Microcomputed tomography as a diagnostic tool for detection of lymph node metastasis in non-small cell lung cancer: A decision-support approach for pathological examination "A pilot study for method validation".

Background: Non-small cell lung cancer (NSCLC) patients without lymph node (LN) metastases (pN0) may exhibit different survival rates, even when their T stage is similar. This divergence could be attributed to the current pathology practice, wherein LNs are examined solely in two-dimensional (2D). Unfortunately, adhering to the protocols of 2D pathological examination does not ensure the exhaustive sampling of all excised LNs, thereby leaving room for undetected metastatic foci in the unexplored depths of tissues. The employment of micro-computed tomography (micro-CT) facilitates a three-dimensional (3D) evaluation of all LNs without compromising sample integrity. In our study, we utilized quantitative micro-CT parameters to appraise the metastatic status of formalin-fixed paraffin-embedded (FFPE) LNs. Methods: Micro-CT scans were conducted on 12 FFPEs obtained from 8 NSCLC patients with histologically confirmed mediastinal LN metastases. Simultaneously, whole-slide images from these FFPEs underwent scanning, and 47 regions of interest (ROIs) (17 metastatic foci, 11 normal lymphoid tissues, 10 adipose tissues, and 9 anthracofibrosis) were marked on scanned images. Quantitative structural variables obtained via micro-CT analysis from tumoral and non-tumoral ROIs, were analyzed. Result: Significant distinctions were observed in linear density, connectivity, connectivity density, and closed porosity between tumoral and non-tumoral ROIs, as indicated by kappa coefficients of 1, 0.90, 1, and 1, respectively. Receiver operating characteristic analysis substantiated the differentiation between tumoral and non-tumoral ROIs based on thickness, linear density, connectivity, connectivity density, and the percentage of closed porosity. Conclusions: Quantitative micro-CT parameters demonstrate the ability to distinguish between tumoral and non-tumoral regions of LNs in FFPEs. The discriminatory characteristics of these quantitative micro-CT parameters imply their potential usefulness in developing an artificial intelligence algorithm specifically designed for the 3D identification of LN metastases while preserving the FFPE tissue.

Oncological results in primary and secondary malignant chest wall tumors.

Background: This study aims to evaluate the oncological results of primary and secondary chest wall tumors treated with curative resections and to investigate possible prognostic factors. Methods: Between January 2010 and December 2021, a total of 77 patients (53 males, 24 females; median age: 59 years; range, 3 to 87 years) who underwent curative resection for malignant chest wall tumors were retrospectively analyzed. Each tumor was staged according to its histological type. Age, sex, tumor diameter, tumor type (primary/secondary), histological tumor type, grade, stage, complete resection, rib resection, reconstruction, neoadjuvant and adjuvant therapy, recurrence, and survival data were recorded. Results: Of the chest wall tumors, 33 (42.9%) were primary and 44 (57.1%) were secondary (local invasion, metastasis). Nine (11.7%) patients had positive surgical margins. Chest wall resection was most commonly performed due to lung cancer invasion (46.8%), followed by Ewing sarcoma (13%). Recurrence was observed in 34 (44.2%) patients. The five-year recurrence-free survival rate was 42.7% and the five-year overall survival rate was 58.6%. There was no significant difference between the primary and secondary tumors in terms of recurrence-free and overall survival (p=0.663 and p=0.313, respectively). In the multivariate analysis, tumor grade and rib resection were found to be independent prognostic factors for both recurrence-free survival (p=0.005 and p<0.001, respectively) and overall survival (p=0.048 and p=0.007, respectively). Conclusion: Successful oncological results can be achieved in wellselected patients with primary and secondary chest wall tumors. The grade of the tumor should be taken into account while determining the neoadjuvant or adjuvant treatment approach and surgical margin width. Rib resection should not be avoided when necessary.

Assessment of complications and surgical timing in post-COVID-19 thoracotomy patients: A single-center experience.

