Changes In Lung Cancer Patients Research Articles

Integration of clinical phenoms and metabolomics facilitates precision medicine for lung cancer

Lung cancer is a common malignancy that is frequently associated with systemic metabolic disorders. Early detection is pivotal to survival improvement. Although blood biomarkers have been used in its early diagnosis, missed diagnosis and misdiagnosis still exist due to the heterogeneity of lung cancer. Integration of multiple biomarkers or trans-omics results can improve the accuracy and reliability for lung cancer diagnosis. As metabolic reprogramming is a hallmark of lung cancer, metabolites, specifically lipids might be useful for lung cancer detection, yet systematic characterizations of metabolites in lung cancer are still incipient. The present study profiled the polar metabolome and lipidome in the plasma of lung cancer patients to construct an inclusive metabolomic atlas of lung cancer. A comprehensive analysis of lung cancer was also conducted combining metabolomics with clinical phenotypes. Furthermore, the differences in plasma lipid metabolites were compared and analyzed among different lung cancer subtypes. Alcohols, amides, and peptide metabolites were significantly increased in lung cancer, while carboxylic acids, hydrocarbons, and fatty acids were remarkably decreased. Lipid profiling revealed a significant increase in plasma levels of CER, PE, SM, and TAG in individuals with lung cancer as compared to those in healthy controls. Correlation analysis confirmed the association between a panel of metabolites and TAGs. Clinical trans-omics studies elucidated the complex correlations between lipidomic data and clinical phenotypes. The present study emphasized the clinical importance of lipidomics in lung cancer, which involves the correlation between metabolites and the expressions of other omics, ultimately influencing clinical phenotypes. This novel trans-omics network approach would facilitate the development of precision therapy for lung cancer.Graphical 1. Integrating multiple biomarkers or trans-omics results improves diagnostic accuracy and reliability in heterogeneous lung cancer.2. Metabolomics and lipidomics, along with clinical phenotypes, construct a comprehensive metabolic profile of lung cancer patients.3. TAG expression shows strong positive correlation with polar metabolites, potentially impacting clinical phenotypic changes in lung cancer patients.

Modular Edge Analysis Reveals Chemotherapy-induced Brain Network Changes in Lung Cancer Patients.

Lung cancer patients with post-chemotherapy may have disconnected or weakened function connections within brain networks. This study aimed to explore the abnormality of brain functional networks in lung cancer patients with post-chemotherapy by modular edge analysis. Resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed on 40 patients after chemotherapy, 40 patients before chemotherapy and 40 normal controls. Patients in all three groups were age and sex well-matched. Then, modular edge analysis was applied to assess brain functional network alterations. Post-chemotherapy patients had the worst MoCA scores among the three groups (p < 0.001). In intra-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strengths in the occipital lobe module (p < 0.05). Compared with the nonchemotherapy group, the patients after chemotherapy had decreased connection strengths in the subcortical module (p < 0.05). In inter-modular connections, compared with normal controls, the patients after chemotherapy had decreased connection strength in the frontal-temporal lobe modules (p < 0.05). Compared with the non-chemotherapy group, the patients after chemotherapy had decreased connection strength in the subcortical-temporal lobe modules (p < 0.05). The results reveal that chemotherapy can disrupt connections in brain functional networks. As far as we know, the use of modular edge analysis to report changes in brain functional brain networks associated with chemotherapy was rarely reported. Modular edge analysis may play a crucial part in predicting the clinical outcome for the patients after chemotherapy.

