Small Lung Adenocarcinoma Research Articles

The association between human papillomavirus and lung cancer: A Mendelian randomization study

BackgroundTo investigate the causal relationship between human papillomavirus (HPV) and lung cancer, we conducted a study using the two-sample Mendelian randomization (TSMR). MethodData from genome-wide association studies (GWAS) were analyzed with HPV E7 Type 16 and HPV E7 Type 18 as exposure factors. The outcome variables included lung cancer, small cell lung cancer, adenocarcinoma and squamous cell lung cancer. Causality was estimated using inverse variance weighted (IVW), MR-Egger and weighted median methods. Heterogeneity testing, sensitivity analysis, and multiple validity analysis were also performed.. ResultsThe results showed that HPV E7 Type 16 infection was associated with a higher risk of squamous cell lung cancer (OR = 7.69; 95% CI:1.98–29.85; p = 0.0149). HPV E7 Type 18 infection significantly increased the risk of lung adenocarcinoma (OR = 0.71; 95% CI: 0.38–1.31; p = 0.0079) and lung cancer (OR = 7.69; 95% CI:1.98–29.85; p = 0.0292). No significant causal relationship was found between HPV E7 Type 16 and lung adenocarcinoma, lung cancer, or small cell lung carcinoma, and between HPV E7 Type 18 and squamous cell lung cancer or small cell lung carcinoma. ConclusionsThis study has revealed a causal relationship between HPV and lung cancers. Our findings provide valuable insights for further mechanistic and clinical studies on HPV-mediated cancer.

Clinical Importance of Grading Tumor Spread through Air Spaces in Early-Stage Small-Lung Adenocarcinoma.

This study aimed to identify the clinical manifestation and implications according to the grading of tumor spread through air spaces in early-stage small (≤2 cm) pathological stage I non-mucinous lung adenocarcinomas. Medical records of patients with pathological stage I tumors sized ≤2 cm were retrospectively reviewed and analyzed. The furthest distance of the spread through air spaces from the tumor margin was measured on a standard-length scale (mm). Enrolled patients were categorized into spread through air spaces (STAS) (-) and STAS (+), and STAS (+) was subdivided according to its furthest distance as follows: STAS (+)-L (<2 mm) and STAS (+)-H (≥2 mm). Risk factors for STAS (+) included papillary predominant subtype (p = 0.027), presence of micropapillary patterns (p < 0.001), and EGFR (p = 0.039). The overall survival of the three groups did not differ significantly (p = 0.565). The recurrence-free survival of STAS (+)-H groups was significantly lower than those of STAS (-) and STAS (+)-L (p < 0.001 and p = 0.039, respectively). A number of alveolar spaces were definite risk factors for STAS (+)-H groups (p < 0.001), and male gender could be one (p = 0.054). In the patient group with small (≤2 cm) pathological stage I lung adenocarcinomas, the presence of STAS ≥ 2 mm was related to significantly lower recurrence-free survival. For identifying definite risk factors for the presence of farther STAS, more precise analysis from a larger study population should be undertaken.

The journey of patients with lung cancer: The gap between symptom onset and treatment.

e23119 Background: Prompt access to health care services is a priority in public health policies. The aim of this study was to map the journey of patients (pts) diagnosed with lung cancer (LC) and determine the time interval (TI) from symptom onset (SO) to treatment initiation and assess the impact on survival. Methods: A retrospective cohort study was conducted at the University Hospital of Larissa. Pts’ demographic and disease-related data were captured from medical records. The TI from SO to first doctor visit (StD), SO to first oncologist visit (StO), diagnosis to treatment initiation (DtT) and SO to treatment initiation (StT) were estimated. Overall survival (OS) was defined as the TI between treatment initiation and the date of death or the date of last follow-up. OS were estimated using the Kaplan–Meier analysis and the comparisons were computed with the log-rank test. Results: Overall, 251 sequential pts with metastatic LC were included in the study. Pts’ mean age was 71 years (range 35-88). The majority were men (82%) and 24%, 32%, 28% and 16% were diagnosed with small cell lung cancer (SCLC), squamous cell (SCC), adenocarcinoma (ADC) and large cell (LCC) LC, respectively. The median StD and StO was 32 (0-212) days and 102 (11-608) days, respectively. No difference was detected across genders, age ( &gt; or &lt; 70), family history of cancer or not, and area of residence (rural, urban). The median StT and DtT was 123 (30-851) days and 26 (1-105) days, respectively, with this time intervals being shorter among pts with SCLC compared to pts with non-small cell LC (NSCLC) ( p: 0.006 and p&lt; 0.001, respectively). Pts with sort StT intervals had significantly improved OS, compared to pts with longer StT intervals (SCLC p:0.044, NSCLC p:0.004). Conclusions: To the best of our knowledge, this is the first study aiming to determine specific TI from SO to treatment initiation for pts with LC in Greece and assess the potential impact on OS. Our data revealed a substantially prolonged StT (~4months) that is associated with worse survival rates. This indicates a need to improve public awareness of LC symptoms, enable pts’ access to specialized health care services and facilitate earlier diagnosis and treatment.

