The prognostic value of radiogenomics using CT in patients with lung cancer: a systematic review.

This systematic review aimed to evaluate the effectiveness of combining radiomic and genomic models in predicting the long-term prognosis of patients with lung cancer and to contribute to the further exploration of radiomics. This study retrieved comprehensive literature from multiple databases, including radiomics and genomics, to study the prognosis of lung cancer. The model construction consisted of the radiomic and genomic methods. A comprehensive bias assessment was conducted, including risk assessment and model performance indicators. Ten studies between 2016 and 2023 were analyzed. Studies were mostly retrospective. Patient cohorts varied in size and characteristics, with the number of patients ranging from 79 to 315. The construction of the model involves various radiomic and genotic datasets, and most models show promising prediction performance with the area under the receiver operating characteristic curve (AUC) values ranging from 0.64 to 0.94 and the concordance index (C-index) values from 0.28 to 0.80. The combination model typically outperforms the single method model, indicating higher prediction accuracy and the highest AUC was 0.99. Combining radiomics and genomics in the prognostic model of lung cancer may improve the predictive performance. However, further research on standardized data and larger cohorts is needed to validate and integrate these findings into clinical practice. CRITICAL RELEVANCE STATEMENT: The combination of radiomics and genomics in the prognostic model of lung cancer improved prediction accuracy in most included studies. KEY POINTS: The combination of radiomics and genomics can improve model performance in most studies. The results of establishing prognosis models by different methods are discussed. The combination of radiomics and genomics may be helpful to provide better treatment for patients.

CoT: Contourlet Transformer for Hierarchical Semantic Segmentation.

The Transformer-convolutional neural network (CNN) hybrid learning approach is gaining traction for balancing deep and shallow image features for hierarchical semantic segmentation. However, they are still confronted with a contradiction between comprehensive semantic understanding and meticulous detail extraction. To solve this problem, this article proposes a novel Transformer-CNN hybrid hierarchical network, dubbed contourlet transformer (CoT). In the CoT framework, the semantic representation process of the Transformer is unavoidably peppered with sparsely distributed points that, while not desired, demand finer detail. Therefore, we design a deep detail representation (DDR) structure to investigate their fine-grained features. First, through contourlet transform (CT), we distill the high-frequency directional components from the raw image, yielding localized features that accommodate the inductive bias of CNN. Second, a CNN deep sparse learning (DSL) module takes them as input to represent the underlying detailed features. This memory-and energy-efficient learning method can keep the same sparse pattern between input and output. Finally, the decoder hierarchically fuses the detailed features with the semantic features via an image reconstruction-like fashion. Experiments demonstrate that CoT achieves competitive performance on three benchmark datasets: PASCAL Context 57.21% mean intersection over union (mIoU), ADE20K (54.16% mIoU), and Cityscapes (84.23% mIoU). Furthermore, we conducted robustness studies to validate its resistance against various sorts of corruption. Our code is available at: https://github.com/yilinshao/CoT-Contourlet-Transformer.

High-purity isolation platelets by gradient centrifugation plus filtration.

INTRODUCTION: Platelets can be used as a liquid biopsy source to provide rapid, up-to-date, and relevant information on tumor pathology and treatment response. However, there is still a lack of high efficiency methods for platelet isolation with high purity. METHODS: Three platelet isolation methods were evaluated by platelet recovery and purity. The platelet inhibition cocktail (PIC) was added into peripheral blood, or was not allowed to access the effect of the platelet activation. The CD61, CD45, and CD62P labelled platelets, leukocytes and activated platelets were detected by flow cytometry. Quantitative polymerase chain reaction (qPCR) and next-generation sequencing (NGS) were employed to determine the gene expression levels. A time-dependent experiment combined with qPCR was used to determine the time limit for platelet isolation at room temperature. RESULTS: Compared to the gradient centrifugation alone, and gradient centrifugation plus filtration and magnetic beads separation, gradient centrifugation plus filtration was the preferred method for more efficient and high-purity platelet isolation, with a recovery rate of 9.1% and a purity of 99.98%. Furthermore, there was no difference in platelet activation level, regardless of whether PIC was used. Moreover, the rate of platelet RNA degradation did not differ when platelets were isolated within 48 h of blood collection. CONCLUSION: Gradient centrifugation plus filtration at room temperature within 48 h of blood collection, without PIC, is a novel protocol with high recovery and purity rate to isolate platelets.

