Preoperative Chest Computed Tomography Research Articles

Computed tomography-based 3D convolutional neural network deep learning model for predicting micropapillary or solid growth pattern of invasive lung adenocarcinoma

PurposeTo investigate the value of a computed tomography (CT)-based deep learning (DL) model to predict the presence of micropapillary or solid (M/S) growth pattern in invasive lung adenocarcinoma (ILADC).Materials and MethodsFrom June 2019 to October 2022, 617 patients with ILADC who underwent preoperative chest CT scans in our institution were randomly placed into training and internal validation sets in a 4:1 ratio, and 353 patients with ILADC from another institution were included as an external validation set. Then, a self-paced learning (SPL) 3D Net was used to establish two DL models: model 1 was used to predict the M/S growth pattern in ILADC, and model 2 was used to predict that pattern in ≤ 2-cm-diameter ILADC.ResultsFor model 1, the training cohort’s area under the curve (AUC), accuracy, recall, precision, and F1-score were 0.924, 0.845, 0.851, 0.842, and 0.843; the internal validation cohort’s were 0.807, 0.744, 0.756, 0.750, and 0.743; and the external validation cohort’s were 0.857, 0.805, 0.804, 0.806, and 0.804, respectively. For model 2, the training cohort’s AUC, accuracy, recall, precision, and F1-score were 0.946, 0.858, 0.881,0.844, and 0.851; the internal validation cohort’s were 0.869, 0.809, 0.786, 0.794, and 0.790; and the external validation cohort’s were 0.831, 0.792, 0.789, 0.790, and 0.790, respectively. The SPL 3D Net model performed better than the ResNet34, ResNet50, ResNeXt50, and DenseNet121 models.ConclusionThe CT-based DL model performed well as a noninvasive screening tool capable of reliably detecting and distinguishing the subtypes of ILADC, even in small-sized tumors.

Preoperative Sarcopenia Assessment Using Pectoralis Muscle Mass Indicated Poor Mid-term Cardiac Surgery Prognosis

Background: Many studies have defined sarcopenia based on psoas muscle mass using abdominal computed tomography (CT). We hypothesized that sarcopenia can be assessed by measuring pectoralis muscle mass on chest CT and aimed to examine its relationship with the postoperative prognosis of cardiac surgery. Methods: This retrospective study included 189 patients who underwent cardiac surgery via median sternotomy between July 2020 and June 2022. We excluded patients <70 years old, urgent/emergent cases, no chest CT within 90 days before surgery, and cases in which evaluation of the pectoralis muscle was impossible with CT. The pectoralis muscle area (PMA) was measured using a preoperative chest CT. The sarcopenia cut-off value was defined as the lowest sex-specific tertile in PMA at the level of the 4th thoracic vertebrae. Results: Eighty patients were included. The lower tertile were classified as the sarcopenia group (SG) (n = 26) and the rest as the non-sarcopenia group (NSG) (n = 54). In the SG, 1-year survival was significantly worse than that in NSG (NSG: 92.7% vs. SG: 54.9%, p < 0.0001). In the multivariate model, sarcopenia was an independent risk factor for mid-term all-cause death (hazard ratio, 4.89; 95% confidence interval: 1.14–21.0, p = 0.033). Conclusion: Preoperative sarcopenia defined using PMA was associated with poor mid-term survival after elective cardiac surgery via median sternotomy. The pectoralis muscle mass observed through a chest CT could be used for preoperative risk scoring in older patients undergoing cardiac surgery.

Clinicopathological and computed tomography features associated with recurrence-free survival of patients with small-sized peripheral invasive lung adenocarcinoma after sublobectomy.