Background: This study aims to assess the complications in post-novel coronavirus disease 2019 (COVID-19) thoracotomy patients and to evaluate the time interval between infection and surgery and the effect of vaccine timing and vaccine type in these patients. Methods: Between May 2020 and January 2022, a total of 74 patients (34 males, 40 females; mean age: 54.5±13.7 years; range, 22 to 27 years) who had COVID-19 infection and underwent thoracic surgery were retrospectively analyzed. Data including demographic and clinical characteristics, the surgery type, length of intensive care unit and hospital stay, and postoperative complications were recorded. Complications were defined as respiratory, cardiac, thrombotic, and other complications. The time period between COVID-19 infection and surgery was noted. All patients were questioned regarding their vaccination status. Results: Having a symptomatic COVID-19 infection did not significantly affect the development of postoperative complications and length of intensive care unit or hospital stay. Thoracic involvement was the main factor which affected the length of intensive care unit and hospital stay during COVID-19 infection (p=0.004 and p=0.003, respectively). Conclusion: Our study results suggest that the length of hospital and intensive care unit stay is related to the time period between infection and surgery. The longer that the patient waits after COVID-19 infection, the less time that the patient stays in the hospital.

Pulmonary sleeve resection complications and management.

In tumors involving the central airway or vascular structures, achieving local control and preserving pulmonary function can be possible with a pulmonary sleeve resection. In this section, complications and management of pulmonary sleeve resections are discussed.

Operated pulmonary inflammatory myofibroblastic tumors: Our experience with 17 cases.

Background: In this study, we aimed to evaluate the clinicopathological features of pulmonary inflammatory myofibroblastic tumor cases operated in our clinic. Methods: A total of 17 inflammatory myofibroblastic tumor patients (5 males, 12 females; median age: 46 years) who were operated in our clinic between February 2000 and July 2019 were included. Data including sex, age, symptoms, accompanying diseases, tumor localization, tumor diameter, endobronchial extension, maximum standard uptake value of the tumors, surgery type, recurrence, and survival data were analyzed. Results: Two patients were diagnosed preoperatively and two patients were diagnosed during surgery using frozen-section method before resection. Three (17.7%) patients underwent pneumonectomy, five (29.4%) patients lobectomy, three (17.7%) patients segmentectomy, five (29.4%) patients wedge resection, and one (5.8%) patient bronchial sleeve resection. All patients had complete resection with negative margins. None of them had lymph node metastasis. Median follow-up was 122 (range, 8 to 245 months) months. None of the patients received adjuvant therapy, there was no tumor recurrence or tumor-related death. Conclusion: It is difficult to make a preoperative diagnosis of inflammatory myofibroblastic tumor patients. Systematic lymph node dissection is not required in diagnosed patients. Complete resection is the most important prognostic factor, and it is critical to achieve this with the smallest resection possible.

Combined clinical and specific positron emission tomography/computed tomography-based radiomic features and machine-learning model in prediction of thymoma risk groups.

