Cost-of-care discussions for individuals with advanced non-small cell lung cancer and melanoma: Findings from a large, population-based pilot study.

BACKGROUND: Costs of cancer care can result in patient financial hardship; many professional organizations recommend provider discussions about treatment costs as part of high-quality care. In this pilot study, the authors examined patient-provider cost discussions documented in the medical records of individuals who were diagnosed with advanced non-small cell lung cancer (NSCLC) and melanoma-cancers with recently approved, high-cost treatment options. METHODS: Individuals who were newly diagnosed in 2017-2018 with stage III/IV NSCLC (n = 1767) and in 2018 with stage III/IV melanoma (n = 689) from 12 Surveillance, Epidemiology, and End Results regions were randomly selected for the National Cancer Institute Patterns of Care Study. Documentation of cost discussions was abstracted from the medical record. The authors examined patient, treatment, and hospital factors associated with cost discussions in multivariable logistic regression analyses. RESULTS: Cost discussions were documented in the medical records of 20.3% of patients with NSCLC and in 24.0% of those with melanoma. In adjusted analyses, privately insured (vs. publicly insured) patients were less likely to have documented cost discussions (odds ratio [OR], 0.54; 95% confidence interval [CI], 0.37-0.80). Patients who did not receive systemic therapy or did not receive any cancer-directed treatment were less likely to have documented cost discussions than those who did receive systemic therapy (OR, 0.39 [95% CI, 0.19-0.81] and 0.46 [95% CI, 0.30-0.70], respectively), as were patients who were treated at hospitals without residency programs (OR, 0.64; 95% CI, 0.42-0.98). CONCLUSIONS: Cost discussions were infrequently documented in the medical records of patients who were diagnosed with advanced NSCLC and melanoma, which may hinder identifying patient needs and tracking outcomes of associated referrals. Efforts to increase cost-of-care discussions and relevant referrals, as well as their documentation, are warranted.

A cuproptosis score model and prognostic score model can evaluate clinical characteristics and immune microenvironment in NSCLC.

BACKGROUND: Cuproptosis-related genes (CRGs) are associated with lung adenocarcinoma. However, the links between CRGs and non-small-cell lung cancer (NSCLC) are not clear. In this study, we aimed to develop two cuproptosis models and investigate their correlation with NSCLC in terms of clinical features and tumor microenvironment. METHODS: CRG expression profiles and clinical data from NSCLC and normal tissues was obtained from GEO (GSE42127) and TCGA datasets. Molecular clusters were classified into three patterns based on CRGs and cuproptosis cluster-related specific differentially expressed genes (CRDEGs). Then, two clinical models were established. First, a prognostic score model based on CRDEGs was established using univariate/multivariate Cox analysis. Then, through principal component analysis, a cuproptosis score model was established based on prognosis-related genes acquired via univariate analysis of CRDEGs. NSCLC patients were divided into high/low risk groups. RESULTS: Eighteen CRGs were acquired, all upregulated in tumor tissues, 15 of which significantly (P < 0.05). Among the three CRG clusters, cluster B had the best prognosis. In the CRDEG clusters, cluster C had the best survival. In the prognostic score model, the high-risk group had worse prognosis, higher tumor mutation load, and lower immune infiltration while in the cuproptosis score model, a high score represented better survival, lower tumor mutation load, and high-level immune infiltration. CONCLUSIONS: The cuproptosis score model and prognostic score model may be associated with NSCLC prognosis and immune microenvironment. These novel findings on the progression and immune landscape of NSCLC may facilitate the provision of more personalized immunotherapy interventions for NSCLC patients.

Delta-radiomics features for predicting the major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer.

OBJECTIVES: To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. METHODS: Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. RESULTS: Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. CONCLUSION: The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. CLINICAL RELEVANCE STATEMENT: Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. KEY POINTS: • The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy.

Predicting programmed death-ligand 1 (PD-L1) expression with fluorine-18 fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT) metabolic parameters in resectable non-small cell lung cancer.

