Fine-Grained Assessment of COVID-19 Severity Based on Clinico-Radiological Data Using Machine Learning

Background: The severe and critical cases of COVID-19 had high mortality rates. Clinical features, laboratory data, and radiological features provided important references for the assessment of COVID-19 severity. The machine learning analysis of clinico-radiological features, especially the quantitative computed tomography (CT) image analysis results, may achieve early, accurate, and fine-grained assessment of COVID-19 severity, which is an urgent clinical need. Objective: To evaluate if machine learning algorithms using CT-based clinico-radiological features could achieve the accurate fine-grained assessment of COVID-19 severity. Methods: The clinico-radiological features were collected from 78 COVID-19 patients with different severities. A neural network was developed to automatically measure the lesion volume from CT images. The severity was clinically diagnosed using two-type (severe and non-severe) and fine-grained four-type (mild, regular, severe, critical) classifications, respectively. To investigate the key features of COVID-19 severity, statistical analyses were performed between patients’ clinico-radiological features and severity. Four machine learning algorithms (decision tree, random forest, SVM, and XGBoost) were trained and applied in the assessment of COVID-19 severity using clinico-radiological features. Results: The CT imaging features (CTscore and lesion volume) were significantly related with COVID-19 severity (p < 0.05 in statistical analysis for both in two-type and fine-grained four-type classifications). The CT imaging features significantly improved the accuracy of machine learning algorithms in assessing COVID-19 severity in the fine-grained four-type classification. With CT analysis results added, the four-type classification achieved comparable performance to the two-type one. Conclusions: CT-based clinico-radiological features can provide an important reference for the accurate fine-grained assessment of illness severity using machine learning to achieve the early triage of COVID-19 patients

Interpretable Machine Learning for COVID-19:An Empirical Study on Severity Prediction Task

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify biomarkers indicative of severity prediction. Through the interpretation of four machine learning models, decision tree, random forests, gradient boosted trees, and neural networks using permutation feature importance, Partial Dependence Plot (PDP), Individual Conditional Expectation (ICE), Accumulated Local Effects (ALE), Local Interpretable Model-agnostic Explanations (LIME), and Shapley Additive Explanation (SHAP), we identify an increase in N-Terminal pro-Brain Natriuretic Peptide (NTproBNP), C-Reaction Protein (CRP), and lactic dehydrogenase (LDH), a decrease in lymphocyte (LYM) is associated with severe infection and an increased risk of death, which is consistent with recent medical research on COVID-19 and other research using dedicated models. We further validate our methods on a large open dataset with 5644 confirmed patients from the Hospital Israelita Albert Einstein, at S\~ao Paulo, Brazil from Kaggle, and unveil leukocytes, eosinophils, and platelets as three indicative biomarkers for COVID-19.Comment: 14 pages, 10 figure

Targeting glutamine metabolic reprogramming of SLC7A5 enhances the efficacy of anti-PD-1 in triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) is a heterogeneous disease that is characterized by metabolic disruption. Metabolic reprogramming and tumor cell immune escape play indispensable roles in the tumorigenesis that leads to TNBC.MethodsIn this study, we constructed and validated two prognostic glutamine metabolic gene models, Clusters A and B, to better discriminate between groups of TNBC patients based on risk. Compared with the risk Cluster A patients, the Cluster B patients tended to exhibit better survival outcomes and higher immune cell infiltration. In addition, we established a scoring system, the glutamine metabolism score (GMS), to assess the pattern of glutamine metabolic modification.ResultsWe found that solute carrier family 7 member 5 (SLC7A5), an amino acid transporter, was the most important gene and plays a vital role in glutamine metabolism reprogramming in TNBC cells. Knocking down SLC7A5 significantly inhibited human and mouse TNBC cell proliferation, migration, and invasion. In addition, downregulation of SLC7A5 increased CD8+ T-cell infiltration. The combination of a SLC7A5 blockade mediated via JPH203 treatment and an anti-programmed cell death 1 (PD-1) antibody synergistically increased the immune cell infiltration rate and inhibited tumor progression.ConclusionsHence, our results highlight the molecular mechanisms underlying SLC7A5 effects and lead to a better understanding of the potential benefit of targeting glutamine metabolism in combination with immunotherapy as a new therapy for TNBC