Sublobar resection is gradually becoming a standard treatment for small-sized (≤2 cm) peripheral non-small cell lung cancer (NSCLC), with lung adenocarcinoma (LADC) being the most frequent histologic subtype. However, the prognostic predictors for preoperatively determining whether sublobectomy is feasible for patients with early LADC have not yet been well identified. Therefore, this study aimed to investigate the clinicopathological and computed tomography (CT) features associated with the recurrence-free survival (RFS) of patients with small-sized invasive LADC (SILADC) after sublobar resection. This retrospective cohort study analyzed 107 patients with SILADC who underwent preoperative chest CT scan and sublobar resection from December 2012 to March 2019. The Kaplan-Meier survival was used to analyze the relationship between clinicopathological characteristics, preoperative chest CT findings, and RFS. The Cox proportional hazards regression was used to identify independent prognostic factors of poor RFS. For clinicopathological characteristics, RFS was shorter in patients aged ≥70 years, smokers, and those with micropapillary/solid-predominant adenocarcinomas (all P values <0.05). For preoperative CT features, RFS was shorter in patients with tumor size ≥1.4 cm, solid component size ≥1.1 cm, proportion of solid component ≥72%, solid density, spiculation, vascular convergence sign, peripheral fibrosis, and type II pleural tag (all P values <0.05). Multivariate analysis showed proportion of solid component ≥72% [hazard ratio (HR): 5.920; P=0.006; 95% confidence interval (CI): 1.686-20.794], spiculation (HR: 5.026; P=0.001; 95% CI: 2.008-12.581), and type II pleural tag (HR: 4.638; P=0.002; 95% CI: 1.773-12.136) were independent risk factors for poor prognosis in patients with SILADC after sub-lobectomy. Clinicopathological and CT characteristics are helpful for predicting the RFS of patients with SILADC after sublobar resection and can be used as an auxiliary tool for thoracic surgeons to choose the best surgical mode.

A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.

Chest computed tomography (CT) is a critical tool in the diagnosis of pulmonary cryptococcosis as approximately 30% of normal immunity individuals may not exhibit any significant symptoms or laboratory findings. Pulmonary cryptococcosis granuloma and lung adenocarcinoma can appear similar on noncontrast chest CT. This study evaluates the use of an integrated model that was developed based on radiomic features combined with demographic and radiological features to differentiate pulmonary cryptococcosis nodules from lung adenocarcinomas. Preoperative chest CT images for 215 patients with solid pulmonary nodules with histopathologically confirmed lung adenocarcinoma and cryptococcosis infection were collected from two clinical centers (108 cases in the training set and 107 cases in the test set divided by the different hospitals). Radiomics models were constructed based on nodular lesion volume (LV), 5-mm extended lesion volume (ELV), and perilesion volume (PLV). A demoradiological model was constructed using logistic regression based on demographic information (age, sex) and 12 radiological features (location, number, shape and specific imaging signs). Both models were used to build an integrated model, the performance of which was assessed using the test set. A junior and a senior radiologist evaluated the nodules. Receiver operating characteristic (ROC) curve analysis was conducted, and areas under the curve (AUCs), sensitivity (SEN), and specificity (SPE) of the models were calculated and compared. Among the radiomics models, AUCs of the LV, ELV, and PLV were 0.558, 0.757, and 0.470, respectively. Age, lesion number, and lobular sign were identified as independent discriminative features providing an AUC of 0.77 in the demoradiological model (SEN 0.815, SPE 0.642). The integrated model achieved the highest AUC of 0.801 (SEN 0.759, SPE 0.755), which was significantly higher than that obtained by a junior radiologist (AUC =0.689, P=0.024) but showed no significant difference from that of the senior radiologist (AUC =0.784, P=0.388). An integrated model with radiomics and demoradiological features improves discrimination of cryptococcosis granulomas from solid adenocarcinomas on noncontrast CT. This model may be an effective strategy for machine complementation to discrimination by radiologists, and whole-lung automated recognition methods might dominate in the future.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography.

Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Association Between Preoperative Sarcopenia and Early Postoperative Outcomes in Pediatric Patients Undergoing Total Correction of Tetralogy of Fallot: A Retrospective Cohort Study

To identify the association between preoperative low muscle mass and early postoperative outcomes in pediatric patients undergoing total correction of tetralogy of Fallot (TOF). A retrospective cohort study. A single university hospital in Seoul, South Korea. Pediatric patients (≤3 years) who underwent total correction of TOF between May 2008 and February 2018. None. Cross-sectional areas of the pectoralis and erector spinae muscles were measured using preoperative chest computed tomography (CT) scans, and adjusted to body surface area to define muscle mass index. The patients were divided into sarcopenia, presarcopenia, and no sarcopenia groups based on cutoff values determined using the mean and SD of the muscle mass index in the third z-weight quintile. Of a total of 330 patients included in the final analysis, 13 were associated with the sarcopenia group, 57 in the presarcopenia group, and 260 in the no sarcopenia group. The sarcopenia group exhibited a higher incidence of major adverse events than the presarcopenia and no sarcopenia groups, respectively (38% v 25% v 18%; p=0.033). Logistic regression analyses revealed that only younger age at the time of surgery was significantly associated with major adverse events (odds ratio 0.82; 95% CI 0.72-0.94, p=0.003). The incidence of sarcopenia, as assessed by preoperative chest CT, was low in pediatric patients undergoing total correction of TOF, and preoperative sarcopenia did not predict early postoperative major adverse events.

CT-derived body composition associated with lung cancer recurrence after surgery

ObjectivesTo evaluate the impact of body composition derived from computed tomography (CT) scans on postoperative lung cancer recurrence. MethodsWe created a retrospective cohort of 363 lung cancer patients who underwent lung resections and had verified recurrence, death, or at least 5-year follow-up without either event. Five key body tissues and ten tumor features were automatically segmented and quantified based on preoperative whole-body CT scans (acquired as part of a PET-CT scan) and chest CT scans, respectively. Time-to-event analysis accounting for the competing event of death was performed to analyze the impact of body composition, tumor features, clinical information, and pathological features on lung cancer recurrence after surgery. The hazard ratio (HR) of normalized factors was used to assess individual significance univariately and in the combined models. The 5-fold cross-validated time-dependent receiver operating characteristics analysis, with an emphasis on the area under the 3-year ROC curve (AUC), was used to characterize the ability to predict lung cancer recurrence. ResultsBody tissues that showed a standalone potential to predict lung cancer recurrence include visceral adipose tissue (VAT) volume (HR = 0.88, p = 0.047), subcutaneous adipose tissue (SAT) density (HR = 1.14, p = 0.034), inter-muscle adipose tissue (IMAT) volume (HR = 0.83, p = 0.002), muscle density (HR = 1.27, p < 0.001), and total fat volume (HR = 0.89, p = 0.050). The CT-derived muscular and tumor features significantly contributed to a model including clinicopathological factors, resulting in an AUC of 0.78 (95% CI: 0.75–0.83) to predict recurrence at 3 years. ConclusionsBody composition features (e.g., muscle density, or muscle and inter-muscle adipose tissue volumes) can improve the prediction of recurrence when combined with clinicopathological factors.

Anatomical view of thoracodorsal artery variants using computed tomography angiography.

BACKGROUND The latissimus dorsi (LD) muscle has a dominant pedicle with one thoracodorsal artery and receives sufficient blood by segmental circulation through several perforators. Thus, it is widely used in various reconstructive surgeries. We are reporting on the patterns of the thoracodorsal artery analyzed by chest CT angiography. PATIENTS AND METHODS We analyzed the preoperative chest CT angiography results of 350 patients scheduled to undergo LD flap breast reconstruction following complete mastectomy for breast cancer between October 2011 and October 2020. RESULTS 700 blood vessels were classified according to the KNUPS TDA classification, 388 (185 Rt. and 203 Lt.), 126 (64 Rt. and 62 Lt.), 91 (49 Rt. and 42 Lt.), 57 (27 Rt. and 30 Lt.), and 38 (25 Rt. and 13 Lt.) vessels were classified as Type I, II, III, IV, and V, respectively. Among 350 patients, 205 patients showed matching types for Lt. and Rt. vessels, whereas 145 patients showed mismatching types. For 205 patients with matching types, the distribution by type was 134, 30, 30, 7, and 4 patients with Type I, II, III, IV, and V, respectively. For 145 patients with mismatching types, the distribution by different combinations was 48, 25, 28, 19, 2, 9, 7, 3, 1, and 3 patients with Types I+II, I+III, I+IV, I+V, II+III, II+IV, II+V, III+IV, III+V, and IV+V, respectively. CONCLUSIONS While there is some diversity in the vascular anatomical structures of the LD flap, the dominant vessel can be found in a similar location in almost all cases and no flap had absence of a dominant vessel. Therefore, in surgical procedures using the thoracodorsal artery as the pedicle, preoperative radiological confirmation is not absolutely necessary; however, due to variants, performing the surgery with an understanding of this aspect should lead to good outcomes.