Tracking Changes in Global Longitudinal Strain in Lung Cancer Patients Receiving Thoracic Radiation

Thoracic radiation improves survival in many lung cancer patients. However, radiation-induced cardiotoxicity is a major source of morbidity and mortality in such patients. Global longitudinal strain (GLS), a novel echocardiography (ECHO) method of assessing left ventricular function, has been shown to predict long-term adverse cardiovascular risk in diverse patient populations. We hypothesized that receipt of thoracic radiation is associated with GLS changes in lung cancer patients. We retrospectively identified patients with lung cancer treated at our institution between 2005-2020 who had ECHOs performed both before and after RT, and in whom GLS was extractable. ECHO Board-Certified cardio-oncologists measured GLS and left ventricular ejection fraction (LVEF) from these ECHOs. A total of 40 patients met inclusion criteria. Median time to ECHO was 78 days prior and 172 days after RT. Two chamber (2C), 3C, 4C, and average GLS were significantly decreased after RT on paired t-test [mean difference (SD) 2.23 (3.29), 2.99 (2.78), 2.25 (3.63), 2.51 (2.66) respectively, all p < 0.001]. Thirteen patients (32.5%) had abnormal GLS (<18%) prior to RT. 5 of those 13 patients (38.5%) had abnormal LVEF (< 50%). 27/40 patients (67.5%) had an abnormal GLS or clinically significant (≥15%) drop in GLS after RT. This difference (32.5% patients pre-RT vs 67.5% post-RT) was statistically significant (p < 0.01). Among patients (n = 27) who had normal LVEF before RT, 1 patient (3.7%) developed abnormal LVEF (<50%) after RT. Backwards logistic regression showed significant interaction between heart volume receiving 5 Gray and change in GLS. This cohort exhibited a significant decrease in 2C, 3C, 4C, and average GLS after RT. ∼1/3 of patients had abnormal GLS at baseline (suggesting a high-risk group for cardiac complications) and 67.5% of patients had clinically significant decrease in GLS after RT. Among the patients with normal GLS before RT, although 51.9% of patients demonstrated a clinically significant drop in GLS after RT, only 3.7% of patients developed abnormal LVEF, suggesting that this is a late occurrence. GLS changes may serve as a valuable tool for early identification of patients who are at high risk for future cardiac complications after receiving thoracic radiation.

Blood-brain barrier and brain structural changes in lung cancer patients with non-brain metastases.

To explore the relationship between blood-brain barrier (BBB) leakage and brain structure in non-brain metastasis lung cancer (LC) by magnetic resonance imaging (MRI) as well as to indicate the possibility of brain metastasis (BM) occurrence. MRI were performed in 75 LC patients and 29 counterpart healthy peoples (HCs). We used the Patlak pharmacokinetic model to calculate the average leakage in each brain region according to the automated anatomical labeling (AAL) atlas. The thickness of the cortex and the volumes of subcortical structures were calculated using the FreeSurfer base on Destrieux atlas. We compared the thickness of the cerebral cortex, the volumes of subcortical structures, and the leakage rates of BBB, and evaluated the relationships between these parameters. Compared with HCs, the leakage rates of seven brain regions were higher in patients with advanced LC (aLC). In contrast to patients with early LC (eLC), the cortical thickness of two regions was decreased in aLCs. The volumes of twelve regions were also reduced in aLCs. Brain regions with increased BBB penetration showed negative correlations with thinner cortices and reduced subcortical structure volumes (P<0.05, R=-0.2 to -0.50). BBB penetration was positively correlated with tumor size and with levels of the tumor marker CYFRA21-1 (P<0.05, R=0.2-0.70). We found an increase in BBB permeability in non-BM aLCs that corresponded to a thinner cortical thickness and smaller subcortical structure volumes. With progression in LC staging, BBB shows higher permeability and may be more likely to develop into BM.