Baseline monocyte and its classical subtype may predict efficacy of PD-1/PD-L1 inhibitor in cancers.

BACKGROUND: Programmed death 1 (PD-1)/ programmed death-ligand 1 (PD-L1) inhibitor is one of the most popular immune therapies. Biomarkers for predicting response are highly needed, but no biomarkers are widely used till now. PATIENTS AND METHODS: From February 2018 to April 2019, pan-cancer patients treated with PD-1 or PD-L1 inhibitor as a single agent in our center were included. The benefit group included patients with partial response, complete response and stable disease, while the patients with progressive disease were classified into the nonbenefit group, according to the RECIST 1.1 criteria. Baseline peripheral blood was sampled to determine absolute monocyte count (AMC) and/or classical monocyte frequency (CMF) of peripheral blood mononuclear cells. Then, the association of the above-mentioned two biomarkers with response or progression-free survival (PFS) was evaluated. RESULTS: In total, 107 patients enrolled in the present study. The nonbenefit group had significantly larger number of AMC than benefit group (P<0.001), and patients with higher AMC had decreased PFS time (P=0.001). Of 39 patients tested for CMF, the nonbenefit group had significantly higher CMF than benefit group (P=0.002), and patients with higher CMF had significantly decreased PFS time (P=0.002). The sensitivity of AMC and CMF was 87.9% and 85.7%, respectively, and the specificity was 44.9% and 61.1%, respectively. Multivariate analysis showed high baseline CMF and AMC were both significantly associated with decreased PFS time. CONCLUSION: Baseline CMF and baseline AMC can be potential pan-cancer biomarkers to predict efficacy of PD-1/PD-L1 inhibitors, especially in the PD-L1 subgroup.

Sentinel node theory helps tracking of primary lesions of cancers of unknown primary.

BACKGROUND: Sentinel lymph node is the first stop of lymphatic spreading of cancer with known primary. The lymph node metastasis pattern of cancer of unknown primary (CUP) is unclear and has been presumed to follow the same pathway. To test this hypothesis, data of all 716 patients clinically diagnosed as CUP in our center were collected. METHODS: Diagnoses of lymph node metastasis were established by 18F-FDG PET-CT and/or biopsy pathology. Three hundred and forty-seven cases meeting the criteria were divided into three groups: pathology-confirmed primary with invasive biopsy or surgery of the suspicious lesion (group A, n = 64), primary still unknown even with invasive biopsy or surgery of the suspicious lesion (group B, n = 204), and others with no suspicious lesion or lesions who had not been sampled due to medical or other reasons (group C, n = 79). We assessed the clinicopathological features between these groups, and the relationship between lymph node metastasis pattern and confirmed primary site. RESULTS: In group A, the primary sites of 61 cases were compatible with sentinel node theory, resulting in a positive predictive value of 95%. No significant differences in age, sex, bone metastasis, or visceral metastasis observed between group A and group B, except that group A had a higher ratio of differentiated carcinoma (94% vs. 77%, P = 0.003). CONCLUSION: To our knowledge, this is the first evidence indicating that the majority of clinical CUP cases follow the sentinel node theory to spread in lymph nodes, which helps tracking the primary, especially for differentiated carcinoma.

Characterizations of Cancer Gene Mutations in Chinese Metastatic Breast Cancer Patients.