HPB P39 Do indeterminate pulmonary nodules in patients undergoing resection for pancreas cancer influence pulmonary metastasis and survival outcomes?

Abstract Background The significance of indeterminate pulmonary nodules (IPNs) on preoperative imaging in the surgical resection of pancreatic cancers remains a clinical dilemma. IPNs are defined as ≥ 1 well-defined, non-calcified lung nodules that are ≤ 1cm in diameter. We report the prevalence and impact of IPNs in surgically managed pancreatic cancer patients from our centre. Methods We studied patients who had attempted/successful surgical resection of pancreatic cancers between 2014 and 2020. The summary statistics are presented as frequencies and percentages, while Fisher's exact test was used to calculate p value for the compared variables including the presence of IPNs, pulmonary metastasis and death. P value of &amp;lt;0.05 was considered statistically significant. Results Our study included 262 patients – 204 (77.9%) had surgical resections including pancreaticoduodenectomy (with or without portal venous resection) or total or distal pancreatectomy, while 58 (22.1%) had trial dissection, bypass and/or laparoscopy. All patients had a preoperative chest computed tomography (CT) scan. 25/262 (9.5%) had IPN on preoperative CT scans. 15/25 (60%) had solitary IPN, 2/25 (8%) had two IPNs, 3/25 (12%) had three IPNs, 1/25 (4%) had 6 IPNs and 4/25 (16%) reported multiple/plural IPNs. In patients with more than 1 IPN, only one patient had unilateral pulmonary nodules. 23/262 (8.8%) developed pulmonary metastasis. Of these, none had IPN preoperatively. There was no statistically significant association between IPN and pulmonary metastasis (p = 0.1428). Neoadjuvant chemotherapy was given to 13/262 patients. None of these patients had IPN pre-operatively, but 4 of them had recurrent/metastatic disease (3 Liver &amp; 2 Pulmonary). IPN was found in 3 patients on post-operative imaging, all three developed metastatic disease after pancreaticoduodenectomies. As at 31st of October 2021, 177/262 patients had died. There was a statistically significant association between pulmonary metastasis and mortality (p = 0.0379). On the other hand, an association between IPN and mortality showed no statistical significance (p = 1.0). Conclusions Our study suggests that IPN is not linked to pulmonary metastasis in pancreatic cancer patients that were offered surgical resection. The presence of IPN did not impact mortality. Therefore, we conclude that IPN should not be used as a determining factor for surgical management of pancreatic cancers.

Preoperative Computed Tomography Radiomics Analysis for Predicting Receptors Status and Ki-67 Levels in Breast Cancer.

To assess the prediction performance of preoperative chest computed tomography (CT) based radiomics features for estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2+), and Ki-67 status of breast cancer. This study enrolled 108 breast cancer patients who received preoperative chest CT examinations in our institution from July 2018 to January 2020. Radiomics features were separately extracted from nonenhanced, arterial, and portal-venous phases CT images. The least absolute shrinkage and selection operator logistic regression was used for feature selection. Then the radiomics signatures for each phase and a combined model of 3 phases were built. Finally, the receiver operating characteristic curves and calibration curves were used to confirm the performance of the radiomics signatures and combined model. In addition, the decision curves were performed to estimate the clinical usefulness of the combined model. The 20 most predictive features were finally selected to build radiomics signatures for each phase. The combined model achieved the overall best performance than using either of the nonenhanced, arterial and portal-venous phases alone, achieving an area under the receiver operating characteristic curve of 0.870 for ER+ versus ER-, 0.797 for PR+ versus PR-, 0.881 for HER2+ versus HER2-, and 0.726 for Ki-67. The decision curve demonstrated that the CT-based radiomics features were clinically useful. This study indicated preopreative chest CT radiomics analysis might be able to assess ER, PR, HER2+, and Ki-67 status of breast cancer. The findings need further to be verified in future larger studies.

