmentation. It is known that routine tumor brain images are multimodal, while normal brain images are often monomodal. This causes the feature comparison a big issue, i.e., multimodal vs. monomodal. To this end, we present a new feature alignment module (FAM) to make the feature distribution of monomodal normal brain images consistent/inconsistent with multimodal tumor brain images at normal/tumor regions, making the feature comparison effective. Both public (BraTS2022) and in-house tumor brain image datasets are used to evaluate our framework. Experimental results demonstrate that for both datasets, our framework can effectively improve the segmentation accuracy and outperforms the state-of-the-art segmentation methods. Codes are available at https://github.com/hb-liu/Normal-Brain-Boost-Tumor-Segmentation.

Transitioning from Da Vinci Si to Xi: assessing surgical outcomes at a high-volume robotic center.

In the emerging field of robotics, only few studies investigated the transition between different robotic platforms in terms of surgical outcomes. We aimed at assessing surgical outcomes of patients receiving robot-assisted radical prostatectomy (RARP) and robot-assisted partial nephrectomy (RAPN) at a high-volume robotic center during the transition from Si to Xi Da Vinci surgical systems. We analyzed data of 1884 patients undergoing RARP (n = 1437, 76%) and RAPN (n = 447, 24%) at OLV hospital (Aalst, Belgium) between 2011 and 2021. For both procedures, we assessed operative time, estimated blood loss, length of stay, and positive surgical margins. For RARP, we investigated length of catheterization and PSA persistence after surgery, whereas warm ischemia time, clampless surgery, and acute kidney injury (AKI) were assessed for RAPN. Multivariable analyses (MVA) investigated the association between robotic platform (Si vs. Xi) and surgical outcomes after adjustment for patient- and tumor-related factors. A total of 975 (68%) and 462 (32%) patients underwent RARP performed with the Si vs. Xi surgical system, respectively. Baseline characteristics did not differ between the groups. On MVA, we did not find evidence of a difference between the groups with respect to operative time (estimate: 1.07) or estimated blood loss (estimate: 32.39; both p > 0.05). Median (interquartile range [IQR]) length of stay was 6 (3, 6) and 4 (3, 5) days in the Si vs. Xi group, respectively (p < 0.0001). On MVA, men treated with the Xi vs. Si robot had lower odds of PSM (Odds ratio [OR]: 0.58; p = 0.014). A total of 184 (41%) and 263 (59%) patients received RAPN with the Si and Xi robotic system, respectively. Baseline characteristics, including demographics, functional data, and tumor-related features did not differ between the groups. On MVA, operative time was longer in the Xi vs. Si group (estimate: 30.54; p = 0.006). Patients treated with the Xi vs. Si system had higher probability of undergoing a clampless procedure (OR: 2.56; p = 0.001), whereas the risk of AKI did not differ between the groups (OR: 1.25; p = 0.4). On MVA, patients operated with the Xi robot had shorter length of stay as compared to the Si group (estimate: -0.86; p = 0.003), whereas we did not find evidence of an association between robotic system and PSM (OR: 1.55; p = 0.3). We found that the Xi robot allowed for improvements in peri-operative outcomes as compared to the Si platform, with lower rate of positive margins for RARP and higher rate of off-clamp procedures for RAPN. Hospital stay was also shorter for patients operated with the Xi vs. Si robot, especially after robot-assisted partial nephrectomy. Awaiting future investigations-in particular, cost analyses-these results have important implications for patients, surgeons, and healthcare policymakers.

Multi-Modal Learning for Predicting the Genotype of Glioma.

The isocitrate dehydrogenase (IDH) gene mutation is an essential biomarker for the diagnosis and prognosis of glioma. It is promising to better predict glioma genotype by integrating focal tumor image and geometric features with brain network features derived from MRI. Convolutional neural networks show reasonable performance in predicting IDH mutation, which, however, cannot learn from non-Euclidean data, e.g., geometric and network data. In this study, we propose a multi-modal learning framework using three separate encoders to extract features of focal tumor image, tumor geometrics and global brain networks. To mitigate the limited availability of diffusion MRI, we develop a self-supervised approach to generate brain networks from anatomical multi-sequence MRI. Moreover, to extract tumor-related features from the brain network, we design a hierarchical attention module for the brain network encoder. Further, we design a bi-level multi-modal contrastive loss to align the multi-modal features and tackle the domain gap at the focal tumor and global brain. Finally, we propose a weighted population graph to integrate the multi-modal features for genotype prediction. Experimental results on the testing set show that the proposed model outperforms the baseline deep learning models. The ablation experiments validate the performance of different components of the framework. The visualized interpretation corresponds to clinical knowledge with further validation. In conclusion, the proposed learning framework provides a novel approach for predicting the genotype of glioma.