OBJECTIVES: In this single-center study, we aimed to propose a machine-learning model and assess its ability with clinical data to classify low- and high-risk thymoma on fluorine-18 (18F) fluorodeoxyglucose (FDG) (18F-FDG) PET/computed tomography (CT) images. METHODS: Twenty-seven patients (14 male, 13 female; mean age: 49.6 ± 10.2 years) who underwent PET/CT to evaluate the suspected anterior mediastinal mass and histopathologically diagnosed with thymoma were included. On 18F-FDG PET/CT images, the anterior mediastinal tumor was segmented. Standardized uptake value (SUV)max, SUVmean, SUVpeak, MTV and total lesion glycolysis of primary mediastinal lesions were calculated. For texture analysis first, second, and higher-order texture features were calculated. Clinical information includes gender, age, myasthenia gravis status; serum levels of lactate dehydrogenase (LDH), alkaline phosphatase, C-reactive protein, hemoglobin, white blood cell, lymphocyte and platelet counts were included in the analysis. RESULTS: Histopathologic examination was consistent with low risk and high-risk thymoma in 15 cases and 12 cases, respectively. The age and myasthenic syndrome were statistically significant in both groups (P = 0.039 and P = 0.05, respectively). The serum LDH level was also statistically significant in both groups (450.86 ± 487.07 vs. 204.82 ± 59.04; P < 0.001). The highest AUC has been achieved with MLP Classifier (ANN) machine learning method, with a range of 0.830 then the other learning classifiers. Three features were identified to differentiate low- and high-risk thymoma for the machine learning, namely; myasthenia gravis, LDH, SHAPE_Sphericity [only for 3D ROI (nz>1)]. CONCLUSIONS: This small dataset study has proposed a machine-learning model by MLP Classifier (ANN) analysis on 18F-FDG PET/CT images, which can predict low risk and high-risk thymoma. This study also demonstrated that the combination of clinical data and specific PET/CT-based radiomic features with image variables can predict thymoma risk groups. However, these results should be supported by studies with larger dataset.

A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors.

Radiomics is a new image processing technology developed in recent years. In this study, CT radiomic features are evaluated to differentiate pulmonary hamartomas (PHs) from pulmonary carcinoid tumors (PCTs). A total of 138 patients (78 PCTs and 60 PHs) were evaluated. The Radcloud platform (Huiying Medical Technology Co., Ltd., Beijing, China) was used for managing the data, clinical data, and subsequent radiomics analysis. Two hand-crafted radiomics models are prepared in this study: the first model includes the data regarding all of the patients to differentiate between the groups; the second model includes 78 PCTs and 38 PHs without signs of fat tissue. The separation of the training and validation datasets was performed randomly using an (8:2) ratio and 620 random seeds. The results revealed that the MLP method (RF) was best for PH (AUC = 0.999) and PCT (AUC = 0.999) for the first model (AUC = 0.836), and PC (AUC = 0.836) in the test set for the second model. Radiomics tumor features derived from CT images are useful to differentiate the carcinoid tumors from hamartomas with high accuracy. Radiomics features may be used to differentiate PHs from PCTs with high levels of accuracy, even without the presence of fat on the CT. Advances in knowledge: CT-based radiomic holds great promise for a more accurate preoperative diagnosis of solitary pulmonary nodules (SPNs).

Comparison of outcomes between neoadjuvant treatment followed by surgery and surgical treatment alone in patients with locally advanced esophageal cancer: Our single-center experience.

Background: This study aims to investigate the effect of neoadjuvant therapy on overall survival and recurrence-free survival in locally advanced esophageal cancer patients. Methods: Between January 2010 and December 2019, a total of 143 patients (84 males, 59 females; mean age: 58.8±11.5 years; range, 26 to 87 years) operated for esophageal cancer were retrospectively analyzed. A comparison was made between the groups of 42 patients who underwent direct surgery and 42 patients who underwent surgery after neoadjuvant therapy. The patients were selected by matching one to one with propensity score with a sensitivty of 0.054. Results: Pathological complete response was observed in 21 (50%) of 42 patients who received neoadjuvant therapy. No progression was detected in any of the patients. While the five-year overall survival rate was 58.3% in patients with a pathologic complete response, this rate was 52.8% in patients without a complete response (p=0.709). The five-year overall survival rate was 8% (median 22.3 months) in patients who did not receive neoadjuvant therapy and it was 52.9% (median 62.5 months) in those who received neoadjuvant therapy (p<0.001). The five-year recurrence-free survival rate for patients who did not receive neoadjuvant therapy was 26.2% (median 14.5 months), whereas this rate was 41.3% (median 35 months) for patients who received neoadjuvant therapy (p=0.025). Conclusion: In patients with locally advanced esophageal cancer, the overall survival and disease-free survival rates are significantly better with surgical treatment after neoadjuvant chemotherapy/ neoadjuvant chemoradiotherapy compared to surgery alone.

