Objective To develop a nomogram diagnostic model to differentiate peripheral small cell lung cancer(PSCLC)from peripheral lung adenocarcinoma(PADC)using clinical and multi spiral computed tomography features.Methods A retrospective analysis was conducted on the CT characteristics and clinical presentations of 50 PSCLC and 100 PADC.Univariate and multivariate logistic regression analyses were employed to identify significant features.A nomogram was constructed to quantify the influencing factors.The efficacy and clinical applicability of the model were assessed using the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results Smoking,neuron-specific enolase(NSE),smooth margin,spindle/branching shape,and lymphadenopathy were independent risk factors for PSCLC(P＜0.05),whereas rough margin and lobulation sign were independent risk factors for PADC(P＜0.05).The nomogram model demonstrated high diagnostic efficacy,and the calibration curve exhibited a good degree of calibration(Brier=0.079).The DCA indicated that the nomogram model possesses substantial clinical utility.Conclusion The nomogram model developed based on six indicators,including smoking,NSE≥17 ng/mL,margin characteristics,spindle/branching shape,lobulation sign,and lymphadenopathy can well distinguish PSCLC from PADC.

Objective To develop a nomogram diagnostic model to differentiate peripheral small cell lung cancer(PSCLC)from peripheral lung adenocarcinoma(PADC)using clinical and multi spiral computed tomography features.Methods A retrospective analysis was conducted on the CT characteristics and clinical presentations of 50 PSCLC and 100 PADC.Univariate and multivariate logistic regression analyses were employed to identify significant features.A nomogram was constructed to quantify the influencing factors.The efficacy and clinical applicability of the model were assessed using the receiver operating characteristic(ROC)curve,calibration curve,and decision curve analysis(DCA).Results Smoking,neuron-specific enolase(NSE),smooth margin,spindle/branching shape,and lymphadenopathy were independent risk factors for PSCLC(P＜0.05),whereas rough margin and lobulation sign were independent risk factors for PADC(P＜0.05).The nomogram model demonstrated high diagnostic efficacy,and the calibration curve exhibited a good degree of calibration(Brier=0.079).The DCA indicated that the nomogram model possesses substantial clinical utility.Conclusion The nomogram model developed based on six indicators,including smoking,NSE≥17 ng/mL,margin characteristics,spindle/branching shape,lobulation sign,and lymphadenopathy can well distinguish PSCLC from PADC.