CT patterns and serial CT Changes in lung Cancer patients post stereotactic body radiotherapy (SBRT)

BackgroundTo evaluate computed tomography (CT) patterns of post-SBRT lung injury in lung cancer and identify time points of serial CT changes.Materials and methodsOne hundred eighty-three tumors in 170 patients were evaluated on sequential CTs within 29 months (median). Frequencies of post-SBRT CT patterns and time points of initiation and duration were assessed. Duration of increase of primary lesion or surrounding injury without evidence of local recurrence and time to stabilization or local recurrence were evaluated.ResultsPost-SBRT CT patterns could overlap in the same patient and were nodule-like pattern (69%), consolidation with ground glass opacity (GGO) (41%), modified conventional pattern (39%), peribronchial/patchy consolidation (42%), patchy GGO (24%), diffuse consolidation (16%), “orbit sign” (21%), mass-like pattern (19%), scar-like pattern (15%) and diffuse GGO (3%). Patchy GGO started at 4 months post-SBRT. Peribronchial/patchy consolidation and consolidation with GGO started at 4 and 5 months respectively. Diffuse consolidation, diffuse GGO and orbit sign started at 5, 6 and 8 months respectively. Mass-like, modified conventional and scar-like pattern started at 8, 12 and 12 months respectively. Primary lesion (n = 11) or surrounding injury (n = 85) increased up to 13 months. Primary lesion (n = 119) or surrounding injury (n = 115) started to decrease at 4 and 9 months respectively. Time to stabilization was 20 months. The most common CT pattern at stabilization was modified conventional pattern (49%), scar-like pattern (23%) and mass-like pattern (12%). Local recurrence (n = 15) occurred at a median time of 18 months.ConclusionDifferent CT patterns of lung injury post-SBRT appear in predictable time points and have variable but predictable duration. Familiarity with these patterns and timeframes of appearance helps differentiate them from local recurrence.

Dual-energy CT based monitoring of treatment-induced bone marrow changes in lung cancer patients: preliminary results.

Systemic lung cancer treatment-induced changes in bone marrow attenuation assessed via dual-energy CT-based virtual non-calcium (VNCa) imaging of the axial skeleton and their relationship to hematological laboratory have not yet been investigated. VNCa bone marrow images of the axial skeleton derived from 93 unenhanced reduced dose dual-energy CTs of the thorax and abdomen of 31 patients were retrospectively analyzed. Each patient had received one pre-therapy baseline exam and two consecutive follow-up exams (FU1 and FU2) at a mean of 7.7 and 11.7 weeks after start of therapy. Concurrent hematologic laboratory data were available for every exam. Seven regions of interest were placed in the spine and pelvis and mean VNCa bone marrow attenuation was measured. Twenty-two Patients receiving highly myelotoxic treatment (Group A) were compared to 9 patients receiving less toxic substances (Group B). Median bone marrow attenuation in Group A/Group B was -31.8 HU (IQR 12.7)/40.6 HU (IQR, 12.2) at baseline, -46.5 HU (IQR, 12.5)/-43.8 HU (15.7) at FU1 and -46.9 HU (IQR, 22.0)/-38.5 HU (IQR, 18.5) at FU2. In both subgroups the reduction of the mean attenuation between baseline and FU1 was statistically significant although in Group A it was more pronounced; no significant difference was found between FU1 and FU2. The differences between Groups were not statistically significant. Leukopenia rates at FU1 and FU 2 were 50% and 54.5% in Group A and 0% and 22% in Group B. Anemia rates rose from 31.8% at baseline to 90% at FU1 and 86.4% at FU2 in Group A and fell from 77.8% at baseline to 66.7% at FU1 and further to 55.6% at FU2 in Group B. Both highly myelotoxic as well as-to a smaller degree-less myelotoxic systemic therapy led to a significant drop in bone marrow attenuation with no significant tendency towards subsequent elevation irrespective of the treatment's degree of toxicity or the presence of myelosuppression and not even under hematological supportive therapy. The results suggest that in this clinical setting an increase in bone marrow attenuation should be regarded as suspicious for tumor infiltration.

Blood–Brain Barrier and Brain Structural Changes in Lung Cancer Patients with Non-Brain Metastases

Blood–Brain Barrier and Brain Structural Changes in Lung Cancer Patients with Non-Brain Metastases

Identification of modes of tumor regression in non-small cell lung cancer patients during radiotherapy.