Should We Always Perform Preoperative Chest Computed Tomography in Patients with cT1a Renal Cell Carcinoma?

No definitive criteria regarding the performance of preoperative chest computed tomography (CT) in patients with cT1a renal cell carcinoma (RCC) exists. We aimed to establish an objective standard for the optimal timing of preoperative chest CT in patients with RCC. Data from 890 patients who underwent surgical treatment for RCC between January 2011 and December 2020 were retrospectively collected. The primary endpoint was detection of lung metastasis on chest CT before nephrectomy. A multivariable logistic regression model predicting positive chest CT scans was used. Predictors included preoperative cTN stage, presence of systemic symptoms, Charlson comorbidity index (CCI), platelet count/hemoglobin ratio, albumin/globulin ratio (AGR), and De Ritis ratio. The overall rate of positive chest CT scans before nephrectomy was 3.03% (27/890). Only one patient had lung metastasis before surgery for cT1a. cT stage (≥cT1b), CCI ≥4, and low AGR were associated with a higher risk of positive chest CT scans. The best cutoff value for AGR was 1.39. After 890-sample bootstrap validation, the concordance index was 0.80. The net benefit of the proposed strategy was superior to that of the select-all and select-none strategies according to decision curve analysis. Therefore, when chest CT scans were performed with a risk of a positive result ≥10%, 532 (59.8%) negative chest CT scans could be prevented. Only 24 (2.7%) potentially positive chest CT scans were misdiagnosed. Therefore, we recommend chest CT in patients with ≥cT1b disease, CCI ≥4, and low AGR.

Comparison of Two-dimensional and Three-dimensional Features of Chest CT in the Diagnosis of Invasion of Pulmonary Ground Glass Nodules

At present, more and more studies predict invasive adenocarcinoma (IAC) through three-dimensional features of pulmonary nodules, but few studies have confirmed that three-dimensional features have more advantages in diagnosing IAC than traditional two-dimensional features of pulmonary nodules. This study analyzed the differences of chest computed tomography (CT) features between IAC and minimally invasive adenocarcinoma (MIA) from three-dimensional and two-dimensional levels, and compared the ability of diagnosing IAC. The non-invasive adenocarcinoma group includes precursor glandular lesions (PGL) and minimally invasive adenocarcinoma (MIA). The clinical data of 1,045 patients with ground glass opacity (GGO) from January to December 2019 were collected. Then the correlation between preoperative CT image characteristics and pathological results were analyzed retrospectively. The independent influencing factors for the identification of IAC were screened out according to two-dimensional and three-dimensional classification by multivariate Logistic regression and the cut-off point for the identification of IAC was found out through the receiver operating characteristic (ROC) curve. At last, the ability of diagnosing IAC was evaluated by Yoden index. The diameter of nodule, the diameter of solid component, the diameter of mediastinal window nodule in two-dimensional factors, and the volume of nodule, the volume of solid part and the average CT value in three-dimensional factors were independent risk factors for the diagnosis of IAC. These factors were arranged by Yoden index: solid partial volume (0.601)>nodule volume (0.536)>solid component diameter (0.525)>nodule diameter (0.518)>mediastinal window nodule diameter (0.488)>proportion of solid component volume (0.471)>1-tumor disappearance ratio (TDR) (0.468)>consolidation tumor ratio (CTR) (0.394)>average CT value (0.380). The CT features of three-dimensional are better than two-dimensional in the diagnosis of IAC, and the size of solid components is better than the overall size of nodules.

Contemporary Role of Computed Tomography in Managing Pediatric Primary Spontaneous Pneumothorax.