Architecture and topologies of gene regulatory networks associated with breast cancer, adjacent normal, and normal tissues.

Most cancer studies employ adjacent normal tissues to tumors (ANTs) as controls, which are not completely normal and represent a pre-cancerous state. However, the regulatory landscape of ANTs compared to tumor and non-tumor-bearing normal tissues is largely unexplored. Among cancers, breast cancer is the most commonly diagnosed cancer and a leading cause of death in women worldwide, with a lack of sufficient treatment regimens for various reasons. Hence, we aimed to gain deeper insights into normal, pre-cancerous, and cancerous regulatory systems of breast tissues towards identifying ANT and subtype-specific candidate genes. For this, we constructed and analyzed eight gene regulatory networks (GRNs), including five subtypes (viz., Basal, Her2, Luminal A, Luminal B, and Normal-Like), one ANT, and two normal tissue networks. Whereas several topological properties of these GRNs enabled us to identify tumor-related features of ANT, escape velocity centrality (EVC+) identified 24 functionally significant common genes, including well-known genes such as E2F1, FOXA1, JUN, BRCA1, GATA3, ERBB2, and ERBB3 across all six tissues including subtypes and ANT. Similarly, the EVC+ also helped us to identify tissue-specific key genes (Basal: 18, Her2: 6, Luminal A: 5, Luminal B: 5, Normal-Like: 2, and ANT: 7). Additionally, differentially correlated switching gene pairs along with functional, pathway, and disease annotations highlighted the cancer-associated role of these genes. In a nutshell, the present study revealed ANT and subtype-specific regulatory features and key candidate genes, which can be explored further using in vitro and in vivo experiments for better and effective disease management at an early stage.

An MRI-based grading system for preoperative risk estimation of positive surgical margin after radical prostatectomy

ObjectiveTo construct a simplified grading system based on MRI features to predict positive surgical margin (PSM) after radical prostatectomy (RP).MethodsPatients who had undergone prostate MRI followed by RP between January 2017 and January 2021 were retrospectively enrolled as the derivation group, and those between February 2021 and November 2022 were enrolled as the validation group. One radiologist evaluated tumor-related MRI features, including the capsule contact length (CCL) of lesions, frank extraprostatic extension (EPE), apex abutting, etc. Binary logistic regression and decision tree analysis were used to select risk features for PSM. The area under the curve (AUC), sensitivity, and specificity of different systems were calculated. The interreader agreement of the scoring systems was evaluated using the kappa statistic.ResultsThere were 29.8% (42/141) and 36.4% (32/88) of patients who had PSM in the derivation and validation cohorts, respectively. The first grading system was proposed (mrPSM1) using two imaging features, namely, CCL ≥ 20 mm and apex abutting, and then updated by adding frank EPE (mrPSM2). In the derivation group, the AUC was 0.705 for mrPSM1 and 0.713 for mrPSM2. In the validation group, our grading systems showed comparable AUC with Park et al.’s model (0.672–0.686 vs. 0.646, p > 0.05) and significantly higher specificity (0.732–0.750 vs. 0.411, p < 0.001). The kappa value was 0.764 for mrPSM1 and 0.776 for mrPSM2. Decision curve analysis showed a higher net benefit for mrPSM2.ConclusionThe proposed grading systems based on MRI could benefit the risk stratification of PSM and are easily interpretable.Critical relevance statementThe proposed mrPSM grading systems for preoperative prediction of surgical margin status after radical prostatectomy are simplified compared to a previous model and show high specificity for identifying the risk of positive surgical margin, which might benefit the management of prostate cancer.Key points• CCL ≥ 20 mm, apex abutting, and EPE were important MRI features for PSM.• Our proposed MRI-based grading systems showed the possibility to predict PSM with high specificity.• The MRI-based grading systems might facilitate a structured risk evaluation of PSM.Graphical

Diffusion MRI-based connectomics features improve the noninvasive prediction of H3K27M mutation in brainstem gliomas