Observed gross tumor volume (GTV) shrinkage during radiotherapy (RT) raises the question of whether to adapt treatment to changes observed on the acquired images. In the literature, two modes of tumor regression have been described: elastic and non-elastic. These modes of tumor regression will affect the safety of treatment adaptation. This study applies a novel approach, using routine cone-beam computed tomography (CBCT) and deformable image registration to automatically distinguish between elastic and non-elastic tumor regression. In this retrospective study, 150 locally advanced non-small cell lung cancer patients treated with 55 Gray of radiotherapy were included. First, the two modes of tumor regression were simulated. For each mode of tumor regression, one timepoint was simulated. Based on the results of simulated data, the approach used for analysis in real patients was developed. CBCTs were non-rigidly registered to the baseline CBCT using a cubic B-spline algorithm, NiftyReg. Next, the Jacobian determinants were computed from the deformation vector fields. To capture local volume changes, 10 Jacobian values were sampled perpendicular to the surface of the GTV, across the lung-tumor boundary. From the simulated data, we can distinguish elastic from non-elastic tumor regression by comparing the Jacobian values samples between 5 and 12.5mm inside and 5 and 12.5mm outside the planning GTV. Finally, morphometric results were compared between tumors of different histologies. Most patients (92.3%) in our cohort showed stable disease in the first week of treatment and non-elastic shrinkage in the later weeks of treatment. At week 2, 125 patients (88%) showed stable disease, three patients (2.1%) disease progression, and 11 patients (8%) regression. By treatment completion, 91 patients (64%) had stable disease, one patient (0.7%) progression and 46 patients (32%) regression. A slight difference in the mode of tumor change was observed between tumors of different histologies. Our novel approach shows that it may be possible to automatically quantify and identify global changes in lung cancer patients during RT, using routine CBCT images. Our results show that different regions of the tumor change in different ways. Therefore, careful consideration should be taken when adapting RT.

Dynamic functional network connectivity reveals the brain functional alterations in lung cancer patients after chemotherapy.

This study aimed to investigate alterations of brain functional network connectivity (FNC) in lung cancer patients after chemotherapy and explore links between these FNC differences and cognitive impairment.Twenty-two lung cancer patients receiving chemotherapy and 26 healthy controls (HCs) underwent resting-state functional MRI (rs-fMRI) and neuropsychological testing. Group independent component analysis (GICA) was applied to rs-fMRI data to extract whole-brain resting state networks (RSNs). Static and dynamic FNC (dFNC) were constructed to reveal RSNs connectivity alterations between lung cancer patients and HCs group, and the correlations between the group differences in RSNs and cognitive performance were analyzed.Our findings revealed that chemotherapeutics can produce widespread connectivity abnormalities in RSNs, mainly focused on default mode network (DMN) and executive control network. Furthermore, the dFNC analysis help identify network configurations of each state and capture more chemotherapy-induced disorders of interactions between and within RSNs, which mainly includes sensorimotor network, attentional network and auditory network. In addition, after chemotherapy, the lung cancer patients spend shorter mean dwell time (MDT) in state 2. The decreased dFNC between DMN [independent component 5 (IC5)] and DMN (IC6) in the lung cancer patients after chemotherapy in state 4 was negatively correlated with Montreal Cognitive Assessment (MoCA) scores (r=-0.447, p=0.042).The dFNC analysis enrich our understanding of the neural mechanisms underlying the chemobrain, and suggested that the temporal dynamics of FNC could be a potential effective method to detect cognitive changes in lung cancer patients receiving chemotherapy.