Clinical Efficacy Analysis of Wedge Resection of Pulmonary in Patients with Small Volume Invasive Lung Adenocarcinoma

With further understanding and research into non-small cell lung cancer with tumours ≤2 cm in maximum diameter, segmental lung resection is able to achieve the same long-term prognosis as lobectomy. However, there are few studies on the prognostic effect of wedge resection on small volume invasive lung adenocarcinoma with an invasion depth of 0.5 to 1.0 cm. Therefore, this study focuses on the clinical efficacy and prognosis of wedge resection in patients with small-volume invasive lung adenocarcinoma. A retrospective analysis of the medical records of 208 patients who underwent surgery in the Department of Thoracic Surgery of the Affiliated Provincial Hospital of Anhui Medical University from February 2016 to December 2017 was made, and the postoperative pathological results confirmed small volume invasive lung adenocarcinoma. According to their surgical methods, they were divided into lobectomy group (n=115), segmentectomy group (n=48) and wedge resection group (n=45). Kaplan-Meier survival curve estimation and Cox proportional risk regression model were used to explore the influence of different surgical methods on the prognosis of patients with small volume invasive lung adenocarcinoma. The wedge resection group had better perioperative outcomes compared with the segmentectomy group and lobectomy group, with statistically significant differences in intraoperative bleeding (P=0.036), postoperative drainage (P<0.001), operative time (P=0.018), postoperative time with tubes (P=0.001), and postoperative complication rate (P=0.006). There were no significant differences when comparing the three groups in terms of survival rate (lobectomy group vs segmentectomy group, P=0.303; lobectomy group vs wedge resection group, P=0.742; and segmentectomy group vs wedge resection group, P=0.278) and recurrence-free survival rate (lobectomy group vs segmentectomy group, P=0.495; lobectomy group vs wedge resection group, P=0.362; segmentectomy group vs wedge resection group, P=0.775). Univariate and multivariate survival analyses showed that consolidation tumor ratio (CTR) was the prognostic factor of overall survival and revurrence-free survival for patients with small-volume invasive lung adenocarcinoma (P<0.05). Wedge resection in patients with small volume invasive lung adenocarcinoma can achieve long-term outcomes similar to segmentectomy and lobectomy. When the CTR≤0.5, wedge resection is preferred in such patients.

Characterization of two transcriptomic subtypes of marker-null large cell carcinoma of the lung suggests different origin and potential new therapeutic perspectives

Pulmonary large cell carcinoma (LCC) is an undifferentiated neoplasm lacking morphological, histochemical, and immunohistochemical features of small cell lung cancer, adenocarcinoma (ADC), or squamous cell carcinoma (SCC). The available molecular information on this rare disease is limited. This study aimed to provide an integrated molecular overview of 16 cases evaluating the mutational asset of 409 genes and the transcriptomic profiles of 20,815 genes. Our data showed that TP53 was the most frequently inactivated gene (15/16; 93.7%) followed by RB1 (5/16; 31.3%) and KEAP1 (4/16; 25%), while CRKL and MYB genes were each amplified in 4/16 (25%) cases and MYC in 3/16 (18.8%) cases; transcriptomic analysis identified two molecular subtypes including a Pure-LCC and an adenocarcinoma like-LCC (ADLike-LCC) characterized by different activated pathways and cell of origin. In the Pure-LCC group, POU2F3 and FOXI1 were distinctive overexpressed markers. A tuft cell-like profile and the enrichment of a replication stress signature, particularly involving ATR, was related to this profile. Differently, the ADLike-LCC were characterized by an alveolar-cell transcriptomic profile and association with AIM2 inflammasome complex signature. In conclusion, our study split the histological marker-null LCC into two different transcriptomic entities, with POU2F3, FOXI1, and AIM2 genes as differential expression markers that might be probed by immunohistochemistry for the differential diagnosis between Pure-LCC and ADLike-LCC. Finally, the identification of several signatures linked to replication stress in Pure-LCC and inflammasome complex in ADLike-LCC could be useful for designing new potential therapeutic approaches for these subtypes.