BACKGROUND: Programmed death-ligand 1 (PD-L1) expression is a predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). PD-L1 and glucose transporter 1 expression are closely associated, and studies demonstrate correlation of PD-L1 with glucose metabolism. AIM: The aim of this study was to investigate the association of fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters with PD-L1 expression in primary lung tumour and lymph node metastases in resected NSCLC. METHODS: We conducted a retrospective analysis of 210 patients with node-positive resectable stage IIB-IIIB NSCLC. PD-L1 tumour proportion score (TPS) was determined using the DAKO 22C3 immunohistochemical assay. Semi-automated techniques were used to analyse pre-operative [18F]FDG-PET/CT images to determine primary and nodal metabolic parameter scores (including max, mean, peak and peak adjusted for lean body mass standardised uptake values (SUV), metabolic tumour volume (MTV), total lesional glycolysis (TLG) and SUV heterogeneity index (HISUV)). RESULTS: Patients were predominantly male (57%), median age 70 years with non-squamous NSCLC (68%). A majority had negative primary tumour PD-L1 (TPS < 1%; 53%). Mean SUVmax, SUVmean, SUVpeak and SULpeak values were significantly higher (p < 0.05) in those with TPS ≥ 1% in primary tumour (n = 210) or lymph nodes (n = 91). However, ROC analysis demonstrated only moderate separability at the 1% PD-L1 TPS threshold (AUCs 0.58-0.73). There was no association of MTV, TLG and HISUV with PD-L1 TPS. CONCLUSION: This study demonstrated the association of SUV-based [18F]FDG-PET/CT metabolic parameters with PD-L1 expression in primary tumour or lymph node metastasis in resectable NSCLC, but with poor sensitivity and specificity for predicting PD-L1 positivity ≥ 1%. CLINICAL RELEVANCE STATEMENT: Whilst SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography metabolic parameters may not predict programmed death-ligand 1 positivity ≥ 1% in the primary tumour and lymph nodes of resectable non-small cell lung cancer independently, there is a clear association which warrants further investigation in prospective studies. TRIAL REGISTRATION: Non-applicable KEY POINTS: • Programmed death-ligand 1 immunohistochemistry has a predictive role in non-small cell lung cancer immunotherapy; however, it is both heterogenous and dynamic. • SUV-based fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT) metabolic parameters were significantly higher in primary tumour or lymph node metastases with positive programmed death-ligand 1 expression. • These SUV-based parameters could potentially play an additive role along with other multi-modal biomarkers in selecting patients within a predictive nomogram.

Brigatinib in Japanese patients with ALK-positive non-small-cell lung cancer: Final results of the phase 2 J-ALTA trial.

The phase 2, single-arm, multicenter, open-label J-ALTA study evaluated the efficacy and safety of brigatinib in Japanese patients with advanced ALK+ non-small-cell lung cancer (NSCLC). One expansion cohort of J-ALTA enrolled patients previously treated with ALK tyrosine kinase inhibitors (TKIs); the main cohort included patients with prior alectinib ± crizotinib. The second expansion cohort enrolled patients with TKI-naive ALK+ NSCLC. All patients received brigatinib 180 mg once daily (7-day lead-in at 90 mg daily). Among 47 patients in the main cohort, 5 (11%) remained on brigatinib at the study end (median follow-up: 23 months). In this cohort, the independent review committee (IRC)-assessed objective response rate (ORR) was 34% (95% CI, 21%-49%); median duration of response was 14.8 months (95% CI, 5.5-19.4); median IRC-assessed progression-free survival (PFS) was 7.3 months (95% CI, 3.7-12.9). Among 32 patients in the TKI-naive cohort, 25 (78%) remained on brigatinib (median follow-up: 22 months); 2-year IRC-assessed PFS was 73% (90% CI, 55%-85%); IRC-assessed ORR was 97% (95% CI, 84%-100%); the median duration of response was not reached (95% CI, 19.4-not reached); 2-year duration of response was 70%. Grade ≥3 adverse events occurred in 68% and 91% of TKI-pretreated and TKI-naive patients, respectively. Exploratory analyses of baseline circulating tumor DNA in ALK TKI-pretreated NSCLC showed associations between poor PFS and EML4-ALK fusion variant 3 and TP53. Brigatinib is an important treatment option for Japanese patients with ALK+ NSCLC, including patients previously treated with alectinib.

Efficacy evaluation of neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer: comparison of PET/CT with postoperative pathology.