Changes on myocardial perfusion scintigraphy and contrast-enhanced cardiac magnetic resonance imaging after definitive radiotherapy in patients with lung cancer

AbstractAim:To determine whether myocardial perfusion scintigraphy (MPS) changes in lung cancer patients treated with radiotherapy (RT) were detectable with late gadolinium enhancement cardiac magnetic resonance imaging (LGE CMR).Materials and methods:Twenty-one patients with lung cancer were evaluated pre-RT and at 2 and 6 months post-RT follow-up (FU) with MPS and LGE CMR. MPS changes in the left ventricle (LV) were analysed using the semi-quantitative summed rest score method (20 segments) and the Bull’s-eye-view technique. The LGE CMR studies were analysed for visual signs of myocardial damage (fibrosis), that is, focal LGE in the LV and cardiac function parameters.Results:MPS changes were detected in 7/20 patients at 2 months FU and in 8/13 patients at 6 months FU. Only one patient had a new irreversible defect judged to be caused by direct irradiation. MPS changes in two cases were deemed to be caused by attenuation. All new MPS defects were minor and no corresponding myocardial damage, or any functional changes, were evident on LGE CMR.Findings:The extent of MPS changes at 6 months FU appeared less prominent than in previous reports. No visual signs or functional changes corresponding to myocardial damage were detected on LGE CMR. A risk for false-positive MPS changes caused by attenuation is evident.

Pulmonary Micro-Ecological Changes and Potential Microbial Markers in Lung Cancer Patients.

The relationship between the microbiome and disease has been investigated for many years. As a highly malignant tumor, biomarkers for lung cancer are diverse. However, precision of these biomarkers has not yet been achieved. It has been confirmed that lung microecology changes in lung cancer patients compared with healthy individuals. Furthermore, the abundance of some bacterial species shows obvious changes, suggesting their potential use as a microbial marker for the detection of lung cancer. In addition, recent studies have confirmed that inflammation, immune response, virulence factors, and metabolism may be potential mechanisms linking the microbiome with carcinogenesis. In this review, microbiome studies of lung cancer, potential mechanisms, potential microbial markers, and the influence of the microbiome on the diagnosis and treatment of lung cancer are summarized, providing theoretical strategies for the diagnosis and treatment of lung cancer.

Analysis of Pulmonary Microbial Diversity in Patients with Advanced Lung Cancer Based on High-throughput Sequencing Technology

背景与目的肺部微生物组与肺部疾病的发生密切相关，微生物可从多方面促进肿瘤的发生与发展。既往研究证实肺癌患者肺部微生物组较健康人发生了变化，但关于不同类型肺癌患者中肺部微生物组的微生物组成尚未明确。本研究旨在探讨不同组织学类型肺癌间的微生物组相关性及差异性。方法采用Illumina HiSeq高通量测序技术对晚期肺癌患者痰样本进行细菌V3-V4区的16S rDNA测序及生物信息学分析。结果经物种分类分析发现肺癌患者痰样本中以链球菌属、奈瑟氏菌属、普雷沃氏菌属为主。腺癌（adenocarcinoma, AD）组以链球菌属及奈瑟氏菌属为主，小细胞肺癌（small cell lung cancer, SCLC）组以奈瑟氏菌属为主，鳞状细胞癌（squamous cell carcinoma, SCC）组以链球菌属为主，混合型小细胞肺癌（combined small cell lung cancer, C-SCLC）组以链球菌属为主。结论不同组织学类型肺癌的肺部细菌微生物组结构及组成存在差异性，不同组织学类型肺癌间优势菌群有所不同，本研究丰富了肺癌肺部细菌微生物组学数据。

External validation of a hidden Markov model for gamma-based classification of anatomical changes in lung cancer patients using EPID dosimetry.