A New "Ice Globe" Method for Dilating Leaking Cervical Esophageal Strictures.

An esophageal stricture may develop during healing of a large esophageal perforation. When such a stricture occurs, mechanical dilatation is the treatment of choice. As in our case, if a cervical esophageal stricture and leakage are present together, the treatment becomes even more challenging. As a new treatment method, we made spherical ice globes of various sizes using molds created with a 3D printer to treat the esophageal stricture and prevent its progression. This method can be used to safely treat leaking cervical esophageal strictures. A step-by-step of how to do it has been described.

Clinical characteristics of lung cancer patients with COVID-19: Retrospective case series.

INTRODUCTION: One of the patient groups adversely affected during the COVID19 pandemic is those suffering with cancer. The aim of this study was to evaluate the clinical characteristics and outcomes of lung cancer (LC) patients with COVID-19. MATERIALS AND METHODS: Three thousand seven-hundred and fifty hospitalized patients with a presumptive diagnosis of COVID-19 in a tertiary referral hospital between March 2020-February 2021 were retrospectively evaluated. Among them, 36 hospitalized COVID-19 patients with a history of primary LC were included in the study. Univariate and multivariate analyses were carried out to assess the risk factors associated with severe disease. RESULT: Of the 36 patients included in the study, 28 (77%) were males and 8 (23%) were females. Median age was 67 years (min-max: 53-81 years). Six patients (17%) had a diagnosis of small cell LC, whereas 30 patients (83%) had a diagnosis of non-small cell LC. The most common symptoms were fever (n= 28, 77%), coughing and myalgia (n= 21, 58%) and dyspnea (n= 18, 50%). The most common radiological finding was ground glass opacity (GGO) (n= 30), of which 13 was bilateral and 17 was unilateral in distribution. Nearly 30% (n= 11) of LC patients with COVID-19 developed severe disease, 5% (n= 2) of the 36 patients were admitted to intensive care unit and all of these patients eventually expired. LC patients with COVID-19 and patchy consolidation on computed tomography of thorax (Th CT) on admission had a higher risk of developing severe disease in univariate (HR 2.41, 95%CI: 1.4- 4.4, p= 0.04) and multivariate Cox regression analysis (HR 0.48, 95%CI: 0.24-0.97, p= 0.03). CONCLUSIONS: Clinical characteristics, laboratory and radiographic findings were similar in LC patients with COVID-19 when compared with the general population, LC patients have a higher mortality rate than the general population, with a 5% mortality rate in our series. Our findings suggest that LC may be a risk factor associated with the prognosis of COVID-19 patients.

The management of postoperative COVID-19 pneumonia in a case with right pneumonectomy.

A 61-year-old male patient was hospitalized for the evaluation of a lung mass. The patient underwent right pneumonectomy. Although reverse-transcription polymerase chain reaction tests were negative for COVID-19, the diagnosis was supported by thoracic computed tomography. The patient responded to COVID-19 treatment, as evidenced by thoracic computed tomography. This case report highlights the importance of prompt diagnosis and treatment of COVID-19 in a patient who underwent pneumonectomy, which has high mortality and morbidity rates.

A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.