Comprehensive profiling of serum glycosphingolipids to discover the diagnostic biomarkers of lung cancer and uncover the variation of glycosphingolipid networks in different lung cancer subtypes.

Glycosphingolipids are glycolipid complexes formed by an oligosaccharide chain covalently linked to a ceramide backbone and play important roles in the occurrence and metastasis of lung cancer. In this study, an UHPLC-HRMS method was developed for the comprehensive profiling of glycosphingolipids, with an in-house library constructed for data interpretation. Serum glycosphingolipids were profiled in 31 healthy controls (HCs) and 92 lung cancer patients with different pathologic subtypes. Over 1700 glycosphingolipids were detected in human serum based on the novel method. A total of 567 differential glycosphingolipids (adjusted P < 0.05, and fold change > 2) were found between lung cancer patients and HCs. Glycosphingolipids can be used as potential biomarkers for lung cancer diagnosis, with sensitivity much higher than that of traditional serum tumor markers. The levels of most glycosphingolipids in squamous cell carcinoma (Squa) were significantly lower than those in small cell lung cancer (SCLC) and adenocarcinoma (Aden). The highest Cer1P abundance in SCLC patients among the three different subtypes of lung cancer was thought to be related to the high malignancy and metastasis of SCLC. An artificial neural network (ANN) model was constructed for the discrimination of the three different subtypes of lung cancer, with accuracy higher than 93%. Beyond providing biomarkers and statistical models for the diagnosis of lung cancer and discrimination of lung cancer subtypes, this study uncovered the variation of glycosphingolipid networks in different subtypes of lung cancer and thereby provided a novel insight to study the pathogenesis of lung cancer and explore therapeutic targets.

Rare giant ovarian metastasis arising from a small primary lung adenocarcinoma: a case report.

This intricate case report details an exceptionally rare incidence of ovarian metastasis originating from a primary lung adenocarcinoma (LUAD). The relative rarity of this metastatic pathway in medical literature indicates significant diagnostic challenges. This patient was initially found to have both the ovarian tumor and lung nodule and they were originally considered independent primary tumors, derived from radiological interpretations and biomarker profiling. Nevertheless, subsequent postoperative histopathological and immunohistochemical staining evaluations identified ovarian tumors as invasive adenocarcinoma metastasized from lung. The lung and ovary tumor both showed marked anaplastic lymphoma kinase gene (ALK) protein expression by immunohistochemistry. The molecular pathologic genetic testing for lung tumor also revealed ALK rearrangement positive. The complexity of this case underscores the essentiality of maintaining a high degree of diagnostic vigilance, particularly when confronting synchronous tumors. In addition, immunohistochemical staining plays an important role in diagnosing the ovarian neoplasm's metastatic nature and determining the primary site of metastatic adenocarcinoma. For lung cancer with ovary metastasis patients, the adopting an adaptable treatment approach responsive to evolving diagnostic evidence can improve the accuracy of diagnosis and avoid excessive treatment of patients.

CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.

Exploring a non-invasive method to accurately differentiate peripheral small cell lung cancer (PSCLC) and peripheral lung adenocarcinoma (PADC) could improve clinical decision-making and prognosis. This retrospective study reviewed the clinicopathological and imaging data of lung cancer patients between October 2017 and March 2022. A total of 240 patients were enrolled in this study, including 80 cases diagnosed with PSCLC and 160 with PADC. All patients were randomized in a seven-to-three ratio into the training and validation datasets (170 vs. 70, respectively). The least absolute shrinkage and selection operator regression was employed to generate radiomics features and univariate analysis, followed by multivariate logistic regression to select significant clinical and radiographic factors to generate four models: clinical, radiomics, clinical-radiographic, and clinical-radiographic-radiomics (comprehensive). The Delong test was to compare areas under the receiver operating characteristic curves (AUCs) in the models. Five clinical-radiographic features and twenty-three selected radiomics features differed significantly in the identification of PSCLC and PADC. The clinical, radiomics, clinical-radiographic and comprehensive models demonstrated AUCs of 0.8960, 0.8356, 0.9396, and 0.9671 in the validation set, with the comprehensive model having better discernment than the clinical model (P=0.036), the radiomics model (P=0.006) and the clinical-radiographic model (P=0.049). The proposed model combining clinical data, radiographic characteristics and radiomics features could accurately distinguish PSCLC from PADC, thus providing a potential non-invasive method to help clinicians improve treatment decisions.

The predictive accuracy of CT radiomics combined with machine learning in predicting the invasiveness of small nodular lung adenocarcinoma.