To externally validate a hidden Markov model (HMM) for classifying gamma analysis results of in vivo electronic portal imaging device (EPID) measurements into different categories of anatomical change for lung cancer patients. Additionally, the relationship between HMM classification and deviations in dose-volume histogram (DVH) metrics was evaluated. The HMM was developed at CHU de Québec (CHUQ), and trained on features extracted from gamma analysis maps of in vivo EPID measurements from 483 fractions (24 patients, treated with three-dimensional 3D-CRT or intensity modulated radiotherapy), using the EPID measurement of the first treatment fraction as reference. The model inputs were the average gamma value, standard deviation, and average value of the highest 1% of gamma values, all averaged over all beams in a fraction. The HMM classified each fraction into one of three categories: no anatomical change (Category 1), some anatomical change (no clinical action needed, Category 2) and severe anatomical change (clinical action needed, Category 3). The external validation dataset consisted of EPID measurements from 263 fractions of 30 patients treated at Maastro with volumetric modulated arc therapy (VMAT) or hybrid plans (containing both static beams and VMAT arcs). Gamma analysis features were extracted in the same way as in the CHUQ dataset, by using the EPID measurement of the first fraction as reference (γQ), and additionally by using an EPID dose prediction as reference (γM). For Maastro patients, cone beam computed tomography (CBCT) scans and image-guided radiotherapy (IGRT) classification of these images were available for each fraction. Contours were propagated from the planning CT to the CBCTs, and the dose was recalculated using a Monte Carlo dose engine. Dose-volume histogram metrics for targets and organs-at-risk (OARs: lungs, heart, mediastinum, spinal cord, brachial plexus) were extracted for each fraction, and compared to the planned dose. HMM classification of the external validation set was compared to threshold classification based on the average gamma value alone (a surrogate for clinical classification at CHUQ), IGRT classification as performed at Maastro, and differences in DVH metrics extracted from 3D dose recalculations on the CBCTs. The HMM achieved 65.4%/65.0% accuracy for γQ and γM, respectively, compared to average gamma threshold classification. When comparing HMM classification with IGRT classification, the overall accuracy was 29.7% for γQ and 23.2% for γM. Hence, HMM classification and IGRT classification of anatomical changes did not correspond. However, there is a trend towards higher deviations in DVH metrics with classification into higher categories by the HMM for large OARs (lungs, heart, mediastinum), but not for the targets and small OARs (spinal cord, brachial plexus). The external validation shows that transferring the HMM for anatomical change classification to a different center is challenging, but can still be valuable. The HMM trained at CHUQ cannot be used directly to classify anatomical changes in the Maastro data. However, it may be possible to use the model in a different capacity, as an indicator for changes in the 3D dose based on two-dimensional EPID measurements.

The results of quantitative evaluation of postradiation changes in lung cancer patients, which were obtained using a new procedure for analysis of dynamic X-ray computed tomography imaging of thoracic organs

Objective: to reveal a relationship of lung changes to time after radiotherapy (RT), its dose, and irradiated volume, by using a new procedure to quantitatively evaluate lung tissue density changes according to the data of dynamic X-ray computed tomography (X-ray CT) imaging.Subjects and methods. Data on 29 cancer patients who had received RT to the chest area using 3D planning, 2-5-Gy fractions, and a total focal RT dose of 16 to 84 Gy in the lung tissue were retrospectively selected. Each patient underwent at least two X-ray CT studies (before and after RT); a total of 83 studies were analyzed. The new diagnostic procedure developed by the authors was used to quantitatively analyze lung tissue density changes in the areas with a selected dose range.Results. On days 1 to 15-30 after completion of RT, the quantitative analysis revealed no significant density changes. On days 70-80, there was an increase in changes that were different from those in the nonirradiated areas. On 80 to 100-120 days, there was a decline of radiation reactions with preserved changes in the areas irradiated at a dose of 19-20 Gy. From 120 days onwards, the changes were preserved in the areas irradiated at a dose of over 32-37 Gy through the formation of post-radiation pulmonary fibrosis. In addition, eight (27.6%) patients treated with less than 20 Gy were detected to have changes associated with the initial density values, which can be regarded as an increased individual radiosensitivity.Conclusion. The time course of a quantitative change in the median lung tissue density in the areas irradiated at different doses allows radiation-induced lung injury to be detected at X-ray CT until the visual signs of pneumonitis appear. A continuous, quantitative scale of changes will assist in more reliably and accurately studying post-radiation changes at a later time, and the time course of spaciotemporal changes will be able to compare and quantify lung injury when exploring new treatment methods and policies.