OBJECTIVES: To assess the value of positron emission tomography/computed tomography (PET/CT) in the efficacy evaluation of patients undergoing neoadjuvant immunotherapy plus chemotherapy, and to analyze its correlation with postoperative pathology. METHODS: The PET/CT metabolic parameters and CT size were retrospectively analyzed before and after neoadjuvant immunotherapy plus chemotherapy in 67 patients with resectable stage II/IIIA non-small-cell lung cancer (NSCLC). CT assessment based on immune response evaluation criteria in solid tumor criteria ((i)RECIST) was compared with PET/CT assessment based on the response criteria in solid tumors (PERCIST). The correlations between PET/CT metabolic parameters and postoperative pathology were analyzed. The value of PET/CT in the efficacy evaluation was assessed. RESULTS: The PET/CT assessment showed high consistency with postoperative pathological evaluation, yet the CT assessment showed low consistency with postoperative pathological evaluation. The (i)RECIST and PERCIST criteria showed statistically significant differences (p < 0.001). The postoperative pathological response was negatively associated with ΔSUVmax (%) (r = - 0.812, p < 0.001), ΔSUVmean (%) (r = - 0.805, p < 0.001), and ΔSUVpeak (%) (r = - 0.800, p < 0.001). The cut-off values of 75.8 for ΔSUVmax (%), 67.8 for ΔSUVmean (%), and 74.6 for ΔSUVpeak (%) had the highest sensitivity and specificity. CONCLUSION: The PERCIST criteria are more sensitive and accurate than (i)RECIST criteria to identify more responders when evaluating the response of neoadjuvant immunotherapy plus chemotherapy for NSCLC. PET/CT shows high accuracy in predicting postoperative pathological response. Our study shows the important role PET/CT plays in the efficacy evaluation of NSCLC patients undergoing neoadjuvant immunotherapy plus chemotherapy, as well as in predicting the prognosis and guiding postoperative treatment. CLINICAL RELEVANCE STATEMENT: Neoadjuvant immunotherapy plus chemotherapy is highly effective in the treatment of non-small-cell lung cancer. And PET/CT played an important role in the efficacy evaluation following neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer. KEY POINTS: • Neoadjuvant immunotherapy plus chemotherapy is highly effective in the treatment of NSCLC. • The PERCIST criteria are more sensitive and accurate than (i)RECIST criteria to identify more responders when evaluating the response of neoadjuvant immunotherapy plus chemotherapy for NSCLC. • PET/CT played an important role in the efficacy evaluation; ΔSUVmax (%), ΔSUVmean (%), and ΔSUVpeak (%) following neoadjuvant immunotherapy plus chemotherapy for NSCLC had high consistency and strong correlations with postoperative pathology.

A prognostic model for predicting progression-free survival in patients with advanced non-small cell lung cancer after image-guided microwave ablation plus chemotherapy.

OBJECTIVES: This study aimed to build and validate a prediction model that can predict progression-free survival (PFS) in patients with advanced non-small cell lung cancer (NSCLC) after image-guided microwave ablation (MWA) plus chemotherapy. METHODS: Data from a previous multi-center randomized controlled trial (RCT) was used and assigned to either the training data set or the external validation data set according to the location of the centers. Potential prognostic factors were identified by multivariable analysis in the training data set and used to construct a nomogram. After bootstraps internal and external validation, the predictive performance was evaluated by concordance index (C-index), Brier Score, and calibration curves. Risk group stratification was conducted using the score calculated by the nomogram. Then a simplified scoring system was built to make risk group stratification more convenient. RESULTS: In total, 148 patients (training data set: n = 112; external validation data set: n = 36) were enrolled for analysis. Six potential predictors were identified and entered into the nomogram, including weight loss, histology, clinical TNM stage, clinical N category, tumor location, and tumor size. The C-indexes were 0.77 (95% CI, 0.65-0.88, internal validation) and 0.64 (95% CI, 0.43-0.85, external validation). The survival curves of different risk groups also displayed significant distinction (p < 0.0001). CONCLUSIONS: We found weight loss, histology, clinical TNM stage, clinical N category, tumor location, and tumor size were prognostic factors of progression after receiving MWA plus chemotherapy and constructed a prediction model that can predict PFS. CLINICAL RELEVANCE STATEMENT: The nomogram and scoring system will assist physicians to predict the individualized PFS of their patients and decide whether to perform or terminate MWA and chemotherapy according to the expected benefits. KEY POINTS: • Build and validate a prognostic model using the data from a previous randomized controlled trial to predict progression-free survival after receiving MWA plus chemotherapy. • Weight loss, histology, clinical TNM stage, clinical N category, tumor location, and tumor size were prognostic factors. • The nomogram and scoring system published by the prediction model can be used to assist physicians to make clinical decisions.

Advanced non-small-cell lung cancer treated with first-line pembrolizumab plus chemotherapy: tumor response dynamics as a marker for survival.

