Prediction of post-operative pulmonary function after lobectomy for primary lung cancer: A comparison among counting method, effective lobar volume, and lobar collapsibility using inspiratory/expiratory CT

Preoperative three-dimensional lung volumetry predicts respiratory complications in patients undergoing major liver resection for colorectal metastases

Colorectal liver metastases (CRLM) are the predominant factor limiting survival in patients with colorectal cancer and liver resection with complete tumor removal is the best treatment option for these patients. This study examines the predictive ability of three-dimensional lung volumetry (3DLV) based on preoperative computerized tomography (CT), to predict postoperative pulmonary complications in patients undergoing major liver resection for CRLM. Patients undergoing major curative liver resection for CRLM between 2010 and 2021 with a preoperative CT scan of the thorax within 6 weeks of surgery, were included. Total lung volume (TLV) was calculated using volumetry software 3D-Slicer version 4.11.20210226 including Chest Imaging Platform extension ( http://www.slicer.org ). The area under the curve (AUC) of a receiver-operating characteristic analysis was used to define a cut-off value of TLV, for predicting the occurrence of postoperative respiratory complications. Differences between patients with TLV below and above the cut-off were examined with Chi-square or Fisher's exact test and Mann-Whitney U tests and logistic regression was used to determine independent risk factors for the development of respiratory complications. A total of 123 patients were included, of which 35 (29%) developed respiratory complications. A predictive ability of TLV regarding respiratory complications was shown (AUC 0.62, p = 0.036) and a cut-off value of 4500 cm3 was defined. Patients with TLV < 4500 cm3 were shown to suffer from significantly higher rates of respiratory complications (44% vs. 21%, p = 0.007) compared to the rest. Logistic regression analysis identified TLV < 4500 cm3 as an independent predictor for the occurrence of respiratory complications (odds ratio 3.777, 95% confidence intervals 1.488-9.588, p = 0.005). Preoperative 3DLV is a viable technique for prediction of postoperative pulmonary complications in patients undergoing major liver resection for CRLM. More studies in larger cohorts are necessary to further evaluate this technique.

Clinical application of computed tomographic volumetric imaging in postoperative lung function assessment in patients with lung cancer

BACKGROUND

To evaluate the effectiveness of the computed tomographic (CT) volumetric analysis in postoperative lung function assessment and the predicting value for postoperative complications in patients who had segmentectomy for lung cancer.

METHODS

CT scanning and pulmonary function examination were performed for 100 patients with lung cancer. CT volumetric analyses were performed by specific software, for the volume of the inspiratory phase (Vin), the mean inspiratory lung density (MLDin), the volume of expiratory phase (Vex), and the mean lung density at expiratory phase (MLDex). Pulmonary function examination results and CT volumetric analysis results were used to predict postoperative lung function. The concordance and correlations of these values were assessed by Bland-Altman analysis and Pearson correlation analysis, respectively. Multivariate binomial logistic regression analysis was executed to assess the associations of CT data with complication occurrence.

RESULTS

Correlations between CT scanning data and pulmonary function examination results were significant in both pre- and post-operation (0.8083 ≤ r ≤ 0.9390). Forced vital capacity (FVC), forced expiratory volume in the first second (FEV1), and the ratio of FVC and FEV1 estimated by CT volumetric analyses showed high concordance with those detected by pulmonary function examination. Preoperative (Vin-Vex) and (MLDex- MLDin) values were identified as predictors for post-surgery complications, with hazard ratios of 5.378 and 6.524, respectively.

CONCLUSIONS

CT volumetric imaging analysis has the potential to determine the pre- and post-operative lung function, as well as to predict post-surgery complication occurrence in lung cancer patients with pulmonary lobectomy.

Estimating lung function from computed tomography at the patient and lobe level using machine learning

BACKGROUND

Automated estimation of Pulmonary function test (PFT) results from Computed Tomography (CT) could advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases. Estimating lung function per lobe, which cannot be done with PFTs, would be helpful for risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.

PURPOSE

To automatically estimate PFT results from CT and furthermore disentangle the individual contribution of pulmonary lobes to a patient's lung function.