Conventionally, the judgment of whether small pulmonary nodules are invasive is mainly made by thoracic surgeons according to the chest computed tomography (CT) features of patients. However, there are limits to how much useful information can be obtained from this approach. A large number of feature information was extracted from CT images by CT radiomics. The machine learning algorithm was used to construct models based on radiomic characteristics to predict the invasiveness of lung adenocarcinoma (LUAD) with a good prediction accuracy. A total of 416 patients with pathologically confirmed preinvasive lesions and LUAD after video-assisted thoracoscopic surgery (VATS) in the Department of Thoracic Surgery of the First People's Hospital of Yunnan Province from February 2020 to February 2022 were retrospectively analyzed. According to random classification, patients were divided into 2 groups. The RadCloud platform was used to extract radiomics features, and the most relevant radiomics features were selected by continuous dimension reduction method. Then, 6 machine learning algorithms were used to establish and verify the prediction model of small lung nodular adenocarcinoma invasiveness. Receiver operating characteristic (ROC) curve and area under curve (AUC) were used to evaluate the predictive performance. There were 78 cases of pre-invasive lesions and 226 cases of invasive lesions in the training group, and 34 cases of pre-invasive lesions and 78 cases of invasive lesions in the validation group. In the training group, the AUC values of the 6 models were all more than 0.914, the 95% confidence interval (CI) was 0.857-1.00, the sensitivity was equal or more than 0.87, and the specificity was equal or more than 0.85. In the validation group, the AUC values of the 6 models were all equal or more than 0.732, the 95% CI was 0.651-1.00, the sensitivity was equal or more than 0.7, and the specificity was more than 0.77. Machine learning algorithms were used to construct models to predict the invasiveness of small nodular LUAD based on radiomics features, which it could provide more evidence for doctors to make diagnoses and more personalized treatment plans for patients.

Factors associated with preoperative and postoperative seizures in patients undergoing resection of brain metastases.

Epileptic seizures are a common and potentially devastating complication of metastatic brain tumors. Although tumor-related seizures have been described in previous case series, most studies have focused on primary brain tumors and have not differentiated between different types of cerebral metastases. The authors analyzed a large surgical cohort of patients with brain metastases to examine risk factors associated with preoperative and postoperative seizures and to better understand the seizure risk factors of metastatic brain tumors. Patients who underwent resection of a brain metastasis at the University of California, San Francisco (UCSF), were retrospectively reviewed. Patients included in the study were ≥ 18 years of age, required resection of a brain metastasis, and were treated at UCSF. Primary cancers included melanoma, non-small cell lung adenocarcinoma, breast adenocarcinoma, colorectal adenocarcinoma, esophageal adenocarcinoma, gastric adenocarcinoma, renal cell carcinoma, urothelial carcinoma, ovarian carcinoma, cervical squamous cell carcinoma, and endometrial adenocarcinoma. Patients were evaluated for primary cancer type and seizure occurrence, as well as need for use of antiepileptic drugs preoperatively, at time of discharge, and at 6 months postoperatively. Additionally, Engel classification scores were assigned to those patients who initially presented with seizures preoperatively. Univariate and multivariate regression analyses were used to assess the association of tumor type with preoperative seizures. Data were retrospectively analyzed for 348 consecutive patients who underwent surgical treatment of brain metastases between 1998 and 2019. The cohort had a mean age of 60 years at the time of surgery and was 59% female. The mean and median follow-up durations after the date of surgery for the cohort were 22 months and 10.8 months, respectively. In univariate analysis, frontal lobe location (p = 0.05), melanoma (p = 0.02), KRAS mutation in lung carcinoma (p = 0.04), intratumoral hemorrhage (p = 0.04), and prior radiotherapy (p = 0.04) were associated with seizure presentation. Postoperative checkpoint inhibitor use (p = 0.002), prior radiotherapy (p = 0.05), older age (p = 0.002), distant CNS progression (p = 0.004), and parietal lobe tumor location (p = 0.002) were associated with seizures at 6 months postoperatively. The final multivariate model confirmed the independent effects of tumor location in the frontal lobe and presence of intratumoral hemorrhage as predictors of preoperative seizures, and checkpoint inhibitor use and parietal lobe location were identified as significant predictors of seizures at 6 months postoperatively. Within this surgical cohort of patients with brain metastases, seizures were seen in almost a quarter of patients preoperatively. Frontal lobe metastases and hemorrhagic tumors were associated with higher risk of preoperative seizures, whereas checkpoint inhibitor use and parietal lobe tumors appeared to be associated with seizures at 6 months postoperatively. Future research should focus on the effect of metastatic lesion-targeting therapeutic interventions on seizure control in these patients.