PurposeTo establish an individualized predictive model to identify patients with brainstem gliomas (BSGs) at high risk of H3K27M mutation, with the inclusion of brain structural connectivity analysis based on diffusion MRI (dMRI). Materials and MethodsA primary cohort of 133 patients with BSGs (80 H3K27M-mutant) were retrospectively included. All patients underwent preoperative conventional MRI and dMRI. Tumor radiomics features were extracted from conventional MRI, while two kinds of global connectomics features were extracted from dMRI. A machine learning-based individualized H3K27M mutation prediction model combining radiomics and connectomics features was generated with a nested cross validation strategy. Relief algorithm and SVM method were used in each outer LOOCV loop to select the most robust and discriminative features. Additionally, two predictive signatures were established using the LASSO method, and simplified logistic models were built using multivariable logistic regression analysis. An independent cohort of 27 patients was used to validate the best model. Results35 tumor-related radiomics features, 51 topological properties of brain structural connectivity networks, and 11 microstructural measures along white matter tracts were selected to construct a machine learning-based H3K27M mutation prediction model, which achieved an AUC of 0.9136 in the independent validation set. Radiomics- and connectomics-based signatures were generated and simplified combined logistic model was built, upon which derived nomograph achieved an AUC of 0.8827 in the validation cohort. ConclusiondMRI is valuable in predicting H3K27M mutation in BSGs, and connectomics analysis is a promising approach. Combining multiple MRI sequences and clinical features, the established models have good performance.

Identification of different types of tumors based on photoacoustic spectral analysis: preclinical feasibility studies on skin tumors.

Collagen and lipid are important components of tumor microenvironments (TME) and participates in tumor development and invasion. It has been reported that collagen and lipid can be used as a hallmark to diagnosis and differentiate tumors. We aim to introduce photoacoustic spectral analysis (PASA) method that can provide both the content and structure distribution of endogenous chromophores in biological tissues to characterize the tumor-related features for identifying different types of tumors. Ex vivo human tissues with suspected squamous cell carcinoma (SCC), suspected basal cell carcinoma (BCC), and normal tissue were used in this study. The relative lipid and collagen contents in the TME were assessed based on the PASA parameters and compared with histology. Support vector machine (SVM), one of the simplest machine learning tools, was applied for automatic skin cancer type detection. The PASA results showed that the lipid and collagen levels of the tumors were significantly lower than those of the normal tissue, and there was a statistical difference between SCC and BCC (), consistent with the histopathological results. The SVM-based categorization achieved diagnostic accuracies of 91.7% (normal), 93.3% (SCC), and 91.7% (BCC). We verified the potential use of collagen and lipid in the TME as biomarkers of tumor diversity and achieved accurate tumor classification based on the collagen and lipid content using PASA. The proposed method provides a new way to diagnose tumors.

The significance of peri-neural invasion in patients with gallbladder carcinoma after curative surgery: a 10year experience in China.

The significance of peri-neural invasion (PNI) in patients with gallbladder carcinoma (GBC) after curative surgery remains unknown. Current study was performed to evaluate the significance of PNI in resected GBC patients in terms of tumor-related biological features and long-term survival. Patients with GBC between September 2010 and September 2020 were reviewed and analyzed. SPSS 25.0 software were used for statistical analysis. A total of 324 resected GBC patients were identified (No. PNI: 64). An elevated preoperative Ca19.9 level (P = 0.001), obstructive jaundice (P = 0.001), liver invasion (P < 0.0001), lymph-vascular invasion (P < 0.0001), lymph-node metastasis (P < 0.0001), and poor or moderate differentiation status (P = 0.036) were more frequently detected in patients with PNI. Major hepatectomy (P = 0.019), bile duct resection (P < 0.0001), combined multi-visceral resections (P = 0.001), and combined major vascular resections and reconstructions (P = 0.002) were also more frequently detected. However, a significantly lower R0 rate (P < 0.0001) was acquired in patients with PNI. Patients with PNI were generally more advanced disease and shared a much worse prognosis even after matching. PNI was an independent prognostic factor of disease-free survival as well as an independent predictor of early recurrence. Postoperative adjuvant chemotherapy has brought an obvious survival benefit in resected GBC patients with PNI. PNI could be regarded as an indicator of worse prognosis and could serve as an independent predictor of early recurrence. Postoperative adjuvant chemotherapy was correlated with an improved survival for resected GBC patients with PNI. Upcoming multicenter studies covering various races are warranted for further validation.