The value of chest computed tomography (CT) in pediatric primary spontaneous pneumothorax (PSP) remains controversial. This study sought to evaluate the utility of CT scans in a contemporary cohort of children with PSP. An institutional review board approval was obtained for a retrospective review of all children (aged ≤18y) who underwent video-assisted thoracoscopic surgery (VATS) for PSP between 2009 and 2019 at a university-affiliated pediatric hospital. Preoperative CT scans were evaluated for diagnostic accuracy of the CT of bleb disease. Thirty nine patients underwent VATS procedures for PSP, 34 (87%) of the patients were noted to have blebs. Twenty eight (72%) patients received preoperative CT scans with a 5.5:1 male to female ratio. On CT, 17 (61%) were diagnosed with blebs and all had blebs intraoperatively. CT did not identify disease in 11 patients, but seven had blebs intraoperatively. The positive and negative predictive values of preoperative CT for detecting ipsilateral bleb disease were 100% and 36%, respectively, with a sensitivity of 71%. Eleven patients had a contralateral disease on CT (39%). Five received elective contralateral VATS and three developed spontaneous PSP, with intraoperative blebs in all eight patients. Three never developed contralateral PSP. Six (21%) patients with no contralateral disease on CT developed spontaneous PSP with intraoperative blebs. The decision to operate for PSP should be made based on clinical findings rather than on the presence or absence of blebs identified by CT.

A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma.

BackgroundClinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.MethodsA total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram.ResultsThe predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768–0.796) in the primary cohort and 0.813 (95% CI, 0.787–0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful.ConclusionThis study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Asymptomatic Giant Left Atrial Appendage Cavernous Haemangiona Misdiagnosed as a Mediastinal Mass

A 50-year-old female was admitted because a chest computed tomography (CT) incidentally noted a mediastinal mass. Physical examination revealed a regular heart rate of 84 beats/minute and blood pressure of 124/88 mmHg. Moreover, no cardiac murmurs were auscultated. Preoperative contrast-enhanced chest CT demonstrated an 8.2 cm × 6.0 cm non-enhancing mass in the left anterior mediastinum (Figure 1A). No abnormalities were illustrated in the 3D heart vasculature reconstruction (Figure 1B) and echocardiogram (Figure 1C).

Sarcopenia on preoperative chest computed tomography predicts cancer-specific and all-cause mortality following pneumonectomy for lung cancer: A multicenter analysis.

BackgroundMortality risk prediction in patients undergoing pneumonectomy for non‐small cell lung cancer (NSCLC) remains imperfect. Here, we aimed to assess whether sarcopenia on routine chest computed tomography (CT) independently predicts worse cancer‐specific (CSS) and overall survival (OS) following pneumonectomy for NSCLC.MethodsWe included consecutive adults undergoing standard or carinal pneumonectomy for NSCLC at Massachusetts General Hospital and Heidelberg University from 2010 to 2018. We measured muscle cross‐sectional area (CSA) on CT at thoracic vertebral levels T8, T10, and T12 within 90 days prior to surgery. Sarcopenia was defined as T10 muscle CSA less than two standard deviations below the mean in healthy controls. We adjusted time‐to‐event analyses for age, body mass index, Charlson Comorbidity Index, forced expiratory volume in 1 second in % predicted, induction therapy, sex, smoking status, tumor stage, side of pneumonectomy, and institution.ResultsThree hundred and sixty‐seven patients (67.4% male, median age 62 years, 16.9% early‐stage) underwent predominantly standard pneumonectomy (89.6%) for stage IIIA NSCLC (45.5%) and squamous cell histology (58%). Sarcopenia was present in 104 of 367 patients (28.3%). Ninety‐day all‐cause mortality was 7.1% (26/367). After a median follow‐up of 20.5 months (IQR, 9.2–46.9), 183 of 367 patients (49.9%) had died. One hundred and thirty‐three (72.7%) of these deaths were due to lung cancer. Sarcopenia was associated with shorter CSS (HR 1.7, p = 0.008) and OS (HR 1.7, p = 0.003).ConclusionsThis transatlantic multicenter study confirms that sarcopenia on preoperative chest CT is an independent risk factor for CSS and OS following pneumonectomy for NSCLC.