99. Histopathologic Correlation Somatic Variants in Non Small Cell Lung Cancer with High Tumor Mutation Burden

<h3>Introduction</h3> High tumor mutation burden (TMB), having at least 10 mutations/megabase (mut/Mb), is reported to be as high as 42% of non small cell lung adenocarcinoma (LADC). Micropapillary adenocarcinoma is one of the most aggressive histologic subtypes of lung adenocarcinoma (LADC) and is often associated with a poor prognosis. <h3>Methods</h3> We performed next-generation sequencing (NGS) with and explored histologic and molecular features in 33 consecutive TMB High LADC utilizing the Ion Oncomine Comprehensive Panel Plus (OCAPlus). <h3>Results</h3> LADC with high tumor mutation burdens (TMBs) were more likely to display necrosis, as well as papillary and aggressive micropapillary features. Interestingly, RBM10 truncations were enriched in aggressive TMB LACD, seen in 2/2 cases of pleomorphic carcinoma and 4/7 cases with papillary or micropapillary features. BRAF class 2 mutations (non V600E) were also more likely to be seen in these aggressive tumors, identified in 5/33 cases of TMB high LADC, while no class 1 BRAF V600E mutations were observed. <h3>Conclusions</h3> A subset of high TMB LADC is especially aggressive, with histology including necrosis, micropapillary, and pleomorphic features. A subset of these tumors, with an especially poor overall survival rate contain either class 2 BRAF variants or truncated RBM10 mutations in 7/10 cases identified with these aggressive histologic features.

Contribution of F-18 fluorodeoxyglucose PET/CT and contrast-enhanced thoracic CT texture analyses to the differentiation of benign and malignant mediastinal lymph nodes.

Texture analysis and machine learning methods are useful in distinguishing between benign and malignant tissues. To discriminate benign from malignant or metastatic mediastinal lymph nodes using F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and contrast-enhanced computed tomography (CT) texture analyses with machine learning and determine lung cancer subtypes based on the analysis of lymph nodes. Suitable texture features were entered into the algorithms. Features that statistically significantly differed between the lymph nodes with small cell lung cancer (SCLC), adenocarcinoma (ADC), and squamous cell carcinoma (SCC) were determined. The most successful algorithms were decision tree with the sensitivity, specificity, and area under the curve (AUC) values of 89%, 50%, and 0.692, respectively, and naive Bayes (NB) with the sensitivity, specificity, and AUC values of 50%, 81%, and 0.756, respectively, for PET/CT, and NB with the sensitivity, specificity, and AUC values of 10%, 96%, and 0.515, respectively, and logistic regression with the sensitivity, specificity, and AUC values of 21%, 83%, and 0.631, respectively, for CT. In total, 13 features were able to differentiate SCLC and ADC, two features SCLC and SCC, and 33 features ADC and SCC lymph node metastases in PET/CT. One feature differed between SCLC and ADC metastases in CT. Texture analysis is beneficial to discriminate between benign and malignant lymph nodes and differentiate lung cancer subtypes based on the analysis of lymph nodes.

One-step algorithm for fast-track localization and multi-category classification of histological subtypes in lung cancer

BackgroundsAccumulated evidence has proven that computer-derived features from computed tomography (CT) through radiomics and deep learning technologies can identify extensive characteristics of pulmonary malignancies, such as nodules detection and malignant lesion discrimination. However, there are few studies on whether CT images can reflect histological subtypes of lung cancer through computer-derived features. MethodsContrast-enhanced CT images prior treatment from 417 patients diagnosed with small cell lung cancer (SCLC), lung adenocarcinoma (ADC), or lung squamous cell carcinoma (SCC) were collected. ITK-SNAP software was used by trained radiologists for the manual delineation of tumor volume. Patients of each category (SCLC, ADC, SCC) were then randomly split into training datasets and test datasets in an approximately ratio of 8:2. After image pre-processing and augmentation, 25,042 CT images from the training datasets were used to train our self-developed deep learning model for fast-tracking tumor lesions and classifying corresponding histological subtypes simultaneously. The performance of the network was evaluated by accuracy, F1-score and weighted F1-average using 1,921 testing images based on parameters generated during training. ResultsThe prediction accuracy of SCLC, ADC, and SCC were 0.83, 0.75 and 0.67, respectively. The weighted F1-average was 0.75. ADC obtained the best F1-score of 0.78, which was outperformed SCLC (0.77) and SCC (0.66). The corresponding AUC values of SCLC, ADC, and SCC were 0.87, 0.84, and 0.76, respectively. Only 0.24 s were required to simultaneously achieve functions of tumor localization and histological classification on a thoracic CT image slice. The heat map visualization illustrated the extracted tumor features to classify subtypes of lung cancer by the proposed model. ConclusionsThe newly developed multi-task algorithm provides a CNN-based DL approach in lung cancer for automatically fast-tracking tumor lesions and classifying corresponding histological subtypes in one-step.

