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Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.
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Current studies show that postoperative adjuvant radiotherapy (PORT)may improve local control and overall survival for Ⅲ A-N2 NSCLC.With modern radiotherapy technology and techniques,the adverse effect of PORT is moderate and tolerated.Besides,modern PORT doesn′t decrease the survival.Inves-tigation concerning postoperative target volume for PORT is rare,so that definite agreement has not been reached.Available informations suggest that clinical target volume should include bronchial stump,involved lymph nodes,ipsilateral hilar nodes,subcarinal nodes and ipsilateral lower paratracheal nodes.
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etabolic response to radiotherapy may predict the prognosis of paitents with locally advanced NPC. The prognosis is poor for patients with high FDG uptake before and after radiotherapy or SUV max-NSUV max-P .
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Objective To determine the prognostic value of standardized uptake value(SUV)of fluorodeoxyglucose(FDG)by positron emission tomography and computed tomography(PET-CT)in nonsmall cell lung cancer(NSCLC).Methods Forty-eight patients(39 male,9 female)with stage ⅢNSCLC were reviewed.All patients had at least two repeated FDG PET-CT scans either before and after therapy and the maximum standardized uptake value(SUVmax)of the primary lung lesion was calculated. Resuits Of the 45 eligible patients,after a median follow-up of 22.5 months(rang,13 to 35 months),24 patients had local and regional recurrenee or metastasis and 21 remain disease-free.The mean SUVmax of patients who had local recurrence or metastasis before and after treatment was 12.30±3.17 and 5.35±2.29,respectively.The mean SUVmax of patients who had no loeal recurrence or metastasis before and after treatment was 8.46±3.00 and 2.82±0.63,respectively.Significant differences(tbefore=4.15,Pbefore＜0.01;Pafter=4.88,Pafter＜0.01)in SUVmax were observed either before and after treatment.However,the percent change of SUVmax between pretreatment and post-treatment were not significiantly different(t=1.99,P＞0.05).Using the SUVbefore of 9.0 yielded 92% sensitivity,62% specificity,73% positive predictive value and 87%negaffve Dredictive value in predicting regional recurrence or metastasis. While using the SUVafter of 4.3 yielded 71% sensitivity,100% specificity,100% positive predictive valne and 72% negative Dredictive value. Conclusions PET-CT may have the potientials to predict response to therapy and the SUVmax is a significant predictor for recurrent or metastasis in patients of NSCLC.
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Objective To discuss the value of dual-time-point 18FDG PET-CT imaging on involved field radiotherapy for hilar and mediastinal metastatic lymph nodes in patients with non-small cell lung cancer (NSCLC).Methods Fifty-four patients with NSCLC were included in this analysis,including 34 men and 20 women with mean age of 59(34-76)years.Two sequential PET-CT scans given 3-5 days before surgery were standard single-time-point imaging for the whole body and delayed imaging for the thorax.The pathologic data were used as golden standard to determine the difference between the standard single-time-point and dual-time-point FET-CT imaging in the definition of gross target volume(GTV)of involved-field radiotherapy for metastatic lymph nodes. Results For hilar metastatic lymph nodes,the GTV defined by single-time-point imaging was consistent with pathologic GTV in 21 patients(39%),comparing with 31 patients(57%) by dual-time-point imaging.Using pathologic data as golden standard,GTV alteration defined by single-time-point imaging had statisticaly significant difference comparing with that defined by dual-time-point imaging(u=519.00,P=0.023).For mediastinal metastatic lymph nodes,the GTV defined by single-time-point imaging was consistent with pathologic GTV in 30 patients(56%),comparing with 36 patients(67%)by dual-time-point imaging.Using pathologic data as golden standard.GTV alteration defined by single-time-point imaging had no statisticaly significant difference comparing with that defined by dual-time-point imaging(u=397.50,P=0.616).Conclusions For patients with NSCLC receiving involved-field radiotherapy,GTV definition for hilar and mediastinal metastatic lymph nodes by dual-time-point imaging is more consistent with that by pathologic data.Dual-time-point imaging has a larger value in terms of target delineation for hilar and mediastinal metastatic lymph nodes.