METHODS

We propose I3Dr, a deep learning architecture for estimating global measures from an image that can also estimate the contributions of individual parts of the image to this global measure. We apply it to estimate the separate contributions of each pulmonary lobe to a patient's total lung function from CT, while requiring only CT scans and patient level lung function measurements for training. I3Dr consists of a lobe-level and a patient-level model. The lobe-level model extracts all anatomical pulmonary lobes from a CT scan and processes them in parallel to produce lobe level lung function estimates that sum up to a patient level estimate. The patient-level model directly estimates patient level lung function from a CT scan and is used to re-scale the output of the lobe-level model to increase performance. After demonstrating the viability of the proposed approach, the I3Dr model is trained and evaluated for PFT result estimation using a large data set of 8 433 CT volumes for training, 1 775 CT volumes for validation, and 1 873 CT volumes for testing.

RESULTS

First, we demonstrate the viability of our approach by showing that a model trained with a collection of digit images to estimate their sum implicitly learns to assign correct values to individual digits. Next, we show that our models can estimate lobe-level quantities, such as COVID-19 severity scores, pulmonary volume (PV), and functional pulmonary volume (FPV) from CT while only provided with patient-level quantities during training. Lastly, we train and evaluate models for producing spirometry and diffusion capacity of carbon mono-oxide (DLCO) estimates at the patient and lobe level. For producing Forced Expiratory Volume in one second (FEV1), Forced Vital Capacity (FVC), and DLCO estimates, I3Dr obtains mean absolute errors (MAE) of 0.377 L, 0.297 L, and 2.800 mL/min/mm Hg respectively. We release the resulting algorithms for lung function estimation to the research community at https://grand-challenge.org/algorithms/lobe-wise-lung-function-estimation/ CONCLUSIONS: I3Dr can estimate global measures from an image, as well as the contributions of individual parts of the image to this global measure. It offers a promising approach for estimating PFT results from CT scans and disentangling the individual contribution of pulmonary lobes to a patient's lung function. The findings presented in this work may advance the use of CT in screening, diagnosis, and staging of restrictive pulmonary diseases as well as in risk assessment for pulmonary resection surgery and bronchoscopic lung volume reduction.

Depicting and predicting changes of lung after lobectomy for cancer by using CT images

Lobectomy is an effective and well-established therapy for localized lung cancer. This study aimed to assess the lung and lobe change after lobectomy and predict the postoperative lung volume. The study included 135 lung cancer patients from two hospitals who underwent lobectomy (32, right upper lobectomy (RUL); 31, right middle lobectomy (RML); 24, right lower lobectomy (RLL); 26, left upper lobectomy (LUL); 22, left lower lobectomy (LLL)). We initially employ a convolutional neural network model (nnU-Net) for automatically segmenting pulmonary lobes. Subsequently, we assess the volume, effective lung volume (ELV), and attenuation distribution for each lobe as well as the entire lung, before and after lobectomy. Ultimately, we formulate a machine learning model, incorporating linear regression (LR) and multi-layer perceptron (MLP) methods, to predict the postoperative lung volume. Due to the physiological compensation, the decreased TLV is about 10.73%, 8.12%, 13.46%, 11.47%, and 12.03% for the RUL, RML, RLL, LUL, and LLL, respectively. The attenuation distribution in each lobe changed little for all types of lobectomy. LR and MLP models achieved a mean absolute percentage error of 9.8% and 14.2%, respectively. Radiological findings and a predictive model of postoperative lung volume might help plan the lobectomy and improve the prognosis.

Comparison between functional lung volume measurement and segment counting for predicting postoperative pulmonary function after pulmonary resection in lung cancer patients

BACKGROUND

Functional lung volume (FLV) obtained from computed tomography images was a breakthrough for lung imaging and functional assessment. We compared the accuracy of the FLV measurement method and the segment-counting (SC) method in predicting postoperative pulmonary function.

METHODS

A total of 113 patients who underwent two thoracoscopic surgeries were enrolled in our study. We predicted postoperative pulmonary function by the FLV measurement method and the SC method. Novel formulas based on the FLV measurement method were established using linear regression equations between the factors affecting pulmonary function and the measured values.