OBJECTIVES: The study investigated tumor burden dynamics on computed tomography (CT) scans in patients with advanced non-small-cell lung cancer (NSCLC) during first-line pembrolizumab plus chemotherapy, to provide imaging markers for overall survival (OS). METHODS: The study included 133 patients treated with first-line pembrolizumab plus platinum-doublet chemotherapy. Serial CT scans during therapy were assessed for tumor burden dynamics during therapy, which were studied for the association with OS. RESULTS: There were 67 responders, with overall response rate of 50%. The tumor burden change at the best overall response ranged from - 100.0% to + 132.1% (median of - 30%). Higher response rates were associated with younger age (p < 0.001) and higher programmed cell death-1 (PD-L1) expression levels (p = 0.01). Eighty-three patients (62%) showed tumor burden below the baseline burden throughout therapy. Using an 8-week landmark analysis, OS was longer in patients with tumor burden below the baseline burden in the first 8 weeks than in those who experienced ≥ 0% increase (median OS: 26.8 vs. 7.6 months, hazard ratio (HR): 0.36, p < 0.001). Tumor burden remained below their baseline throughout therapy was associated with significantly reduced hazards of death (HR: 0.72, p = 0.03) in the extended Cox models, after adjusting for other clinical variables. Pseudoprogression was noted in only one patient (0.8%). CONCLUSIONS: Tumor burden staying below the baseline burden throughout the therapy was predictive of prolonged overall survival in patients with advanced NSCLC treated with first-line pembrolizumab plus chemotherapy, and may be used as a practical marker for therapeutic decisions in this widely used combination regimen. CLINICAL RELEVANCE STATEMENT: The analysis of tumor burden dynamics on serial CT scans in reference to the baseline burden can provide an additional objective guide for treatment decision making in patients treated with first-line pembrolizumab plus chemotherapy for their advanced NSCLC. KEY POINTS: • Tumor burden remaining below baseline burden during therapy predicted longer survival during first-line pembrolizumab plus chemotherapy. • Pseudoprogression was noted in 0.8%, demonstrating the rarity of the phenomenon. • Tumor burden dynamics may serve as an objective marker for treatment benefit to guide treatment decisions during first-line pembrolizumab plus chemotherapy.

Prognostic value of TLR from FDG PET/CT in patients with margin-negative stage IB and IIA non-small cell lung cancer.

OBJECTIVES: To evaluate the prognostic value of TLR from PET/CT in patients with resection margin-negative stage IB and IIA non-small cell lung cancer (NSCLC) and compare high-risk factors necessitating adjuvant treatment (AT). METHODS: Consecutive FDG PET/CT scans performed for the initial staging of NSCLC stage IB and IIA were retrospectively reviewed. The maximum standardized uptake value (SUVmax) of the primary tumor and mean SUV of the liver were acquired. The tumor-to-liver SUV ratio (TLR) was also calculated. Charts were reviewed for basic patient characteristics and high-risk factors for considering AT (poor differentiation, visceral pleura invasion, vascular invasion, tumors > 4 cm, and wedge resection). Statistical analysis was performed using Cox regression analysis and the Kaplan-Meier method. RESULTS: Of the 112 patients included, 15 (13.4%) died, with a median overall survival (OS) of 43.8 months. Twenty-two patients (19.6%) exhibited recurrence, with median disease-free survival (DFS) of 36.0 months. In univariable analysis, pathology, poor differentiation, and TLR were associated with shorter DFS and OS. In multivariable analysis, TLR (hazard ratio [HR] = 1.263, p = 0.008) and differentiation (HR = 3.087, p = 0.012) were associated with shorter DFS. Also, TLR (HR = 1.422, p < 0.001) was associated with shorter OS. CONCLUSION: TLR from FDG PET/CT was an independent prognostic factor for recurrence and survival. PET parameters constitute risk factors for consideration in the decision-making for AT in margin-negative stage IB and IIA NSCLC. CLINICAL RELEVANCE STATEMENT: In this study, TLR from FDG PET/CT was an independent prognostic factor in stage IB-IIA non-small cell cancer patients. Although additional validation studies are warranted, TLR has the potential to be used to determine the need for adjuvant therapy. KEY POINTS: • High TLR is an independent poor prognostic factor in stage IB-IIA NSCLC. • Adjuvant treatment should be considered in patients with high TLR following complete tumor resection.

Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review.