Impact of clinicopathological factors on extended endocrine therapy decision making in estrogen receptor–positive breast cancer

PurposeIn our study, we aim to analyze the impact of clinicopathological factors on the recommendation of extended endocrine therapy (EET) in patients with ER+ breast cancer and to retrospectively validate the value of CTS5 in EET decision making.Patients and methodsThe retrospective analysis was performed in patients with ER+ breast cancer who have finished 4.5–5 years of adjuvant endocrine therapy and undergone MDT discussion from October 2017 to November 2019. Multivariate logistic regression was used to identify the independent factors for treatment recommendation. CTS5 was calculated for retrospective validation of the EET decision making.ResultsTwo hundred thirty-five patients were received; 4.5–5 years of adjuvant endocrine therapy were included in the study. Multivariate analysis suggested that age (OR 0.460, 95% CI 0.219–0.965, p = 0.04), pN (OR 39.350, 95% CI 9.831–157.341, P < 0.001), and receipt of chemotherapy (OR 3.478, 95% CI 1.336–9.055, p = 0.011) were independent predictors for the recommendation of EET. In the previously selective estrogen receptor modulator (SERM)–treated subgroup, pN and receipt of chemotherapy were independent predictors for the recommendation of EET. In the previously AI-treated subgroup, age, pN, and receipt of chemotherapy were independent predictors. Adverse events did not affect the recommendation in patients previously treated with adjuvant endocrine treatment nor in the previously SERM or AI-treated subgroups. CTS5 (OR 21.887, 95% CI 2.846–168.309, p = 0.003) remained an independent predictor for the recommendation of EET.ConclusionsOur study indicated that age, lymph nodal status, and receipt of chemotherapy were independent predictors for the recommendation of EET. The application of the CTS5 on EET decision making might be valuable among ER+ breast cancer patients

Overexpression of epithelial growth factor receptor (EGFR) predicts better response to neo-adjuvant chemotherapy in patients with triple-negative breast cancer

Abstract Background Triple negative breast cancer (TNBC) occurs in approximately 10% to 25% of all patients with breast cancer and is associated with poor prognosis. Neo-adjuvant chemotherapy has been reported to produce a higher pathologic complete response (pCR) rate in TNBC. If pCR is achieved, patients with TNBC had a similar survival with non-TNBC patients. The aim of our study was to investigate the protein expression of epithelial growth factor receptor (EGFR) and response to neo-adjuvant chemotherapy and clinical outcome in patients with TNBC compared with non-TNBC. Methods A total of 198 locally advanced breast cancer patients who received neo-adjuvant chemotherapy were studied. Immunohistochemistry (IHC) was carried out to detect the protein expression of EGFR in tumor samples. Clinical and pathological parameters, pCR rate and survival data were compared between 40 TNBCs and 158 non-TNBCs. Results In 198 cases who received neo-adjuvant chemotherapy, significant differences exist in surgical therapy (P=0.005) and pCR rate (P=0.012) between patients with TNBCs and non-TNBCs. Overexpression of EGFR was significantly associated with pCR rate in patients with TNBCs (P &lt; 0.001). Survival analysis revealed that patients with TNBCs had worse DFS and OS than those with non-TNBCs (P = 0.001, P &lt; 0.001 respectively). Furthermore, for patients with non-TNBCs, those who acheived pCR had better DFS and OS than those who acheived RD (both P &lt; 0.001). Conclusions Our results suggested that patients with TNBCs had increased pCR rates compared with non-TNBC. Overexpression of EGFR predicted better response to neo-adjuvant chemotherapy in patients with TNBCs. </jats:sec

Retrospective analysis of 119 Chinese noninflammatory locally advanced breast cancer cases treated with intravenous combination of vinorelbine and epirubicin as a neoadjuvant chemotherapy: a median follow-up of 63.4 months

