Large Cell Carcinoma Research Articles

A Novel Approach to Diagnosis: Using Convolutional Neural Networks to Classify Non-Small Cell Lung Cancers on CT Scans

Background: Lung cancer is the leading cause of cancer-related deaths around the world, making early detection vital to treatment and survival. However, lung cancer has variable clinical presentations, so diagnosis, even by trained medical professionals, is challenging and time- consuming. Deep Learning (DL) has proven effective in training big data to generate accurate diagnoses through classification in a timely manner. Aim and Objective: This study uses data augmentation and hyperparameter optimization methods on convolutional neural networks (CNN) to understand the benefits of various architectures in addition to creating an accurate transfer-based tool for lung cancer diagnosis on CT scans. Materials and Methods: We found a dataset composed of chest CT images of patients with non-small cell lung cancers (NSCLC) categorized into adenocarcinoma, large cell carcinoma, and squamous cell carcinoma and a control group of normal chest CT scans. We tested the CNN architectures VGG16, InceptionV3, ResNet50, and EfficientNetB0 for feature extraction and observed each model on a variety of different metrics such as validation accuracy, statistical errors, and learning rate. Each model was trained on the prechosen data set 2 times at 6 and 12 epochs and several evaluation metrics were recorded. Observation and Results: EfficientNetB0, ResNet50, VGG16, InceptionV3 achieved test accuracies of 98.92%, 91.40%, 77.42%, 70.97% respectively when implemented with hyperparameter tuning and dropout layers. EfficientNetB0 also exceeded the other architectures in metrics such as precision, recall, and F1-score. Conclusion: Convolutional neural networks using EfficientNetB0 yielded a higher accuracy in diagnosis of non-small cell cancer on CT scan which encourages further research in developing robust diagnostic tools using DL to expedite diagnosis, especially in locations with inadequate healthcare resources.

A large unilateral basal cell carcinoma treated with Hedgehog inhibitor sonidegib: a case report

Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. BCCs are categorized into “easy-to-treat” and “difficult-to-treat” groups, with the latter including all BCCs that are challenging to manage due to technical, patient-related, or tumor-related factors, such as locally advanced BCCs. In this report, we describe an 84-year-old patient with an extensive, unilateral BCC. Following a decision by the multidisciplinary skin cancer board, the patient was successfully treated with a daily dose of 200 mg of sonidegib, an inhibitor of the Hedgehog pathway, for eight months, acquiring complete clinical and histopathological remission. No significant side effects were reported. The follow-up period of 24 months has shown no negative results.

Reconstruction of 2 Independent Facial Defects Using A Single Paramedian Forehead Flap

Background: The paramedian forehead flap (PMFF) has been well established for use in reconstruction limited to a single nasal outer layer defect and has recently gained recognition as an acceptable alternative to traditional methods of lower eyelid reconstruction. The use of a single, pedicled PMFF for the reconstruction of more than one defect has yet to be described. Methods: A 59-year-old male patient was originally diagnosed with large squamous cell carcinoma resulting in radical resection and ipsilateral neck dissection. Postoperative final pathology was read as basal cell carcinoma; due to close margins, the patient required adjuvant radiation therapy. Immediate reconstruction at index surgery included radial forearm free flap; however, the patient subsequently developed left eye symptoms due to lid retraction and nasal symptoms with a left alar defect. The authors report the use of a single pedicled PMFF to reconstruct 2 distinct ipsilateral defects in a radiated patient—nasal ala and lower eyelid. Results: At 6 months post-secondary reconstruction, both recipient sites of the single PMFF appear viable and in adequate position without contraction. The patient has persistent but much improved symptoms related to his eye and nasal defects. Conclusion: A single PMFF can be safely and effectively used in the reconstruction of 2 distinct facial defects in the appropriate patient.

Effect of Smoking Duration and Pack-Year History on Histological Subtypes of Lung Cancer

Background: Lung cancer is the leading cause of cancer-related mortality worldwide, with smoking as the principal risk factor. The histological subtypes of lung cancer, including adenocarcinoma, squamous cell carcinoma, and small cell carcinoma, vary according to smoking history and intensity. This study investigates the effect of smoking duration and pack-year history on the histological subtypes of lung cancer in Bangladesh. Methods: A cross-sectional study was conducted at the National Institute of Diseases of the Chest and Hospital (NIDCH) from July 2007 to June 2008. A total of 98 patients with confirmed primary lung cancer were included, comprising 81 smokers and 17 non-smokers. Results: Among the 98 patients, 45% had adenocarcinoma, 28.6% squamous cell carcinoma, 16.3% small cell carcinoma, 6.1% large cell carcinoma, and 3.1% other subtypes. Smokers exhibited a higher incidence of squamous cell carcinoma (33.3%) and small cell carcinoma (19.8%), while adenocarcinoma was more common in non-smokers (88.2%). Majority (94.1%) of the patients with lesions in the upper lobe was smokers compared to 56.7% of patients with lesions in the lower lobe (p < 0.001) Ratio of upper and lower lobe tumour among smoker was roughly of 4:1. A significant association was found between smoking duration, pack-years, and the occurrence of squamous and small cell carcinoma (p < 0.001). Conclusion: Smoking duration and pack-years have a significant impact on the histological subtypes of lung cancer. Squamous cell carcinoma and small cell carcinoma are more prevalent in smokers, while adenocarcinoma is more common in non-smokers.