OBJECTIVES: To compare the diagnostic performance of a newly developed artificial intelligence (AI) algorithm derived from the fusion of convolution neural networks (CNN) versus human observers in the estimation of malignancy risk in pulmonary nodules. METHODS: The study population consists of 158 nodules from 158 patients. All nodules (81 benign and 77 malignant) were determined to be malignant or benign by a radiologist based on pathologic assessment and/or follow-up imaging. Two radiologists and an AI platform analyzed the nodules based on the Lung-RADS classification. The two observers also noted the size, location, and morphologic features of the nodules. An intraclass correlation coefficient was calculated for both observers and the AI; ROC curve analysis was performed to determine diagnostic performances. RESULTS: Nodule size, presence of spiculation, and presence of fat were significantly different between the malignant and benign nodules (p < 0.001, for all three). Eighteen (11.3%) nodules were not detected and analyzed by the AI. Observer 1, observer 2, and the AI had an AUC of 0.917 ± 0.023, 0.870 ± 0.033, and 0.790 ± 0.037 in the ROC analysis of malignity probability, respectively. The observers were in almost perfect agreement for localization, nodule size, and lung-RADS classification [κ (95% CI)=0.984 (0.961-1.000), 0.978 (0.970-0.984), and 0.924 (0.878-0.970), respectively]. CONCLUSION: The performance of the fusion AI algorithm in estimating the risk of malignancy was slightly lower than the performance of the observers. Fusion AI algorithms might be applied in an assisting role, especially for inexperienced radiologists. ADVANCES IN KNOWLEDGE: In this study, we proposed a fusion model using four state-of-art object detectors for lung nodule detection and discrimination. The use of fusion of deep learning neural networks might be used in a supportive role for radiologists when interpreting lung nodule discrimination.

CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: "Impact of surgical modality choice".

INTRODUCTION: Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. MATERIALS AND METHODS: In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report. RESULTS: Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis. CONCLUSIONS: The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

The prognostic value of 18F-fluorodeoxyglucose positron emission tomography/ computed tomography parameters in patients with malignant pleural mesothelioma.

BACKGROUND: In this study, we aimed to investigate the prognostic value of metabolic 18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters in malignant pleural mesothelioma patients. METHODS: A total of 65 patients with malignant pleural mesothelioma (34 males, 31 females; median age: 60 years; range, 39 to 84 years) who underwent whole-body 18F-fluorodeoxyglucose positron emission tomography/computed tomography for staging before treatment between March 2008 and January 2018 were included. Relationships between clinicopathological factors and 18F-fluorodeoxyglucose positron emission tomography/computed tomography parameters and overall survival were evaluated using a log-rank test and Cox regression analysis. RESULTS: The median follow-up was 13 (range, 4 to 55) months. The Kaplan-Meier analysis revealed a mean survival time of 17±2.6 months. The cumulative two- and five-year survival rates were 34.8% and 7.8%, respectively. Univariate analysis showed that ≥60 age, left hemithorax involvement, a maximum standardized uptake value of ≥9.8, c-T4 status, c-M1 status, and non-surgery were negatively associated with overall survival (p<0.05). Multivariate analysis showed that ≥60 age, left hemithorax involvement, a maximum standardized uptake value of ≥9.8, c-M1 status, and a total lesion glycolysis of ≥180.2 g were negatively associated with overall survival (p<0.05). CONCLUSION: Metabolic parameters of 18F-fluorodeoxyglucose positron emission tomography/computed tomography have the potential to provide prognostic information for malignant pleural mesothelioma patients who are receiving surgery and/or chemotherapy.

Different View on Tumor Size Dilemma in Tumor-Node-Metastasis Staging System for Thymoma.