RESULTS

The predicted postoperative forced vital capacity (ppoFVC) and forced expiratory volume in 1 s (ppoFEV1) measured by the 2 methods showed high concordance between the actual postoperative forced vital capacity (postFVC) and the forced expiratory volume in 1 s (postFEV1) [r = 0.762, P < 0.001 (FLV method) and r = 0.759, P < 0.001 (SC method) for FVC; r = 0.790, P < 0.001 (FLV method) and r = 0.795, P < 0.001 (SC method) for FEV1]. Regression analysis showed that the measured preoperative pulmonary function parameters (FVC, FEV1) and the ratio of reduced FLV to preoperative FLV were significantly associated with the actual postoperative values and could predict these parameters (all P < 0.001). The feasibility of using these equations [postFVC = 0.8 × FVC - 0.784 × ΔFLV/FLV + 0.283 (R² = 0.677, RSD = 0.338), postFEV1 = 0.766 × FEV1 - 0.694 × ΔFLV/FLV + 0.22 (R² = 0.743, RSD = 0.265)] to predict the pulmonary function parameters after wedge resection was also verified.

CONCLUSIONS

The new FLV measurement method is valuable for predicting postoperative pulmonary function in patients undergoing lung resection surgery, with accuracy and consistency similar to those of the conventional SC method.

What Do We Talk About Now When We Talk About Segmentectomy for GGO?

Segmentectomy has been considered as a compromised procedure in patients with early-stage lung cancer who could not tolerate standard lobectomy. By computed tomography (CT) screening, lung cancers are increasingly detected in earlier stages, especially those appearing as ground glass opacity (GGO)-containing lesions on CT scan. This has led to the revival of segmentectomy as an intentional procedure with the aim of curing selected patients, as GGO-containing lesions represent a special group of diseases that are relatively indolent in nature and seldom have lymphatic involvement. Limited resections, especially anatomical segmentectomy, may, thus, be helpful in reducing perioperative risks and preserving higher pulmonary function for patients while retaining similar oncological outcomes. However, clinical trials focusing specifically on the role of segmentectomy in the treatment of GGO-containing lung cancers are still lacking, especially in the minimally invasive surgery setting. Emerging evidence suggests that for such lesions, the oncological non-inferiority of segmentectomy to standard lobectomymay not be limited to lesions with a size ≤ 2 cm. More importantly, it is still unclear whether segmentectomy could indeed minimize perioperative risks and to what extent it could help preserve higher pulmonary function in good-risk patients with less extent of lung parenchyma resection. Hence, it is critical to reevaluate the efficacies of minimally invasive segmentectomy including not only oncological outcomes but also perioperative results and pulmonary function changes compared with lobectomy in good-risk patients with GGO-containing lung cancers. All these remain to be explored in future studies and robust evidence is still needed to prove that patients would indeed benefit from the combination of segmentectomy and minimally invasive surgery.

A Modified Calculation Improves the Accuracy of Predicted Postoperative Lung Function Values in Lung Cancer Patients

PURPOSE

Preoperative pulmonary function testing is mandatory for non-small cell lung cancer (NSCLC) surgery. The predicted postoperative FEV1 (ppoFEV1) is used for further risk stratification. We compared the ppoFEV1 with the postoperative FEV1 (postFEV1) in order to improve the calculation of the ppoFEV1.

METHODS

87 patients voluntarily received an FEV1 assessment 1 year after surgery. ppoFEV1 was calculated according to the Brunelli calculation. Baseline characteristics and surgical procedure were compared in a uni- and multivariate analysis between different accuracy levels of the ppoFEV1. Parameters which remained significant in the multinominal regression analysis were evaluated for a modification of the ppoFEV1 calculation.

RESULTS

Independent factors for a more inaccurate ppoFEV1 were preoperative active smoking (odds ratio (OR) 4.1, confidence interval (CI) 3.6-6.41; p = 0.01), packyears (OR 4.1, CI 3.6-6.41; p = 0.008), younger age (OR 1.1, CI 1.01-1.12; p = 0.03), and patients undergoing pneumectomy (OR 5.55, CI 1.35-23.6; p = 0.01). For the customized ppoFEV1 we excluded pneumonectomies. For patients < 60 years, an additional lung segment was added to the calculation. ppoFEV1 = preFEV1 × [Formula: see text]. For actively smoking patients with more than 30 packyears we subtracted one lung segment from the calculation ppoFEV1 = PreFEV1 × [Formula: see text].