<p>Abstract</p> <p>Background</p> <p>This study is a retrospective evaluation of the efficacy of neoadjuvant chemotherapy (NC) with a vinorelbine (V) and epirubicin (E) intravenous combination regimen and is aimed at identification of predictive markers for the long-term outcome in noninflammatory locally advanced breast cancer (NLABC).</p> <p>Methods</p> <p>One-hundred-and-nineteen patients with NLABC were identified from September 2001 to May 2006. Analysis was performed in March 2008, with a median follow-up of 63.4 months (range, 9-76 months). All patients were diagnosed with invasive breast cancer using 14 G core needle biopsy and treated with three cycles of VE before surgery. Local-regional radiotherapy was offered to all patients after the completion of chemotherapy followed by hormonal therapy according to hormone receptor status. Tissue sections cut from formalin-fixed paraffin-embedded blocks from biopsy specimens and postoperative tumor tissues were stained for the presence of estrogen receptor (ER), progesterone receptor (PgR), HER-2 (human epidermal growth factor receptor-2), and MIB-1(Ki-67).</p> <p>Results</p> <p>Patients characteristics were median age 52 years (range: 25-70 years); clinical TNM stage, stage IIB (n = 32), stage IIIA (n = 56), stage IIIB (n = 22) and stage IIIC (n = 9). All patients were evaluable for response: clinically complete response was documented in 27 patients (22.7%); 78 (65.6%) obtained partial response; stable disease was observed in 13 (10.9%); 1 patient (0.8%) had progressive disease. Pathological complete response was found in 22 cases (18.5%). Seventy-five patients were alive with no recurrence after a median follow-up of 63.4 months, the 5-year rates for disease-free survival and overall survival were 58.7% and 71.3%, respectively, after the start of NC. On multivariate analysis, the independent variables associated with increased risk of relapse and death were high pre-Ki-67(p = 0.012, p = 0.017, respectively), high post-Ki-67 expression (p = 0.045, p = 0.001, respectively), and non-pCR (p = 0.034, p = 0.027, respectively). A significantly increased risk of death was associated with lack of pre-ER expression (p = 0.002). Among patients with non-pCR, those with a pathological response at the tumor site with special involvement (i.e. skin, vessel and more than one quadrant) were at a higher risk of disease relapse and death (p < 0.001, p = 0.001, respectively).</p> <p>Conclusion</p> <p>This study suggests the promising use of a VE regimen as NC for Chinese NLABC after a median follow-up of 63.4 months. Pathological response in the tumor site, pre-Ki-67 and post-Ki-67 expression, and pre-ER expression were the important variables that predicted long-term outcome. Patients with pathological special involvement at the primary site after NC had the lowest survival rates.</p

ADVCSO: Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization for Combinatorial Optimization Problems

High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative optimization requirements due to computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding and straightforward implementation for low-dimensional problems, it suffers from limitations including a low convergence precision, uneven initial solution distribution, and premature convergence. This study proposes an Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization (ADVCSO) algorithm. First, to address the uneven initial solution distribution in the original algorithm, we design an elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations to significantly improve the search space coverage. Second, targeting the exploration&ndash;exploitation imbalance caused by fixed role proportions, a dynamic role allocation mechanism is developed, integrating cosine annealing strategies to adaptively regulate flock proportions and update cycles, thereby enhancing exploration efficiency. Finally, to mitigate the premature convergence induced by single update rules, hybrid mutation strategies are introduced through phased mutation operators and elite dimension inheritance mechanisms, effectively reducing premature convergence risks. Experiments demonstrate that the ADVCSO significantly outperforms state-of-the-art algorithms on 27 of 29 CEC2017 benchmark functions, achieving a 2&ndash;3 orders of magnitude improvement in convergence precision over basic CSO. In complex composite scenarios, its convergence accuracy approaches that of the championship algorithm JADE within a 10&minus;2 magnitude difference. For collaborative multi-subproblem optimization, the ADVCSO exhibits a superior performance in both Multiple Traveling Salesman Problems (MTSPs) and Multiple Knapsack Problems (MKPs), reducing the maximum path length in MTSPs by 6.0% to 358.27 units while enhancing the MKP optimal solution success rate by 62.5%. The proposed algorithm demonstrates an exceptional performance in combinatorial optimization and holds a significant engineering application value