Combination of mean CT value and maximum CT value as a novel predictor of lepidic predominant lesions in small lung adenocarcinoma presenting as solid nodules

Lung adenocarcinomas presenting as solid nodules are occasionally diagnosed as lepidic predominant lesions. The aim of this study was to clarify the histological structure and to identify factors predictive of lepidic predominant lesions. We retrospectively reviewed 38 patients that underwent lobectomy for small (≤ 2 cm) adenocarcinoma presenting as solid nodules. Resected tumor slides were reviewed and histological components were evaluated. Clinical and radiological data were analyzed to identify factors predictive of lepidic predominant lesions. Of 38 solid nodules, 9 (23.7%) nodules were lepidic predominant lesions. Five-year disease-free survival (DFS) rates were 100% for lepidic predominant lesions (n = 9) and 74.6% for non-lepidic predominant lesions (n = 29). Mean CT values (p = 0.039) and maximum CT values (p = 0.015) were significantly lower in lepidic predominant lesions compared with non-lepidic predominant lesions. For the prediction of lepidic predominant lesions, the sensitivity and specificity of mean CT value (cutoff, − 150 HU) were 77.8% and 82.8%, respectively, and those of maximum CT value (cutoff, 320 HU) were 77.8% and 72.4%, respectively. A combination of mean and maximum CT values (cutoffs of − 150 HU and 380 HU for mean CT value and maximum CT value, respectively) more accurately predicted lepidic predominant lesions, with a sensitivity and specificity of 77.8% and 86.2%, respectively. The prognosis of lepidic predominant lesions was excellent, even for solid nodules. The combined use of mean and maximum CT values was useful for predicting lepidic predominant lesions, and may help predict prognosis.

Association of genetic polymorphisms in DNA repair genes ERCC2 Asp312Asn (rs1799793), ERCC2 Lys 751 Gln (rs13181), XRCC1 Arg399 Gln (rs25487) and XRCC3 Thr 241Met (rs861539) with the susceptibility of lung cancer in Saudi population

This study demonstrated the association of polymorphisms in ERCC2 (Asp312Asn) rs1799793, ERCC2 (Lys751Gln) rs13181, XRCC1 (Arg399Gln) rs25487 and XRCC3(Thr241Met) rs861539 polymorphisms with a susceptibility of lung cancer (LC) onset in the Saudi population. The study was performed on 134 LC patients and 270 controls. The data revealed that there was no significant association of LC with subtype squamous cell carcinoma (SCC), small cell lung cancer (SCLC) and adenocarcinoma with the ERCC2 rs1799793 polymorphism. The data showed that the CC genotype for ERCC2 rs13181, the AA genotype for XRCC1 rs25487, and the genotype TT for XRCC3 rs861539 were significantly associated with SCC susceptibility (p < 0.05). Similarly, the CC genotype for ERCC2 rs13181 and the AA genotype for XRCC1 rs25487 were significantly associated with adenocarcinoma susceptibility (p < 0.05). Whereas, the TT genotype for XRCC3 rs861539 was significantly associated with SCLC susceptibility (p = 0.005). In total, significant association of LC susceptibility was found in the following combination models of recessive genotypes: AC heterozygous for ERCC2 rs13181 + AA homozygous for XRCC1 rs25487, CC homozygous for ERCC2 rs13181 + GA heterozygous for rs25487, CC homozygous for rs13181 + AA homozygous for XRCC1 rs25487, CC homozygous for ERCC2 rs13181 + TT homozygous for XRCC3 rs861539, GA heterozygous for XRCC1 rs25487 + CT heterozygous for XRCC3 rs861539, GA heterozygous for XRCC1 rs25487 + TT homozygous for XRCC3 rs861539, AA homozygous for XRCC1 rs25487 + CT heterozygous for XRCC3 rs861539, AA homozygous for XRCC1 rs25487+ TT homozygous for XRCC3 rs861539. These data clearly demonstrated that the combination of recessive genotypes may be associated with susceptibility of LC onset (p < 0.05). In short, the data indicated that DNA repair genes increase LC risk via gene-gene interaction rather than independent variants.