Detection of anatomical changes in lung cancer patients with 2D time-integrated, 2D time-resolved and 3D time-integrated portal dosimetry: a simulation study

The aim of this work is to assess the performance of 2D time-integrated (2D-TI), 2D time-resolved (2D-TR) and 3D time-integrated (3D-TI) portal dosimetry in detecting dose discrepancies between the planned and (simulated) delivered dose caused by simulated changes in the anatomy of lung cancer patients.For six lung cancer patients, tumor shift, tumor regression and pleural effusion are simulated by modifying their CT images. Based on the modified CT images, time-integrated (TI) and time-resolved (TR) portal dose images (PDIs) are simulated and 3D-TI doses are calculated. The modified and original PDIs and 3D doses are compared by a gamma analysis with various gamma criteria. Furthermore, the difference in the D95% (ΔD95%) of the GTV is calculated and used as a gold standard. The correlation between the gamma fail rate and the ΔD95% is investigated, as well the sensitivity and specificity of all combinations of portal dosimetry method, gamma criteria and gamma fail rate threshold.On the individual patient level, there is a correlation between the gamma fail rate and the ΔD95%, which cannot be found at the group level. The sensitivity and specificity analysis showed that there is not one combination of portal dosimetry method, gamma criteria and gamma fail rate threshold that can detect all simulated anatomical changes.This work shows that it will be more beneficial to relate portal dosimetry and DVH analysis on the patient level, rather than trying to quantify a relationship for a group of patients. With regards to optimizing sensitivity and specificity, different combinations of portal dosimetry method, gamma criteria and gamma fail rate should be used to optimally detect certain types of anatomical changes.

Evaluation of postoperative lung volume and perfusion changes by dual-energy computed tomography in patients with lung cancer

PurposeThe aim of our study was to retrospectively evaluate postoperative physiologic changes in lung cancer patients using dual-energy CT (DECT), and develop modified methods reflecting postoperative change for predicting pulmonary function. Methods and materials88 patients (M:F=64:24; mean age, 63.5 years) with lung cancer who underwent DECT and pulmonary function tests before and after operation were included. Volume and iodine values for perfusion of each lobe were quantified. The predicted postoperative FEV1 using the current method was calculated by multiplying the preoperative FEV1 by the fractional contribution of perfusion of the remaining lung. The modified method reflecting postoperative volume change was compared to the current method. ResultsPostoperative lung volume showed compensatory increases in the contralateral and remaining ipsilateral lobes, with a significantly greater increase in the ipsilateral lobe than contralateral lobe (21.8%±46.2% vs. 10.0%±20.8%, P=0.031). Perfusion analysis showed blood volume increases in both ipsilateral and contralateral lobes without statistical differences (blood volume ratio difference, 29.2%±26.7 vs. 24.6%±16.5, P=0.368). The performance of the modified method considering postoperative lung volume change was comparable to that of the current method in the development and validation datasets (95% CI, −24.5% to 37.1% vs. −33.3% to 22.2% and −23.6% to 32.0% vs. −31.9% to 16.0%, respectively). ConclusionsPostoperative compensatory increases in lung volume and perfusion occur in different ways. Our modified method incorporating postoperative lung volume changes can be considered a comparable method for prediction of postoperative lung function.

Rates of EGFR, KRAS and ALK gene changes in lung cancer patients in Croatia.

e23238Background: Rates in targetable gene changes varies between different populations of lung cancer patients. Targetable gene changes include changes in EGFR and ALK gene, as well as KRAS gene. ...

Is integrated transit planar portal dosimetry able to detect geometric changes in lung cancer patients treated with volumetric modulated arc therapy?

