[Experience with Surgical Stabilization for Traumatic Rib Fractures and Sternal Fractures].

The treatment of traumatic rib fractures and sternal fractures have focused on pain and respiratory management, and conservative treatment has been recommended. Recently, however, a number of case series from abroad have been reported and demonstrated the usefulness of surgical stabilization of rib fractures (SSRF) and sternal fractures (SSSF). We have experienced seven cases of SSRF and two cases of SSSF at International University Health and Welfare Narita Hospital and Atami Hospital. Based on our experienced cases, we have outlined the preoperative evaluation, indication for surgery, timing of surgery, surgical techniques, and postoperative course. Of these nine cases, the clinical course of two cases of SSRF and one case of SSSF were detailly presented. The surgical indications and techniques for traumatic rib fractures and sternal fractures vary from institution to institution, and there is no single optimal treatment. We hope that the accumulation of cases, and discussions will help to build a higher quality evidence for surgical treatment of thoracic trauma in Japan.

A simple nomogram for predicting occult lymph node metastasis of non-small cell lung cancer from preoperative computed tomography findings, including the volume-doubling time.

PURPOSE: Latent lymph node metastasis is a clinical concern in the surgical treatment of non-small cell lung cancer (NSCLC). The present study identified a simple tool, including the volume-doubling time (VDT), for evaluating the risk of nodal metastasis. METHODS: We reviewed, retrospectively, 560 patients who underwent radical resection for cN0M0 NSCLC. The whole tumor VDT and solid component VDT (SVDT) for differentiating the histological type and adenocarcinoma subtype were analyzed and a nomogram was constructed using variables selected through a stepwise selection method. The model was assessed through a calibration curve and decision curve analysis (DCA). RESULTS: Lymph node metastases were detected in 89 patients (15.9%). The SVDT tended to be longer in patients with adenocarcinoma (294.5 days, p < 0.0001) than in those with other histological types of NSCLC, but was shorter when the solid/micropapillary component was predominant (127.0 days, p < 0.0001). The selected variables (tumor location, solid component diameter, consolidation tumor ratio, SVDT, and carcinoembryonic antigen) demonstrated significant differences and were used for the nomogram. The calibration curve indicated consistency, and the DCA showed validity across most threshold ranges from 0 to 68%. CONCLUSIONS: The established nomogram is a useful tool for the preoperative prediction of lymph node metastasis, and the SVDT was the most influential factor in the nomogram.

Predicting pathological highly invasive lung cancer from preoperative [18F]FDG PET/CT with multiple machine learning models.

PURPOSE: The efficacy of sublobar resection of primary lung cancer have been proven in recent years. However, sublobar resection for highly invasive lung cancer increases local recurrence. We developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative [18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features. METHODS: Overall, 873 patients who underwent lobectomy or segmentectomy for primary lung cancer were enrolled. Radiomics features were extracted from preoperative PET/CT images with the PyRadiomics package. Seven machine learning models and an ensemble of all models (ENS) were evaluated after 100 iterations. In addition, the probability of highly invasive lung cancer was calculated in a nested cross-validation to assess the calibration plot and clinical usefulness and to compare to consolidation tumour ratio (CTR) on CT images, one of the generally used diagnostic criteria. RESULTS: In the training set, when PET and CT features were combined, all models achieved an area under the curve (AUC) of ≥ 0.880. In the test set, ENS showed the highest mean AUC of 0.880 and smallest standard deviation of 0.0165, and when the cutoff was 0.5, accuracy of 0.804, F1 of 0.851, precision of 0.821, and recall of 0.885. In the nested cross-validation, the AUC of 0.882 (95% CI: 0.860-0.905) showed a high discriminative ability, and the calibration plot indicated consistency with a Brier score of 0.131. A decision curve analysis showed that the ENS was valid with a threshold probability ranging from 3 to 98%. Accuracy showed an improvement of more than 8% over the CTR. CONCLUSION: The machine learning model based on preoperative [18F]FDG PET/CT images was able to predict pathological highly invasive lung cancer with high discriminative ability and stability. The calibration plot showed good consistency, suggesting its usefulness in quantitative risk assessment.

Radiomics is feasible for prediction of spread through air spaces in patients with nonsmall cell lung cancer.

Tumor spread through air spaces (STAS) in non-small-cell lung cancer (NSCLC) is known to influence a poor patient outcome, even in patients presenting with early-stage disease. However, the pre-operative diagnosis of STAS remains challenging. With the progress of radiomics-based analyses several attempts have been made to predict STAS based on radiological findings. In the present study, patients with NSCLC which is located peripherally and tumors ≤ 2 cm in size on computed tomography (CT) that were potential candidates for sublobar resection were enrolled in this study. The radiologic features of the targeted tumors on thin-section CT were extracted using the PyRadiomics v3.0 software package, and a predictive model for STAS was built using the t-test and XGBoost. Thirty-five out of 226 patients had a STAS histology. The predictive model of STAS indicated an area under the receiver-operator characteristic curve (AUC) of 0.77. There was no significant difference in the overall survival (OS) for lobectomy between the predicted-STAS (+) and (-) groups (p = 0.19), but an unfavorable OS for sublobar resection was indicated in the predicted-STAS (+) group (p < 0.01). These results suggest that radiomics with machine-learning helped to develop a favorable model of STAS (+) NSCLC, which might be useful for the proper selection of candidates who should undergo sublobar resection.