Immunohistochemical diagnosis and prognosis of small cell lung cancer: the search for new strategies

The aim: to find the optimal combination of immunohistochemical markers for differential diagnosis and prognosis of small cell lung cancer in small biopsy samples.&#x0D; Materials and methods. The tumor specimens were divided into 3 groups: 1) 25 biopsy samples of small cell lung cancer before treatment; 2) 25 samples of small cell lung cancer procured from autopsies of patients, who underwent chemotherapy; 3) 15 biopsy samples of other lung tumors histologically similar to SCLC. All tumor samples were formalin fixed and paraffin embedded (FFPE). Immunohistochemical study performed with 5 primary antibodies: CD56, p16ink4A, TTF-1, CD117, Ki-67.&#x0D; Results. TTF-1 was positive in all small cell lung cancer, lung adenocarcinomas and atypical carcinoids. Expression of CD56 was positive in 100 % of tumors from 1st group and 92 % of these tumors had more than 25 % of positive tumor cells. Expression of p16ink4A was significantly higher in 1st group than in the 3rd one (р&lt;0,001). The stepwise logistic regression was used for finding the best markers for differential diagnosis of small cell lung cancer in small biopsy samples. The next combination of markers was chosen: TTF-1/CD56 (score 2–4)/p16 ink4A/CD117 (sensitivity – 80 %; specificity – 86.67 %; р&lt;0,001) where “score 2–4” means expression of CD56 more than in 25 % tumor cells. Expression of Ki-67 was higher in the 2nd group compared with the 1st one (р&lt;0,001).&#x0D; Conclusion. Evaluation of p16 expression can be used as additional marker for differential diagnosis of small cell lung cancer. The following combination of markers: TTF-1/CD56 (score2-4)/p16 ink4A/CD117 could be useful in diagnosis of small cell lung cancer in small biopsy samples and in the choice of targeted chemotherapy. The further study in paired tumor samples of small cell lung cancer before and after chemotherapy is required to prove the significance of changes in expression of Ki-67, CD56, CD117 and p16ink4A

Distant organ metastasis patterns in lung cancer patients with pancreatic metastasis - a cluster analysis.

Pancreatic metastasis of lung cancer is rare, but the narrowing or obstruction of the biliary tract from pancreatic metastases limits the choice of antitumour agents. Lung cancer patients with pancreatic metastasis often have metastasis of other organs. For these patients, however, no studies have been conducted to evaluate distant metastasis sites as metastasis patterns. This study aims to assess whether these patients have specific metastasis patterns at the time of diagnosis of lung cancer. Data were collected from consecutive lung cancer patients with pancreatic metastasis between April 2012 and March 2022. Metastatic patterns were analysed using cluster analysis in patients with lung cancer. During the study period, 33 (2.0%) of 1659 patients were diagnosed as having pancreatic metastasis. Of the 33 patients, 28 (84.8%) were male. Eighteen, 14, and one patient had small cell lung cancer (SCLC), lung adenocarcinoma, and large cell neuroendocrine carcinoma, respectively. They were divided into 3 groups by cluster analysis. A statistically significant difference in metastasis frequency was confirmed among these 3 groups (χ2 test, p = 0.001). In lung cancer patients with pancreatic metastasis, knowledge of the metastatic patterns might be useful for clinical practice in the foreseeable future because it enables more efficient detection of metastatic disease through imaging, and more effective treatment at predicted metastatic sites.

Circulating C-reactive protein increases lung cancer risk: Results from a prospective cohort of UK Biobank.

Chronic inflammation has been associated with the development of lung cancer. In this study, we examined the association between C-reactive protein (CRP) and lung cancer in a prospective cohort study and used Mendelian randomization (MR) to clarify the causality. We included 420 977 participants from the UK Biobank (UKB) in the analyses; 1892 thereof were diagnosed with lung cancer during the follow-up. Hazards ratios (HRs) of CRP concentrations were estimated by Cox proportional hazard models and two approaches of MR analysis were performed. Besides, we added CRP concentrations to epidemiological model of lung cancer to evaluate its prediagnostic role through time-dependent receiver operating characteristic curve analysis. Elevated CRP levels were associated with a 22% increased lung cancer risk per 1 SD increase (HR=1.22, 95% confidence interval [CI]=1.18-1.26). Positive associations were observed in small cell lung cancer (HR=1.21, 95%CI=1.10-1.33), lung adenocarcinoma (HR=1.17, 95%CI=1.11-1.23) and lung squamous cell carcinoma (HR=1.22, 95%CI=1.14-1.31). No genetical association of circulating CRP levels and lung cancer risk was observed in MR analysis. When added to a risk model of lung cancer, CRP improved the performance of model as long as 8 years among current smokers (basic model: C-statistic=0.78 [95%CI=0.75-0.80]; CRP model: C-statistic=0.79 [95%CI=0.76-0.81]; Pnonadjusted =.003, Padjusted =.014). Our results did not support the causal association of circulating CRP with lung cancer risk. However, circulating CRP could be a prediagnostic marker of lung cancer as long as 8 years in advance for current smokers.