A Decision-tree Approach to Stratify DLBCL Risk Based on Stromal and Immune Microenvironment Determinants.

A Decision-tree Approach to Stratify DLBCL Risk Based on Stromal and Immune Microenvironment Determinants.

Urinary Continence Recovery after Robotic Radical Prostatectomy without Anterior or Posterior Reconstruction: Experience from a Tertiary Referral Center

The aim of our study is to evaluate the prevalence and predictive factors of short- (30 d) and mid-term continence in a contemporary cohort of patients treated with robotic-assisted laparoscopic prostatectomy (RALP) without any posterior or anterior reconstruction at our referral academic center. Data from patients undergoing RALP between January 2017 and March 2021 were prospectively collected. RALP was performed by three highly experienced surgeons following the principles of the Montsouris technique, with a bladder-neck-sparing intent and maximal preservation of the membranous urethra (if oncologically safe) without any anterior/posterior reconstruction. (Self-assessed urinary incontinence (UI) was defined as the need of one or more pads per die (excluding the need for a safety pad/die. Univariable and multivariable logistic regression analysis was used to assess the independent predictors of early incontinence among routinely collected patient- and tumor-related variables). A total of 925 patients were included; of these, 353 underwent RALP (38.2%) without nerve-sparing intent. The median patient age and BMI were 68 years (IQR 63-72) and 26 (IQR 24.0-28.0), respectively. Overall, 159 patients (17.2%) reported early (30 d) incontinence. In multivariable analysis adjusting for patient- and tumor-related features, a non-nerve-sparing procedure (OR: 1.57 [95% CI: 1.03-2.59], p = 0.035) was independently associated with the risk of urinary incontinence in the short-term period, while the absence of cardiovascular diseases before surgery (OR: 0.46 [95% CI: 0.320.67], p ≤ 0.01) was a protective factor for this outcome. At a median follow-up of 17 months (IQR 10-24), 94.5% of patients reported to be continent. In experienced hands, most patients fully recover urinary continence after RALP at mid-term follow-up. On the contrary, the proportion of patients who reported early incontinence in our series was modest but not negligible. The implementation of surgical techniques advocating anterior and/or posterior fascial reconstruction might improve the early continence rate in candidates for RALP.

The Interplay between Age and Viral Status in EBV-Related Nasopharyngeal and HPV-Related Oropharyngeal Carcinoma Patients.

The aim of this work was to analyze the interplay between age and viral status on the outcomes in loco-regionally advanced oropharyngeal and nasopharyngeal cancer patients treated with radiotherapy and different chemotherapy combinations. A retrospective (2006-2017) analysis was performed on non-metastatic loco-regionally advanced oropharyngeal (both HPV+ and HPV-) and EBV+ nasopharyngeal cancer patients (young: &lt;65 years vs. elderly: ≥65 years) treated with radiotherapy with or without chemotherapy. The impact of age and viral status on overall (OS) and disease-free survival (DFS) were studied with multivariable models, which were adjusted for smoking, stage, comorbidities, chemotherapy dose intensity and treatment strategy. We analyzed 324 patients (146 HPV+ oropharynx, 63 HPV-, 115 nasopharynx). Elderly patients had more comorbidities, and received less intensive treatments when compared to younger subjects. Although OS and DFS were shorter in older patients, after adjustment for stage, smoking, comorbidities, treatment strategy and dose intensity, no significant differences in terms of survival were observed according to age (65 vs. 50 years of age: HR 1.89, 95% CI 0.45-7.84 for HPV+ OPC; HR 0.91, 95% CI 0.29-2.89 for HPV- OPC; HR 1.99, 95% CI 0.9-4.39 for NPC; p = 0.395). Several potential age-related (comorbidities, treatment intensity) and disease-related (stage) confounding factors play a prognostic role with differential impacts on both virus and non-virus-related tumors. In HPV+ oropharyngeal cancer and in EBV+ nasopharyngeal cancer patients, age should be considered as the expression of an array of host- and tumor-related features rather than an independent prognostic factor.