CONCLUSION

We were able to enhance the predictability of the ppoFEV1 with modifications. The modified ppoFEV1 (1.828 l ± 0.479 l) closely approximates the postFEV1 of 1.823 l ± 0.476 l, (0.27%) while the original ppoFEV1 calculation is at 1.78 l ± 0.53 (2.19%). However, if patients require pneumectomy, more complex techniques to determine the ppoFEV1 should be included to stratify risk.

The impact of surgical chest wall damage caused by classic thoracotomy on pulmonary function and morphology

OBJECTIVES

Postoperative changes in pulmonary function (PF) and morphology due to surgical chest wall damage by thoracotomy with rib resection are unclear. Therefore, we evaluated the effects of surgical damage on PF and morphology at > 6 months postoperatively by comparing different lung lobectomy approaches.

METHODS

A total of 140 patients who underwent lobectomy for lung diseases between January 2006 and March 2016 were analyzed. Patients who underwent PF tests and computed tomography (CT) scans preoperatively and postoperatively were divided into posterolateral thoracotomy with one rib resection (PT) group and video-assisted thoracoscopic surgery (VATS) group. A 1:1 propensity score-matched (PSM) analysis was used to balance clinically important confounders between the groups. Regarding morphology, lung volume was measured semi-automatically using image analysis software and reconstructed three-dimensional (3D) images.

RESULTS

After PSM, 31 patients in each group were compared. Perioperative reduction ratios in forced vital capacity (FVC) (- 23% vs. - 13%; P = 0.006) and forced expiratory volume in 1 s (FEV1) (- 19% vs. - 12%; P = 0.02) were significantly larger for the PT group. No significant differences in lung volume values based on 3D CT volumetry (PT vs. VATS; total lung volume: - 7.9% vs. - 7.2%, P = 0.82; non-resected ipsilateral lung volume: + 36% vs. + 40%, P = 0.69; contralateral lung volume: + 9.3% vs. + 9.4%, P = 0.98) were found in either group.

CONCLUSIONS

Among the patients underwent lobectomy, classic thoracotomy decreased PF by an additional FVC loss of 10% and FEV1 loss of 7% compared with VATS, without affecting residual lung volume.

Predicting Postoperative Lung Function Following Lung Cancer Resection: A Systematic Review and Meta-analysis

BACKGROUND

Lung resection remains the gold standard treatment for early stage lung cancer; prediction of postoperative lung function is a key selection criterion for surgery with the aim of determining risk of postoperative dyspnoea. We aimed to identify the different prediction techniques used, and compare their accuracy.

METHODS

A systematic review and meta-analysis sought to synthesise studies conducted that assess prediction of postoperative lung function up to 18/02/2018 (n = 135). PROBAST was used to assess risk of bias in studies, 17 studies were judged to be at low risk of bias.

FINDINGS

Meta-analysis revealed CT volume and density measurement to be the most accurate (mean difference 71 ml) and precise (standard deviation 207 ml) of the reported techniques used for predicting FEV1; evidence for predicting gas transfer was lacking.

INTERPRETATION

The evidence suggests using CT volume and density is the preferred technique in the prediction of postoperative FEV1. Further studies are required to ensure that the methods and thresholds we propose are linked to patient reported outcomes.

FUNDING

Salary support for NKO, RM, PN, BN, and AMT was provided by University Hospitals Birmingham NHS Foundation Trust.

Unilateral Chronic Lung Allograft Dysfunction Assessed by Biphasic Computed Tomographic Volumetry in Bilateral Living-donor Lobar Lung Transplantation

Background

Early diagnosis of unilateral chronic lung allograft dysfunction (CLAD) is difficult because the unaffected contralateral lung functions as a reservoir in bilateral living-donor lobar lung transplantation (LDLLT). We previously reported the usefulness of ¹³³Xe ventilation scintigraphy for detection of unilateral change, but the supply of ¹³³Xe has been stopped globally. The present study aimed to examine the usefulness of inspiratory and expiratory computed tomography (I/E CT) volumetry for detection of unilateral change in CLAD patients.