OBJECTIVES: To provide an overarching evaluation of the value of peritumoral CT radiomics features for predicting the prognosis of non-small cell lung cancer and to assess the quality of the available studies. METHODS: The PubMed, Embase, Web of Science, and Cochrane Library databases were searched for studies predicting the prognosis in patients with non-small cell lung cancer (NSCLC) using CT-based peritumoral radiomics features. Information about the patient, CT-scanner, and radiomics analyses were all extracted for the included studies. Study quality was assessed using the Radiomics Quality Score (RQS) and the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Thirteen studies were included with 2942 patients from 2017 to 2022. Only one study was prospective, and the others were all retrospectively designed. Manual segmentation and multicenter studies were performed by 69% and 46% of the included studies, respectively. 3D-Slicer and MATLAB software were most commonly used for the segmentation of lesions and extraction of features. The peritumoral region was most frequently defined as dilated from the tumor boundary of 15 mm, 20 mm, or 30 mm. The median RQS of the studies was 13 (range 4-19), while all of included studies were assessed as having a high risk of bias (ROB) overall. CONCLUSIONS: Peritumoral radiomics features based on CT images showed promise in predicting the prognosis of NSCLC, although well-designed studies and further biological validation are still needed. KEY POINTS: • Peritumoral radiomics features based on CT images are promising and encouraging for predicting the prognosis of non-small cell lung cancer. • The peritumoral region was often dilated from the tumor boundary of 15 mm or 20 mm because these were considered safe margins. • The median Radiomics Quality Score of the included studies was 13 (range 4-19), and all of studies were considered to have a high risk of bias overall.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. METHODS: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. RESULTS: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). CONCLUSION: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. CLINICAL RELEVANCE STATEMENT: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. KEY POINTS: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

Best practices in epidermal growth factor receptor T790M testing for advanced non-small-cell lung cancer in Hong Kong.

The T790M mutation in the epidermal growth factor receptor gene causes most acquired resistance to firstor second-line epidermal growth factor receptor-tyrosine kinase inhibitors in advanced non-small-cell lung cancer. The results of T790M testing can guide subsequent treatment. Despite the availability of guidelines from international organisations, T790M testing practices in Hong Kong must be streamlined and adapted to the Hospital Authority setting. To address this issue, a panel of experts in oncology and pathology met for discussion of key topics regarding T790M testing practices in Hong Kong, including the appropriate timing of testing and re-testing, as well as optimal testing methods. All panel members voted on the results of the discussion to achieve consensus. Items supported by a majority vote were adopted as consensus statements regarding current best practices for T790M testing in Hong Kong. Among the topics discussed, the panel agreed that T790M testing should be initiated upon radiological progression, including symptomatic disease progression or central nervous system-only progression. The experts also preferred initial testing with liquid biopsy, using the widely available digital polymerase chain reaction platform. This document provides the final consensus statements, as well as a testing and treatment workflow, for clinicians in Hong Kong to use as guidance in T790M testing.

[Current status and outlook of medical treatment for KRAS-mutated non-small cell lung cancer].

Lung cancer remains the leading cause of cancer-related deaths in men and women worldwide, and 85% of these patients have non-small cell lung cancer. In recent years, the clinical use of targeted drug therapy and immune checkpoint inhibitors has dramatically changed the treatment landscape for advanced NSCLC. The mechanism and the value of targeted therapies have been a hot topic of research, as KRAS is one of the earliest discovered and most frequently mutated oncogenes, which is activated by binding to GTP and triggers a series of cascade reactions in cell proliferation and mitosis. The KRAS protein acts as a molecular switch and is activated by binding to GTP, triggering a series of cascade responses in cell proliferation and mitosis. Clinically, patients with KRAS mutated NSCLC have poor response to systemic medical therapy and poor prognosis. Since the first report of KRAS gene in 1982, research on KRAS targeted therapeutics has been slow, and previous studies such as farnesyltransferase inhibitors and downstream protein inhibitors of KRAS signaling pathway have not achieved the expected results, making KRAS long defined as a "non-druggable target". The deeper understanding of the crystal structure of KRAS has led to the discovery of potential therapeutic sites for KRAS and the development of several drugs directly targeting KRAS, especially KRAS G12C inhibitors such as AMG510 (sotorasib) and MRTX849 (adagrasib), which have shown encouraging results in clinical trials. In recent years, studies on the therapeutic efficacy of immune checkpoint inhibitors for KRAS-mutated NSCLC have made some progress. In this review, we systematically introduce the basic understanding of RAS gene and clinical characteristics of KRAS mutated NSCLC patients, summarize the medical treatments for KRAS mutated NSCLC, including chemotherapy, anti-vascular drug therapy and tumor immunotherapy, and focus on the review and outlook of the research progress of KRAS targeted therapy.