Detection of Lung Cancer on Computed Tomography Using Artificial Intelligence Applications Developed by Deep Learning Methods and the Contribution of Deep Learning to the Classification of Lung Carcinoma.

Every year, lung cancer contributes to a high percentage deaths in the world. Early detection of lung cancer is important for its effective treatment, and non-invasive rapid methods are usually used for diagnosis. In this study, we aimed to detect lung cancer using deep learning methods and determine the contribution of deep learning to the classification of lung carcinoma using a convolutional neural network (CNN). A total of 301 patients diagnosed with lung carcinoma pathologies in our hospital were included in the study. In the thorax, Computed Tomography (CT) was performed for diagnostic purposes prior to the treatment. After tagging the section images, tumor detection, small and non-small cell lung carcinoma differentiation, adenocarcinoma-squamous cell lung carcinoma differentiation, and adenocarcinoma-squamous cell-small cell lung carcinoma differentiation were sequentially performed using deep CNN methods. In total, 301 lung carcinoma images were used to detect tumors, and the model obtained with the deep CNN system exhibited 0.93 sensitivity, 0.82 precision, and 0.87 F1 score in detecting lung carcinoma. In the differentiation of small cell-non-small cell lung carcinoma, the sensitivity, precision and F1 score of the CNN model at the test stage were 0.92, 0.65, and 0.76, respectively. In the adenocarcinoma-squamous cancer differentiation, the sensitivity, precision, and F1 score were 0.95, 0.80, and 0.86, respectively. The patients were finally grouped as small cell lung carcinoma, adenocarcinoma, and squamous cell lung carcinoma, and the CNN model was used to determine whether it could differentiate these groups. The sensitivity, specificity, and F1 score of this model were 0.90, 0.44, and 0.59, respectively, in this differentiation. In this study, we successfully detected tumors and differentiated between adenocarcinoma- squamous cell carcinoma groups with the deep learning method using the CNN model. Due to their non-invasive nature and the success of the deep learning methods, they should be integrated into radiology to diagnose lung carcinoma.

A clinicopathologic analysis of microscopic extension in small cell lung cancer and lung adenocarcinoma: Determination of clinical target volume with precise radiotherapy.

PurposeThe identification of the clinical target volume (CTV) is particularly important in the precise radiotherapy of lung cancer. The purpose of this study was to determine the extension margin from gross tumor volume (GTV) to CTV in primary small cell lung cancer (SCLC) and lung adenocarcinoma (ADC) by microscopic extension (ME).Material and MethodsThe data of 25 cases of SCLC and 29 cases of ADC from August 2015 to August 2020 were analyzed. The measurement of tumor size between preoperative thoracic computed tomography (CT) and postoperative macroscopic specimens was compared, and the ME range of tumor cells was measured under a microscope to determine its correlation with clinical features and pathological manifestations.ResultsA total of 217 slides were examined, corresponding to 103 slides for SCLC and 114 slides for ADC. The radiologic sizes of the tumors in SCLC and ADC were 12.8 and 7.9 mm, respectively (p = 0.09), and the macroscopic sizes were 12.5 and 8.5 mm, respectively (p = 0.07). There was a significant correlation between the radiologic and macroscopic size of the same tumor sample (r = 0.886). Compared with ADC, more SCLC tumor cells infiltrated through vascular or lymphatic dissemination (16% vs. 9%, p = 0.047). The mean ME value was 2.81 mm for SCLC and 2.02 mm for ADC (p = 0.012). To take into account 95% of the ME, a margin of 8 and 7.7 mm must be expanded for SCLC and ADC, respectively. The ME value of the tumor was related to the presence of atelectasis, the location of the tumor, and the Ki‐67 cell proliferation index.ConclusionThe GTV of the tumor was contoured according to CT images, which was basically consistent with the actual tumor size. The GTVs of SCLC and ADC should be expanded by 8 and 7.7 mm, respectively, to fully cover the subclinical lesions in 95% of cases.