BACKGROUND: Although tumor size is included in the definition of T descriptor in the tumor-node-metastasis (TNM) classification of many solid tumors, it is not considered for thymomas. This study aimed to assess the relationship of tumor diameters (the largest tumor diameter [LTD] and the mean tumor diameter [MTD]) with survival in thymoma patients undergoing surgical resection in a single center. METHODS: The study included 127 thymoma patients (age, 49.2 ± 15.2 years; 65 males), who were evaluated based on pathological tumor sizes according to the LTD and MTD ([largest diameter + shortest diameter] / 2) and divided into three subgroups for each parameter as: patients with an LTD of ≤5 cm, 5.1 to 10 cm, and >10 cm and patients with an MTD of ≤5, 5.1 to 10, and >10 cm. RESULTS: In thymoma patients, survival significantly differed according to the presence of myasthenia gravis (p = 0.018), resection status (R0 or R1; p = 0.001), T status (p = 0.015), and the Masaoka-Koga stage (p = 0.003). In the LTD subgroups, the overall survival of those with R0 resection was lower in those with an LTD of 5.1 to 10 cm than in those with an LTD of ≤5 cm (p = 0.051) and significantly lower in those with an MTD of 5.1 to 10 cm than in those with an MTD of ≤5 cm (p = 0.027). In the MTD subgroups, survival decreased as the tumor size increased. CONCLUSION: Both smaller tumor size and complete resection are associated with better survival in thymoma patients. Therefore, the largest or the mean tumor size might be considered as a criterion in the TNM staging for thymoma.

Complete resections of giant thorax masses and experienced difficulties.

INTRODUCTION: Thoracic giant masses do not have a clear definition. In some publications, giant thoracic mass definition is used in tumors whose long axis is> 10 cm and in other publications covering more than 50% of the hemithorax. In this study, demographic data of patients with a massive resectable giant thoracic mass and the difficulties and experiences experienced in the peroperative process were reviewed with a general perspective. MATERIALS AND METHODS: 14 giant intrathoracic masses operated at the department of Thoracic Surgery, School of Medicine, Ankara University were included in the study. The masses occupying more than half of the hemithorax and mediastinal lesions with a long axis of 15 cm or larger radiologically were included and evaluated. RESULT: 9 (64.3%) of our patients were male and 9 (35.7%) were female. The average age was 49.2 ± 17.1(between18-68). The tumor localizations of our patients were determined as 9 (64.2%) hemithorax and 5 (35.8%) mediastinal. When the radiological and intraoperative dimensions were examined separately, it was observed that the mean of long axis of CT image is average 18 ± 3.8 cm (between 12 cm and 26 cm), and the mean of long axis of specimen is average 18.14 ± 3.6 cm (between 15 cm and 23 cm). The heaviest mass was average 844 ± 473 g (350 g-2204 g). CONCLUSIONS: The surgical maneuvers and hence the excision of giant masses become difficult to operate due to the narrow localization of the masses and the frequent invasions of adjacent vascular structures and nerve tissues. However, complete resection of these slowly growing and generally encapsulated masses can provide the cure.

Effectiveness of intraoperative bimanual palpation in metastatic tumors of lung.

BACKGROUND: In this study, we aimed to compare effectiveness of thoracic computed tomography versus intraoperative bimanual palpation in the detection of number of nodules in patients undergoing thoracotomy. METHODS: Between January 2011 and January 2019, a total of 157 patients (63 males, 94 females; mean age: 46.6±11.2 years; range, 13 to 77 years) who underwent pulmonary metastasectomy in our institution were retrospectively analyzed. Metastatic nodules evaluated using thoracic computed tomography were compared with nodules detected by intraoperative palpation. RESULTS: A total of 226 muscle-sparing thoracotomy was performed in 157 patients. The time between the preoperative thoracic computed tomography and operation ranged from 3 to 24 days. Metastasectomy with muscle-sparing thoracotomy was performed in 41 (26%) patients two times, in eight (5%) patients three times, and in four (2.5%) patients four times due to bilateral lung metastasis or re-metastasectomy. The thoracic computed tomography could detect 476 metastatic nodules, while 1,218 nodules were palpated and resected intraoperatively. Of these nodules, 920 were pathologically evaluated as metastatic. CONCLUSION: Our study results showed that the number of nodules reported as pathologically malignant after resection was 1.9 times higher than those reported by thoracic computed tomography. This finding indicates that intraoperative bimanual examination significantly increases the possibility of complete resection. This situation raises the need for more caution for the thoracoscopic metastasectomy procedure in which there is no possibility of intraoperative bimanual palpation.