CT radiomics-based long-term survival prediction for locally advanced non-small cell lung cancer patients treated with concurrent chemoradiotherapy using features from tumor and tumor organismal environment

BackgroundDefinitive concurrent chemoradiotherapy (CCRT) is the standard treatment for locally advanced non-small cell lung cancer (LANSCLC) patients, but the treatment response and survival outcomes varied among these patients. We aimed to identify pretreatment computed tomography-based radiomics features extracted from tumor and tumor organismal environment (TOE) for long-term survival prediction in these patients treated with CCRT.MethodsA total of 298 eligible patients were randomly assigned into the training cohort and validation cohort with a ratio 2:1. An integrated feature selection and model training approach using support vector machine combined with genetic algorithm was performed to predict 3-year overall survival (OS). Patients were stratified into the high-risk and low-risk group based on the predicted survival status. Pulmonary function test and blood gas analysis indicators were associated with radiomic features. Dynamic changes of peripheral blood lymphocytes counts before and after CCRT had been documented.ResultsNine features including 5 tumor-related features and 4 pulmonary features were selected in the predictive model. The areas under the receiver operating characteristic curve for the training and validation cohort were 0.965 and 0.869, and were reduced by 0.179 and 0.223 when all pulmonary features were excluded. Based on radiomics-derived stratification, the low-risk group yielded better 3-year OS (68.4% vs. 3.3%, p < 0.001) than the high-risk group. Patients in the low-risk group had better baseline FEV1/FVC% (96.3% vs. 85.9%, p = 0.046), less Grade ≥ 3 lymphopenia during CCRT (63.2% vs. 83.3%, p = 0.031), better recovery of lymphopenia from CCRT (71.4% vs. 27.8%, p < 0.001), lower incidence of Grade ≥ 2 radiation-induced pneumonitis (31.6% vs. 53.3%, p = 0.040), superior tumor remission (84.2% vs. 66.7%, p = 0.003).ConclusionPretreatment radiomics features from tumor and TOE could boost the long-term survival forecast accuracy in LANSCLC patients, and the predictive results could be utilized as an effective indicator for survival risk stratification. Low-risk patients might benefit more from radical CCRT and further adjuvant immunotherapy.Trial registration:retrospectively registered.

HBx and YAP expression could promote tumor development and progression in HBV-related hepatocellular carcinoma

BackgroundHepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) accounts for 10%–20% of the total HCC numbers. Its clinical features include the occurrence in the younger generation, large tumors, and poor prognosis. The contribution of hepatitis B virus X (HBx) protein in hepatocytes during activation of various oncogenic pathways has been reported. We aimed to assess the possible association between HBx and Yes-associated protein (YAP) expression in the liver tissue and the clinical features of HBV-related HCC. MethodsThe relationship between HBx and YAP expression was examined in vivo using HCC tumor and peritumor tissues (n = 55). The clinical information including tumor size, marker, and the prognosis was assessed with protein expressions. The in vitro gene expression analyses were conducted using HBx- and YAP-overexpressing HCC cell lines. ResultsAmong 19 cases of HBV-related, 17 cases of hepatitis C virus (HCV)-related, and 19 cases of nonviral-related HCC, the HBV-related tumor showed the largest size. The HBx-stained area in the tumor and peritumor tissue showed a significant correlation with tumor size and serum α-fetoprotein level. YAP expression was higher in HBV-related tumor tissue than in the peritumor tissue and HCV-related tumor. Additionally, HBx and YAP protein expressions are correlated and both expressions in the tumor contributed to the poor prognosis. An in vitro study demonstrated that HBx and YAP overexpression in the hepatocytes activate the various oncogenic signaling pathways. ConclusionsOur study demonstrated that YAP expression in the liver of HBV-infected patients might be the key factor in HBV-related HCC development and control of tumor-related features.

SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.

Reliable nasopharyngeal carcinoma (NPC) segmentation plays an important role in radiotherapy planning. However, recent deep learning methods fail to achieve satisfactory NPC segmentation in magnetic resonance (MR) images, since NPC is infiltrative and typically has a small or even tiny volume with indistinguishable border, making it indiscernible from tightly connected surrounding tissues from immense and complex backgrounds. To address such background dominance problems, this paper proposes a sequential method (SeqSeg) to achieve accurate NPC segmentation. Specifically, the proposed SeqSeg is devoted to solving the problem at two scales: the instance level and feature level. At the instance level, SeqSeg is forced to focus attention on the tumor and its surrounding tissue through the deep Q-learning (DQL)-based NPC detection model by prelocating the tumor and reducing the scale of the segmentation background. Next, at the feature level, SeqSeg uses high-level semantic features in deeper layers to guide feature learning in shallower layers, thus directing the channel-wise and region-wise attention to mine tumor-related features to perform accurate segmentation. The performance of our proposed method is evaluated by extensive experiments on the large NPC dataset containing 1101 patients. The experimental results demonstrated that the proposed SeqSeg not only outperforms several state-of-the-art methods but also achieves better performance in multi-device and multi-center datasets.