Background: Breast cancer (BC) is a type of disease with high heterogeneity. Molecular profiling, by revealing the intrinsic nature of its various subtypes, has extensively improved the therapeutic management of BC patients. However, the genomic mutation landscape of Chinese metastatic BC has not been fully explored. Methods: Matched plasma and mononuclear cells from 290 Chinese women with metastatic BC were sequenced using either of the two commercially-available panels consisting of 520 cancer-related and 108 BC-related genes. Both panels cover the same critical regions of 91 genes. The circulating tumor DNA mutation profile from our cohort was then compared with publicly-available metastatic BC datasets from Memorial Sloan Kettering Cancer Center (MSKCC) and Pan-cancer analysis of whole genomes (PCAWG). Results: A total of 1,201 mutations spanning 91 genes were detected from 234 patients, resulting in a mutation detection rate of 80.7%. TP53 (64.1%) was the gene with highest mutation frequency, followed by PIK3CA (31%), PTEN (11%), and RB1 (10%). Copy number amplifications (CNAs) in MYC (14.1%), FGFR1 (13.3%), CCND1 (6.6%), FGF3 (6.6%), FGF4 (6.2%) and FGF19 (6.2%) were also detected from our cohort. TP53 mutations were significantly more frequent among triple negative BC (TNBC), HR-/HER2+, and HR+/HER2+ BC, while less common in HR+/HER2- (P < 0.01). Meanwhile, PIK3CA mutations were significantly more frequent among HR+/HER2+, HR+/HER2-, and HR-/HER2+ BC, while less common in TNBC (P < 0.01). Pathogenic or likely pathogenic BRCA1/2 germline mutations were detected in 5.9% of the cohort and 4.4% in TNBC subgroup. Maximum allelic fraction (maxAF) of TP53, RB1, and PIK3CA mutations were associated with multiple organ metastasis. Patients with PIK3CA, PTEN, and RB1 mutation were more likely to have liver metastasis (P < 0.02). Compared with MSKCC and PCAWG dataset, Chinese patients had observably difference in genetic variation rates in different molecular subtypes (TNBC: TP53 73.0 vs. 91.5%, P < 0.001; PIK3CA 21.2 vs. 13.2%, P = 0.061; HR+/HER2-: FGFR1 3.3 vs. 0.7%, P = 0.035; TP 53 46.2 vs. 27.7%, P < 0.001; RB1 6.6 vs. 2.7%, P = 0.046; CDKN2A 7.7 vs. 1.0%, P < 0.001; PIK3CA 30.8 vs. 44.2%, P = 0.012; CDH1 1.1 vs. 18.2%, P < 0.001; GATA3 7.7 vs. 17.2%, P = 0.02). Conclusions: A distinct gene mutation profile was elucidated in Chinese women with metastatic BC, justifying further research. Liquid biopsy provides a quick, real-time, and minimally invasive tool for future clinical trial and routine practice.

The utility of "low current" stimulation threshold of intraoperative electromyography monitoring in predicting facial nerve function outcome after vestibular schwannoma surgery: a prospective cohort study of 103 large tumors.

To investigate the predictive utility of stimulation threshold (ST) of intraoperative electromyography monitoring for facial nerve (FN) outcomes among vestibular schwannoma (VS) patients postoperatively. The authors enrolled 103 unilateral VS patients who underwent surgical resection into a prospective cohort observational study from January 2013 to April 2015 in our hospital. ST values were used to categorize 81 patients into the "low current" (ST ≤ 0.05 mA) group and 22 patients into the control (ST > 0.05 mA) group. The FN function outcomes were summarized and correlated with these two groups at 1, 3, 6, and 12 months after surgery. Binary regression analysis revealed that the percentage of "good" FN outcome, defined by House-Brackmann (HB) classification of facial function (I-II), in the "low current" group was significantly higher than that of the control group (42.0 vs. 4.5% at 1 month, P = 0.015; 64.2 vs. 31.8% at 3 months, P = 0.024; 72.8 vs. 40.9% at 6 months, P = 0.021; 84.0 vs. 45.5% at 12 months, P = 0.002). Ordinal regression analysis showed that the distribution of HB scores was shifted in a favorable direction in the "low current" group at 1, 3, 6, and 12 months postoperatively. For patients with HB IV at the first month postoperative period, the recovery rate of the "low current" group was significantly higher than that of control group (P = 0.003). "Low current" can predict FN function outcomes better and has faster recovery rates than that of the control group.