Predictive value of chest computed tomography for axillary lymph node metastasis in patients with breast cancer: A retrospective cohort study

Purpose: This study aimed to evaluate the predictive value of preoperative chest computed tomography (CT) for axillary lymph node (ALN) metastasis in patients with breast cancer.Methods: CT features of ALNs were retrospectively reviewed in 212 patients with breast cancer who underwent preoperative chest CT examination and ALN dissection. Primary tumor size and CT characteristics of ALNs (cortical thickness, cortical shape, the presence or absence of contrast enhancement of ALNs, and the presence or absence of perinodal infiltration) were recorded and analyzed. A nomogram was developed to visualize the associations between the predictors and each ALN status endpoint.Results: Of 212 patients, 95 (44.8%) had ALN metastasis. Primary tumor size and CT characteristics of ALNs were identified as predictors of ALN metastasis. The nomogram comprising primary tumor size and cortical shape was a good validation model for predicting ALN metastasis. The sensitivity, specificity, and accuracy of the nomogram for predicting ALN metastasis were 88.4%, 79.5%, and 83.5%, respectively.Conclusion: Using preoperative chest CT scans, a nomogram combining the cortical shape of ALNs with the primary tumor size showed good performance in predicting ALN metastasis.

Deep Learning for the Detection of Breast Cancers on Chest Computed Tomography

BackgroundIncidental breast cancers can be detected on chest computed tomography (CT) scans. With the use of deep learning, the sensitivity of incidental breast cancer detection on chest CT would improve. This study aimed to evaluate the performance of a deep learning algorithm to detect breast cancers on chest CT and to validate the results in the internal and external datasets. Patients and MethodsThis retrospective study collected 1170 preoperative chest CT scans after the diagnosis of breast cancer for algorithm development (n = 1070), internal test (n = 100), and external test (n = 100). A deep learning algorithm based on RetinaNet was developed and tested to detect breast cancer on chest CT. ResultsIn the internal test set, the algorithm detected 96.5% of breast cancers with 13.5 false positives per case (FPs/case). In the external test set, the algorithm detected 96.1% of breast cancers with 15.6 FPs/case. When the candidate probability of 0.3 was used as the cutoff value, the sensitivities were 92.0% with 7.36 FPs/case for the internal test set and 93.0% with 8.85 FPs/case for the external test set. When the candidate probability of 0.4 was used as the cutoff value, the sensitivities were 88.5% with 5.24 FPs/case in the internal test set and 90.7% with 6.3 FPs/case in the external test set. ConclusionThe deep learning algorithm could sensitively detect breast cancer on chest CT in both the internal and external test sets.

Outcome of asymptomatic patients with positive SARS-CoV-2 viral RNA reverse transcriptase-PCR undergoing surgery: A matched cohort study.

Outcome of asymptomatic patients with positive SARS-CoV-2 viral RNA reverse transcriptase-PCR undergoing surgery: A matched cohort study.

Application of Multi-Slice Computed Tomography for the Preoperative Diagnosis and Classification of Pulmonary Cystic Echinococcosis.

Pulmonary cystic echinococcosis remains a serious threat to public health. A standardized, imaging-based classification method for pulmonary echinococcosis has not yet been developed despite the existence of a standardized ultrasound classification method and treatment plan for hepatic cystic echinococcosis. Chest computed tomography (CT) images from 34 cases of pulmonary cystic echinococcosis with 46 lesions were used for classification based on the World Health Organization (WHO) standardized ultrasound classification of hepatic cystic echinococcosis. CT findings were compared with intraoperative observations and postoperative pathological results to assess accuracy. Pulmonary cystic echinococcosis was common in women (14/34, 41.2%) and children (14/34, 41.2%) with a single cyst (28/46, 60.9%). Most lesions were classified as cystic echinococcosis 1(CE1, 19/46) or cystic echinococcosis 3(CE3, 21/46). Blood leukocytosis was mostly observed in CE3 lesions (100%, 9/9) (p < 0.05). The preoperative CT diagnosis of pulmonary cystic echinococcosis had an accuracy rate of 100%. The preoperative CT typing, and postoperative pathological typing had a coincidence rate of 97.8% (45/46). Our study provided a classification method based on CT imaging for pulmonary cystic echinococcosis that can be used during pre-surgical planning to reduce patient’s postoperative complications and mortality.