ABSTRACTBackground. Geometric changes are frequent during the course of treatment of lung cancer patients. This may potentially result in deviations between the planned and actual delivered dose. Electronic portal imaging device (EPID)-based integrated transit planar portal dosimetry (ITPD) is a fast method for absolute in-treatment dose verification. The aim of this study was to investigate if ITPD could detect geometric changes in lung cancer patients.Materials and methods. A total of 460 patients treated with volumetric modulated arc therapy (VMAT) following daily cone beam computed tomography (CT)-based setup were visually inspected for geometrical changes on a daily basis. Forty-six patients were subject to changes and had a re-CT and an adaptive treatment plan. The reasons for adaptation were: change in atelectasis (n = 18), tumor regression (n = 9), change in pleural effusion (n = 8) or other causes (n = 11). The ITPDs were calculated on both the initial planning CT and the re-CT and compared with a global gamma (γ) evaluation (criteria: 3%\\3mm). A treatment fraction failed when the percentage of pixels failing in the radiation fields exceeded 10%. Dose-volume histograms (DVHs) were compared between the initial plan versus the plan re-calculated on the re-CT.Results. The ITPD threshold method detected 76% of the changes in atelectasis, while only 50% of the tumor regression cases and 42% of the pleural effusion cases were detected. Only 10% of the cases adapted for other reasons were detected with ITPD. The method has a 17% false-positive rate. No significant correlations were found between changes in DVH metrics and γ fail-rates.Conclusions. This study showed that most cases with geometric changes caused by atelectasis could be captured by ITPD, however for other causes ITPD is not sensitive enough to detect the clinically relevant changes and no predictive power of ITPD was found.

Robust optimization in intensity-modulated proton therapy to account for anatomy changes in lung cancer patients

Background and purposeRobust optimization for IMPT takes setup and range uncertainties into account during plan optimization. However, anatomical changes were not prospectively included. The purpose of this study was to examine robustness and dose variation due to setup uncertainty and anatomical change in IMPT of lung cancer. Material and methodsPlans were generated with multi-field optimization based on planning target volume (MFO-PTV) and worst-case robust optimization (MFO-RO) on simulation computed tomography scans (CT0) for nine patients. Robustness was evaluated on the CT0 by computing the standard deviation of DVH (SD-DVH). Dose variations calculated on weekly CTs were compared with SD-DVH. Equivalent uniform dose (EUD) change from the original plan on weekly dose was also calculated for both plans. ResultsSD-DVH and dose variation on weekly CTs were both significantly lower in the MFO-RO plans than in the MFO-PTV plans for targets, lungs, and the esophagus (p<0.05). When comparing EUD for ITV between weekly and planned dose distributions, three patients and 28% of repeated CTs for MFO-RO plans, and six patients and 44% of repeated CTs for MFO-PTV plans, respectively, showed an EUD change of >5%. ConclusionsRO in IMPT reduces the dose variation due to setup uncertainty and anatomy changes during treatment compared with PTV-based planning. However, dose variation could still be substantial; repeated imaging and adaptive planning as needed are highly recommended for IMPT of lung tumors.

Intra thoracic anatomical changes in lung cancer patients during the course of radiotherapy

Background and purposeConebeam-CT (CBCT) guidance is often used for setup verification of lung cancer patients treated with radiotherapy. The purpose of this study was to quantify intra-thoracic anatomical changes (ITACs) during the radiotherapy treatment and to hand over a decision support system to guide the radiation therapy technologist and radiation oncologist in prioritizing these changes. Materials and methods1793 CBCT-scans of 177 lung cancer patients treated in 2010 in our institute with radical radiotherapy were evaluated. Our decision support system: “the Traffic Light Protocol”, was retrospectively applied to these CBCT-scans. The protocol has four levels: red (immediate action before treatment), orange (action before next fraction), yellow (no action required) and green (no change). ResultsIn 128 patients (72%), 210 ITACs were observed with a maximum level of red, orange and yellow in 12%, 36% and 24% respectively. Types of observed ITACs were, tumor regression (35%), tumor baseline shift (27%), changes in atelectasis (19%), tumor progression (10%), pleural effusion (6%) and infiltrative changes (3%). ConclusionsITACs have been observed in 72% of all lung cancer patients during the course of radical radiotherapy. The clinical relevance of the proposed ITAC classification in lung radiotherapy needs to be validated in a prospective analysis.