Patterns of Pretreatment Diagnostic Assessment in Patients Treated with Stereotactic Body Radiation Therapy (SBRT) for Non-Small Cell Lung Cancer (NSCLC): Special Characteristics in the COVID Pandemic and Influence on Outcomes.

The pandemic raised a discussion about the postponement of medical interventions for non-small cell lung cancer (NSCLC). We analyzed the characteristics of pretreatment diagnostic assessment in the pandemic and the influence of diagnostic assessment on outcomes. A total of 96 patients with stereotactic body radiation therapy (SBRT) for NSCLC were included. The number of patients increased from mean 0.9 (2012–2019) to 1.45 per month in the COVID era (p < 0.05). Pandemic-related factors (contact reduction, limited intensive care unit resources) might have influenced clinical decision making towards SBRT. The time from pretreatment assessment (multidisciplinary tumor board decision, bronchoscopy, planning CT) to SBRT was longer during the COVID period (p < 0.05). Reduced services, staff shortage, or appointment management to mitigate infection risks might explain this finding. Overall survival, progression-free survival, locoregional progression-free survival, and distant progression-free survival were superior in patients who received a PET/CT scan prior to SBRT (p < 0.05). This supports that SBRT guidelines advocate the acquisition of a PET/CT scan. A longer time from PET/CT scan/conventional staging to SBRT (<10 vs. ≥10 weeks) was associated with worse locoregional control (p < 0.05). The postponement of diagnostic or therapeutic measures in the pandemic should be discussed cautiously. Patient- and tumor-related features should be evaluated in detail.

Surgical Management of Synchronous, Bilateral Renal Masses: A 1-decade Referral Center Experience

BackgroundManagement and decision-making in patients with bilateral renal masses are controversial. ObjectiveTo report our experience of surgical management in patients with bilateral renal masses undergoing surgery at a high-volume center. Design, setting, and participantsWe retrospectively collected data from patients treated with partial nephrectomy (PN) or radical nephrectomy for bilateral renal masses at a single referral institution between June 2008 and June 2019. Patient- and tumor-related features, timing (one vs two stage), and surgical approach (open vs robotic) were analyzed. Surgical procedureA one- versus two-stage strategy was adopted according to the opportunity to perform at least one PN using a clampless or selective-clamping approach, in order to avoid acute kidney injury. MeasurementsOperative time, warm ischemia time, and intra- and postoperative complications were recorded. Histopathological results and tumor histology were assessed. Results and limitationsOverall, 41 patients were included. The median age was 67 yr and the median preoperative estimated glomerular filtration rate (eGFR) was 84 ml/min/1.73 m2. The median Preoperative Aspects and Dimensions Used for an Anatomical (PADUA) score was 8 (interquartile range [IQR] 7–8) for both sides. In 17 (42%) patients, a simultaneous approach was chosen, with a pure robotic approach in 11/17 cases, while among the 24 (58.6%) patients treated with a two-stage strategy, 15 (62.5%) were treated with a robotic approach on both sides. Intraoperative complications and postoperative major (CDC ≥3) complications were recorded in 7.3% and 4.9% of cases, respectively. The overall positive surgical margins rate was 2.4%. At a median follow-up of 42 (IQR 18–59) mo, the median eGFR was 73 (IQR 64–80) ml/min/1.73 m2, while disease-free survival and cancer-specific mortality were 90.2% and 7.3%, respectively. ConclusionsOur experience underlines that both simultaneous and staged surgical treatment of patients with bilateral renal masses are feasible and safe if grounded on proper patient selection. Patient summaryManagement of patients with bilateral renal masses is challenging, given the heterogeneity of clinical scenarios and the need to optimize the timing of treatment to achieve maximal functional preservation while ensuring oncological efficacy.