Methods

This was a retrospective single-center, observational study using prospectively collected data. A total of 58 patients who underwent bilateral LDLLT from August 2008 to February 2017 were analyzed. Respiratory function tests, I/E CT were prospectively conducted. ΔLung volume was defined as the value obtained by subtracting expiratory lung volume from inspiratory lung volume.

Results

Fourteen (24%) cases were clinically diagnosed with CLAD, of which 10 (71%) were diagnosed as unilateral CLAD. ΔLung volume of bilateral lungs strongly correlated with forced vital capacity (r = 0.92, P < 0.01) and forced expiratory volume in 1 second (r = 0.80, P < 0.01). Regardless the phenotypes (bronchiolitis obliterans syndrome or restrictive allograft syndrome) of CLAD, Δlung volume onset/baseline significantly decreased compared with that in the non-CLAD group. Among the 10 unilateral CLAD patients, 3 with clinically suspected unilateral rejection yet did not show a 20% decline in forced expiratory volume in 1 second. In 2 of these, Δlung volume of unilateral lungs on the rejection side decreased by 20% or more.

Conclusions

Our findings suggest that I/E CT volumetry may be useful for assessment and early diagnosis of unilateral CLAD after bilateral LDLLT.

CT Radiomics in Thoracic Oncology: Technique and Clinical Applications

Precision medicine offers better treatment options and improved survival for cancer patients based on individual variability. As the success of precision medicine depends on robust biomarkers, the requirement for improved imaging biomarkers that reflect tumor biology has grown exponentially. Radiomics, the field of study in which high-throughput data are generated and large amounts of advanced quantitative features are extracted from medical images, has shown great potential as a source of quantitative biomarkers in the field of oncology. Radiomics provides quantitative information about the morphology, texture, and intratumoral heterogeneity of the tumor itself as well as features related to pulmonary function. Hence, radiomics data can be used to build descriptive and predictive clinical models that relate imaging characteristics to tumor biology phenotypes. In this review, we describe the workflow of CT radiomics, types of CT radiomics, and its clinical application in thoracic oncology.

Impact of mild to moderate COPD on feasibility and prognosis in non-small cell lung cancer patients who received chemotherapy

Background

Non-small cell lung cancer (NSCLC) is the predominant cause of death in patients with COPD, and the severity of COPD in NSCLC patients is classified mainly as mild to moderate. Most advanced NSCLC patients with mild to moderate COPD are treated with chemotherapy; however, the feasibility for and prognosis after chemotherapy of these patients are not well understood. The aim of this study was to elucidate the impact of mild to moderate COPD on the feasibility for and prognosis after chemotherapy in NSCLC patients.

Patients and methods

A retrospective review was performed on 268 NSCLC patients who received first-line chemotherapy from 2009 to 2014 in our institution. Finally, 85 evaluable patients were included in this study. The clinical characteristics, toxicity profile, objective response rate, and prognosis were analyzed and compared between patients with mild to moderate COPD and those without COPD (non-COPD).

Results

Forty-three patients were classified as COPD (27 cases mild and 16 cases moderate) and 42 patients as non-COPD. The COPD group were older and had fewer never-smokers than the non-COPD group. The objective response rate did not differ between groups (p=0.14). There was no significant difference in overall survival between COPD and non-COPD groups (15.0 and 17.0 months, log-rank test p=0.57). In the multivariate Cox's proportional hazard model, the adjusted hazard ratio (HRadj) was statistically significant for male sex (HRadj =5.382, 95% CI: 1.496-19.359; p=0.010), pathological diagnosis of adenocarcinoma (HRadj =0.460, 95% CI: 0.223-0.948; p=0.035), and epithelial growth factor receptor negative mutation (HRadj =6.040, 95% CI: 1.158-31.497; p=0.033), but not for the presence of COPD (HRadj =0.661, 95% CI: 0.330-1.325; p=0.24). Toxicity profile in COPD group was favorable, as in the non-COPD group.

Conclusion

Mild to moderate COPD did not have a significant deleterious impact on toxicity and prognosis in NSCLC patients.