Effect of CT image acquisition parameters on diagnostic performance of radiomics in predicting malignancy of pulmonary nodules of different sizes.

OBJECTIVES: To investigate the effect of CT image acquisition parameters on the performance of radiomics in classifying benign and malignant pulmonary nodules (PNs) with respect to nodule size. METHODS: We retrospectively collected CT images of 696 patients with PNs from March 2015 to March 2018. PNs were grouped by nodule diameter: T1a (diameter ≤ 1.0 cm), T1b (1.0 cm < diameter ≤ 2.0 cm), and T1c (2.0 cm < diameter ≤ 3.0 cm). CT images were divided into four settings according to slice-thickness-convolution-kernels: setting 1 (slice thickness/reconstruction type: 1.25 mm sharp), setting 2 (5 mm sharp), setting 3 (5 mm smooth), and random setting. We created twelve groups from two interacting conditions. Each PN was segmented and had 1160 radiomics features extracted. Non-redundant features with high predictive ability in training were selected to build a distinct model under each of the twelve subsets. RESULTS: The performance (AUCs) on predicting PN malignancy were as follows: T1a group: 0.84, 0.64, 0.68, and 0.68; T1b group: 0.68, 0.74, 0.76, and 0.70; T1c group: 0.66, 0.64, 0.63, and 0.70, for the setting 1, setting 2, setting 3, and random setting, respectively. In the T1a group, the AUC of radiomics model in setting 1 was statistically significantly higher than all others; In the T1b group, AUCs of radiomics models in setting 3 were statistically significantly higher than some; and in the T1c group, there were no statistically significant differences among models. CONCLUSIONS: For PNs less than 1 cm, CT image acquisition parameters have a significant influence on diagnostic performance of radiomics in predicting malignancy, and a model created using images reconstructed with thin section and a sharp kernel algorithm achieved the best performance. For PNs larger than 1 cm, CT reconstruction parameters did not affect diagnostic performance substantially. KEY POINTS: • CT image acquisition parameters have a significant influence on the diagnostic performance of radiomics in pulmonary nodules less than 1 cm. • In pulmonary nodules less than 1 cm, a radiomics model created by using images reconstructed with thin section and a sharp kernel algorithm achieved the best diagnostic performance. • For PNs larger than 1 cm, CT image acquisition parameters do not affect diagnostic performance substantially.

Role of the Microbiota in Lung Cancer: Insights on Prevention and Treatment.

The microbiota is increasingly recognized as a critical player in cancer onset and progression and response to cancer chemotherapy treatment. In recent years, several preclinical and clinical studies have evidenced the involvement of microbiota in lung cancer, one of the world's deadliest cancers. However, the mechanisms by which the microbiota can impact this type of cancer and patient survival and response to treatments remain poorly investigated. In this review, the peculiarities of the gut and lung microbial ecosystems have been highlighted, and recent findings illustrating the possible mechanisms underlying the microbiota-lung cancer interaction and the host immune response have been discussed. In addition, the mucosal immune system has been identified as a crucial communication frame to ease interactive dynamics between the immune system and the microbiota. Finally, the use of specific next-generation intestinal probiotic strains in counteracting airway diseases has been evaluated. We believe that restoring homeostasis and the balance of bacterial microflora should become part of the routine of integrated cancer interventions, using probiotics, prebiotics, and postbiotics, and promoting a healthy diet and lifestyle.

[Impact of the examined number of lymph nodes at N1 station on the prognosis of patients with pT1-3N0M0 non-small cell lung cancer].

