Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study

Nomogram-based prediction model for survival of COVID-19 patients: A clinical study

The study aim to construct an effective model for predicting the survival period of COVID-19 patients.

METHODS

Clinical data of 386 COVID-19 patients were collected from December 2022 to January 2023. The patients were randomly divided into training and validation cohorts in a 7:3 ratio. LASSO regression and multivariate Cox regression analyses were used to identify prognostic factors, and a nomogram was constructed. Nomogram was evaluated using decision curve analysis, receiver operating characteristic curve, consistency index (c-index), and calibration curve.

RESULTS

86 patients (22.3%) died. A new nomogram for predicting the survival was established based on age, resting oxygen saturation, Blood urea nitrogen (BUN), c-reactive protein-to-albumin ratio (CAR), and pneumonia visual score. The decision curve indicated high clinical applicability. The nomogram c-indexes in the training and validation cohorts were 0.846 and 0.81, respectively. The area under the curves (AUCs) for the 15-day and 30-day survival probabilities were 0.906 and 0.869 in the training cohort, and 0.851 and 0.843 in the validation cohort. The calibration curves demonstrated consistency between predicted and actual survival probabilities.

CONCLUSIONS

Our nomogram has the capacity to assist clinical practitioners in estimating the survival rate of COVID-19 patients, thereby facilitating more optimal management strategies and therapeutic interventions with substantial clinical applicability.

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19

OBJECTIVES

Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population.

METHODS

We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction.

RESULTS

These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts.

CONCLUSIONS

Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

BACH1: A Potential Predictor of Survival in Early-Stage Lung Adenocarcinoma

Purpose

Recent researches showed the vital role of BACH1 in promoting the metastasis of lung cancer. We aimed to explore the value of BACH1 in predicting the overall survival (OS) of early-stage (stages I-II) lung adenocarcinoma. Patients and Methods. Lung adenocarcinoma cases were screened from the Cancer Genome Atlas (TCGA) database. Functional enrichment analysis was performed to obtain the biological mechanisms of BACH1. Gene set enrichment analysis (GSEA) was performed to identify the difference of biological pathways between high- and low-BACH1 groups. Univariate and multivariate COX regression analysis had been used to screen prognostic factors, which were used to establish the BACH1 expression-based prognostic model in the TCGA dataset. The C-index and time-dependent AUC curve were used to evaluate predictive power of the model. External validation of prognostic value was performed in two independent datasets from Gene Expression Omnibus (GEO). Decision analysis curve was finally used to evaluate clinical usefulness of the BACH1-based model beyond pathologic stage alone.

Results

BACH1 was an independent prognostic factor for lung adenocarcinoma. High-expression BACH1 cases had worse OS. BACH1-based prognostic model showed an ideal C-index and t-AUC and validated by two GEO datasets, independently. More importantly, the BACH1-based model indicated positive clinical applicability by DCA curves.

Conclusion

Our research confirmed that BACH1 was an important predictor of prognosis in early-stage lung adenocarcinoma. The higher the expression of BACH1, the worse OS of the patients.

Individualized model for predicting COVID-19 deterioration in patients with cancer: A multicenter retrospective study

The 2019 novel coronavirus has spread rapidly around the world. Cancer patients seem to be more susceptible to infection and disease deterioration, but the factors affecting the deterioration remain unclear. We aimed to develop an individualized model for prediction of coronavirus disease (COVID-19) deterioration in cancer patients. The clinical data of 276 cancer patients diagnosed with COVID-19 in 33 designated hospitals of Hubei, China from December 21, 2019 to March 18, 2020, were collected and randomly divided into a training and a validation cohort by a ratio of 2:1. Cox stepwise regression analysis was carried out to select prognostic factors. The prediction model was developed in the training cohort. The predictive accuracy of the model was quantified by C-index and time-dependent area under the receiver operating characteristic curve (t-AUC). Internal validation was assessed by the validation cohort. Risk stratification based on the model was carried out. Decision curve analysis (DCA) were used to evaluate the clinical usefulness of the model. We found age, cancer type, computed tomography baseline image features (ground glass opacity and consolidation), laboratory findings (lymphocyte count, serum levels of C-reactive protein, aspartate aminotransferase, direct bilirubin, urea, and d-dimer) were significantly associated with symptomatic deterioration. The C-index of the model was 0.755 in the training cohort and 0.779 in the validation cohort. The t-AUC values were above 0.7 within 8 weeks both in the training and validation cohorts. Patients were divided into two risk groups based on the nomogram: low-risk (total points ≤ 9.98) and high-risk (total points > 9.98) group. The Kaplan-Meier deterioration-free survival of COVID-19 curves presented significant discrimination between the two risk groups in both training and validation cohorts. The model indicated good clinical applicability by DCA curves. This study presents an individualized nomogram model to individually predict the possibility of symptomatic deterioration of COVID-19 in patients with cancer.