Clinical characteristics of pulmonary cryptococcosis coexisting with lung adenocarcinoma: Three case reports

Pulmonary cryptococcosis coexisting with lung adenocarcinoma: A case report and review of the literature

Pulmonary cryptococcosis (PC) is an invasive pulmonary fungal disease caused by Cryptococcus neoformans or Cryptococcus gattii. It often presents as a single nodule or mass on radiology, which is easily misdiagnosed as lung cancer or metastases. However, cases of PC coexisting with lung cancer are rare and when this scenario is encountered in clinical practice, it is easy to be misdiagnosed as metastatic lung cancer. The present study reported the case of a 65-year-old immunocompetent patient with PC coexisting with lung adenocarcinoma. Percutaneous lung biopsy was performed on the nodule in the anterior segment of the left upper lobe and the nodule in the posterior basal segment of the left lower lobe, which were diagnosed as primary adenocarcinoma and cryptococcus, respectively. Lung cancer was treated by surgery and PC was treated successfully by antifungal treatment. During the 5-year follow-up, contrast-enhanced CT showed no recurrence of either disease. This case reminds us of the possibility of dualism in the diagnosis of multiple pulmonary nodules based on CT examination, such as the coexistence of lung carcinoma and PC. In addition, early diagnosis and treatment contribute to good prognosis.

Development and Validation of a Radiomics Nomogram for Differentiating Pulmonary Cryptococcosis and Lung Adenocarcinoma in Solitary Pulmonary Solid Nodule

Objective

To establish a CT-based radiomics nomogram model for classifying pulmonary cryptococcosis (PC) and lung adenocarcinoma (LAC) in patients with a solitary pulmonary solid nodule (SPSN) and assess its differentiation ability.

Materials and Methods

A total of 213 patients with PC and 213 cases of LAC (matched based on age and gender) were recruited into this retrospective research with their clinical characteristics and radiological features. High-dimensional radiomics features were acquired from each mask delineated by radiologists manually. We adopted the max-relevance and min-redundancy (mRMR) approach to filter the redundant features and retained the relevant features at first. Then, we used the least absolute shrinkage and operator (LASSO) algorithms as an analysis tool to calculate the coefficients of features and remove the low-weight features. After multivariable logistic regression analysis, a radiomics nomogram model was constructed with clinical characteristics, radiological signs, and radiomics score. We calculated the performance assessment parameters, such as sensitivity, specificity, accuracy, negative predictive value (NPV), and positive predictive value (PPV), in various models. The receiver operating characteristic (ROC) curve analysis and the decision curve analysis (DCA) were drawn to visualize the diagnostic ability and the clinical benefit.

Results

We extracted 1,130 radiomics features from each CT image. The 24 most significant radiomics features in distinguishing PC and LAC were retained, and the radiomics signature was constructed through a three-step feature selection process. Three factors-maximum diameter, lobulation, and pleural retraction-were still statistically significant in multivariate analysis and incorporated into a combined model with radiomics signature to develop the predictive nomogram, which showed excellent classification ability. The area under curve (AUC) yielded 0.91 (sensitivity, 80%; specificity, 83%; accuracy, 82%; NPV, 80%; PPV, 83%) and 0.89 (sensitivity, 81%; specificity, 83%; accuracy, 82%; NPV, 81%; PPV, 82%) in training and test cohorts, respectively. The net reclassification indexes (NRIs) were greater than zero (p < 0.05). The Delong test showed a significant difference (p < 0.0001) between the AUCs from the clinical model and the nomogram.

Conclusions

The radiomics technology can preoperatively differentiate PC and lung adenocarcinoma. The nomogram-integrated CT findings and radiomics feature can provide more clinical benefits in solitary pulmonary solid nodule diagnosis.