Objective: To investigate the relationship between the examined number of lymph nodes at the N1 station and the clinicopathological characteristics and prognosis of patients with pT1-3N0M0 non-small cell lung cancer (NSCLC). Methods: A total of 337 patients with pT1-3N0M0 NSCLC who underwent radical lung cancer surgery at the Provincial Hospital Affiliated to Anhui Medical University from January 2013 to March 2015 were selected. The receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off value for predicting 5-year survival in pT1-3N0M0 NSCLC patients by the examined number of lymph nodes at the N1 station. The relationships between the examined number of lymph nodes at the N1 station and the clinicopathological characteristics and prognosis of patients with pT1-3N0M0 NSCLC were analyzed according to the optimal cut-off group. Results: A total of 1 321 lymph nodes at N1 station were examined in 337 patients, with a mean of 3.9 nodes per patient. The median survival time was 42.0 months, with 1-, 3- and 5-year survival rates of 82.2%, 57.1% and 24.9%, respectively. ROC curve analysis showed that the optimal cut-off value of 4.5 lymph nodes examined at the N1 station was used to predict 5-year survival in patients with pT1-3N0M0 NSCLC. After rounding off the number, the number of lymph nodes examined at the N1 station was 5 as the cut-off value, and the patients were divided into the group with <5 lymph nodes examined (212 cases) and the group with ≥5 lymph nodes examined (125 cases). The proportion of patients received adjuvant chemotherapy was 19.2% in the group with ≥5 lymph nodes examined, which was higher than 9.0% in the group with <5 lymph nodes examined (P=0.007), and the differences in other clinicopathological characteristics between the two groups were not statistically significant (P>0.05). The median survival time for patients in the group with <5 lymph nodes examined was 38.0 months, with 1-, 3- and 5-year survival rates of 80.1%, 52.5% and 15.6%, respectively. The median survival time for patients in the group with ≥5 lymph nodes examined was 48.0 months, and the 1-, 3- and 5-year survival rates were 85.6%, 64.0% and 36.0%, respectively. The survival rate of patients in the group with ≥5 lymph nodes examined was better than that in the group with <5 lymph nodes examined (P=0.002). Multifactorial Cox regression analysis showed that T stage (OR=1.408, 95% CI: 1.118-1.670) and the examined number of lymph nodes at N1 station (OR=0.670, 95% CI: 0.526-0.853) were independent influence factors for the prognosis of pT1-3N0M0 NSCLC patients. Conclusion: The examined number of lymph nodes at the N1 station is associated with the prognosis of patients with pT1-3N0M0 NSCLC, and the examination of at least 5 lymph nodes at N1 station at the time of postoperative pathological examination improves the 5-year survival rate of patients.

Mean apparent diffusion coefficient in a single slice may predict tumor response to whole-brain radiation therapy in non-small-cell lung cancer patients with brain metastases.

OBJECTIVES: This study aimed to access the performance of apparent diffusion coefficient (ADC) as a predictor for treatment response to whole-brain radiotherapy (WBRT) in patients with brain metastases (BMs) from non-small-cell lung cancer (NSCLC). METHODS: A retrospective analysis was conducted of 102 NSCLC patients with BMs who underwent WBRT between 2012 and 2016. Diffusion-weighted MRI were performed pre-WBRT and within 12 weeks after WBRT started. Mean single-plane ADC value of ROIs was evaluated by two radiologists blinded to results of each other. The treatment response rate, intracranial progression-free survival (PFS), and overall survival (OS) were analyzed based on the ADC value and ΔADC respectively. At last, we used COX and logistic regression to do the multivariate analysis. RESULTS: There was good inter-observer agreement of mean ADC value pre-WBRT, post-WBRT, and ΔADC between the 2 radiologists (Pearson correlation 0.915 [pre-WBRT], 0.950 [post-WBRT], 0.937 [ΔADC], p < 0.001, for each one). High mean ADC value were related with better response rate (72.2% vs 37.5%, p = 0.001) and iPFS (7.6 vs 6.4 months, p = 0.031). High ΔADC were related with better response rate (73.6% vs 36.7%, p < 0.001). Multivariate analysis shows that histopathology, BMs number, high ADC value pre-WBRT, and high ΔADC post-WBRT were related to better treatment response of WBRT, and KPS, BMs number, and low ADC value pre-WBRT increased the risk of developing intracranial relapse. CONCLUSIONS: The mean single-plane ADC value pre-WBRT and ΔADC post-WBRT were potential predictor for intracranial tumor response to WBRT in NSCLC patients with brain metastases. KEY POINTS: • ADC value is a potential predictor of intracranial treatment response to WBRT in NSCLC patients with brain metastases. • Higher mean ADC value pre-WBRT and ΔADC post-WBRT of brain metastases were related to better intracranial tumor response. • Prediction of response before WBRT using ADC value can help oncologists to make better therapy plans and avoid missing opportunities for rescue therapy.

Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives.

OBJECTIVES: Radiomics is the extraction of quantitative data from medical imaging, which has the potential to characterise tumour phenotype. The radiomics approach has the capacity to construct predictive models for treatment response, essential for the pursuit of personalised medicine. In this literature review, we summarise the current status and evaluate the scientific and reporting quality of radiomics research in the prediction of treatment response in non-small-cell lung cancer (NSCLC). METHODS: A comprehensive literature search was conducted using the PubMed database. A total of 178 articles were screened for eligibility and 14 peer-reviewed articles were included. The radiomics quality score (RQS), a radiomics-specific quality metric emulating the TRIPOD guidelines, was used to assess scientific and reporting quality. RESULTS: Included studies reported several predictive markers including first-, second- and high-order features, such as kurtosis, grey-level uniformity and wavelet HLL mean respectively, as well as PET-based metabolic parameters. Quality assessment demonstrated a low median score of + 2.5 (range - 5 to + 9), mainly reflecting a lack of reproducibility and clinical evaluation. There was extensive heterogeneity between studies due to differences in patient population, cancer stage, treatment modality, follow-up timescales and radiomics workflow methodology. CONCLUSIONS: Radiomics research has not yet been translated into clinical use. Efforts towards standardisation and collaboration are needed to identify reproducible radiomic predictors of response. Promising radiomic models must be externally validated and their impact evaluated within the clinical pathway before they can be implemented as a clinical decision-making tool to facilitate personalised treatment for patients with NSCLC. KEY POINTS: • The included studies reported several promising radiomic markers of treatment response in lung cancer; however, there was a lack of reproducibility between studies. • Quality assessment using the radiomics quality score (RQS) demonstrated a low median total score of + 2.5 (range - 5 to + 9). • Future radiomics research should focus on implementation of standardised radiomics features and software, together with external validation in a prospective setting.

[Stereotactic body radiation therapy: radiobiological characteristics, physical-technical prerequisites, clinical applications]. / Extrakranielle Stereotaxie: strahlenbiologische Besonderheiten, physikalisch-technische Voraussetzungen, klinische Einsatzmöglichkeiten.

Radiotherapy of small targets with very high single doses administered in 1 to approximately 12 fractions-carried out under image guidance and with the intention of "tumour ablation"-is called stereotactic body radiation therapy (SBRT) for extracranial tumours or metastases. Radiobiologically, besides damaging the DNA of the tumour cells, the tumour vessels are also occluded and immunological effects are triggered. The safe performance of SBRT requires a very high physical-technical effort in order to ensure sufficient protection of healthy organs. Clinically, SBRT offers a wide range of applications in curative therapy (e.g. non-small-cell lung cancer stage I). Furthermore, it is a conservative, effective and well-tolerated option for the treatment of individual metastases and an optimal combination partner in the therapy of oligometastatic tumours.

[Predicting response to non-small cell lung cancer immunotherapy using pre-treatment contrast-enhanced CT texture-based classification].

Objective: To explore the value of pre-treatment contrast-enhanced computed tomography (CT)-based texture analysis in predicting response to non-small cell lung cancer (NSCLC) immunotherapy. Methods: From January to July 2018, a total of 51 lesions from 42 patients with advanced non-small cell lung cancer receiving immunotherapy at Shanghai Chest Hospital were selected in this retrospective study. Pre-treatment contrast-enhanced CT-based texture features were extracted by MaZda software. Ten optimal texture features were chosen based on three different methods: Fisher coefficient, mutual information measure (MI) and minimization of classification error probability combined average correlation coefficients(POE+ ACC), respectively. According to the efficacy of the first immunotherapy, 51 lesions were divided into non-progressive disease (non-PD, n=26) and progressive disease (PD, n=25). The differences were tested in each texture feature set between the two groups. The immunotherapy effects of target lesions were analyzed by principal component analysis(PCA), linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA). The sensitivity, specificity, accuracy, positive-predictive value (PPV) and negative-predictive value (NPV) were calculated. The area under the curve (AUC) was used to quantify the predictive accuracy of the three analysis models for each texture feature set and compare them with the actual classification results. Results: In all of three texture feature sets, the texture parameter differences of Perc.50%, Perc.90%, "S(5, 5)SumEntrp" and "S(4, 4)SumEntrp" were higher in PD group than those in non-PD group (all P<0.05). The classification result of texture feature set chosen by POE+ ACC and analyzed by NDA was identified as the best model (AUC=0.802, 95%CI: 0.674-0.930), and its sensitivity, specificity, accuracy, PPV and NPV were 72%, 88.5%, 80.4%, 85.7%, 76.7%, respectively. Conclusion: Pre-treatment contrast-enhanced CT-based texture characteristics of NSCLC may function as non-invasive biomarkers for the evaluation of response to immunotherapy.