A Large Basal Cell Carcinoma Treated With Hedgehog Inhibitor: A Case Report

A Large Basal Cell Carcinoma Treated With Hedgehog Inhibitor: A Case Report

Non-small cell lung carcinoma. Clinical case

In 2020, 2.2 million new cases of lung cancer were reported. The main form of lung cancer is non-small cell carcinoma (85%). Non-small cell lung cancer has 3 subtypes: adenocarcinoma, large cell and squamous cell carcinoma. Squamous cell lung cancer occurs in 20-40% of all lung cancers. Squamous cell lung cancer is associated with an unfavorable outcome. 5-year survival rate not exceeding 18%.

P2.10B.06 Improvements in Survival Outcomes of Large Cell Carcinoma Over Past Two Decades (2000-2020)

P2.10B.06 Improvements in Survival Outcomes of Large Cell Carcinoma Over Past Two Decades (2000-2020)

B-356 Siemens CYFRA 21-1: an assay for measurement of cytokeratin fragment 21-1

Abstract Background Cytokeratins are structural proteins forming the subunits of epithelial intermediary filaments. The soluble fragment of cytokeratin 19, cytokeratin fragment 21-1 (CYFRA 21-1), can be found in the blood of lung cancer patients, primarily those with non-small cell lung cancer (NSCLC). CYFRA 21-1 is the preferred biomarker in the management of NSCLC, particularly squamous cell and large cell carcinoma subtypes. An assay for CYFRA 21-1 on Siemens ADVIA Centaur® XPT and Siemens Atellica® IM (Siemens CYFRA) is being developed and analytical performance is being presented. Methods The Siemens CYFRA 21-1 assay (in development) is a one-step chemiluminescent microparticle immunoassay (CMIA) for the quantitative determination of CYFRA 21-1 in human serum and plasma. The specimen is incubated with paramagnetic microparticles coated with the monoclonal antibody (mAb) BM19.21 and acridinium-labeled mAb KS19.1 conjugate on the Siemens ADVIA Centaur XPT System and Siemens Atellica IM System. After incubation and washing, Acid and Base reagents are added. The resulting relative light units (RLUs) are directly proportional to the amount of CYFRA 21-1 in the sample allowing for the quantitative determination of CYFRA 21-1 in the specimen. Results The following performance were determined using 3 unique lots of reagents and calibrators with the calibration range for the assay being 0.00 to 100.00 ng/mL. For the ADVIA Centaur XPT and Atellica IM systems were shown to be equivalent within 4%. The limit of blank (LoB), limit of detection (LoD), and limit of quantitation at 20% CV (LoQ) were determined and met the goal of ≤ 1.00 ng/mL. Linearity according to CLSI EP06 Ed2 was demonstrated for a range of 0.66 through 100.00 ng/mL. A Precision study of 2 controls and 5 panels spanning the range of the assay demonstrated a total %CV ≤ 8% at all levels. In the sample tube type study, a matrix comparison of 61 matched serum and plasma samples were evaluated. Passing-Bablock slopes of < 5% and r > 0.99 were observed when evaluating samples within the measurement range for plasma (LiHep, LiHep separator, K2EDTA and K3EDTA) tubes and serum separator tubes (SST) compared to the Red Top serum samples. Six endogenous substances were evaluated for interference in the CYFRA 21-1 assay. The average percent difference between test and control samples for all endogenous interferents was < 10%. Percent difference in the presence of 20 potentially interfering drugs was ≤ 4.0%. Conclusions The Siemens CYFRA 21-1 assay currently in development is a sensitive and precise assay for the quantitative determination of CYFRA 21-1 in human serum and plasma.

Large cell carcinoma of the lung: LDCT features and survival in screen-detected cases.

To investigate the early radiological features and survival of Large Cell Carcinoma (LCC) cases diagnosed in low-dose computed tomography (LDCT) screening trials. Two radiologists jointly reviewed the radiological features of screen-detected LCCs observed in NLST, ITALUNG, and LUSI trials between 2002 and 2016, comprising a total of 29,744 subjects who underwent 3-5 annual screening LDCT examinations. Survival or causes of death were established according to the mortality registries extending more than 12years since randomization. LCC was diagnosed in 30 (4%) of 750 subjects with screen-detected lung cancer (LC), including 15 prevalent and 15 incident cases. Three additional LCCs occurred as interval cancers during the screening period. LDCT images were available for 29 cases of screen-detected LCCs, and 28 showed a single, peripheral, and well-defined solid nodule or mass with regularly smooth (39%), lobulated (43%), or spiculated (18%) margins. One case presented as hilar mass. In 9 incident LCCs, smaller solid nodules were identified in prior LDCT examinations, allowing us to calculate a mean Volume Doubling Time (VDT) of 98.7±47.8 days. The overall five-year survival rate was 50%, with a significant (p=0.0001) difference between stages I-II (75% alive) and stages III-IV (10% alive). LCC is a fast-growing neoplasm that can escape detection by annual LDCT screening. LCC typically presents as a single solid peripheral nodule or mass, often with lobulated margins, and exhibits a short VDT. The 5-year survival reflects the stage at diagnosis.

Hybrid Approach of Multiclassification for Lung Cancer Types Identification

As a widespread health concern, lung cancer requires sophisticated detection techniques for a precise classification. This research proposes an integrated framework for improving how well lung cancer is detected by combining processing methods for images and machine learning. The dataset, which includes normal cases, large-cell carcinoma, squamous cell carcinoma, and adenocarcinoma, allows for a detailed investigation of various forms of lung cancer. K-means clustering is the initial stage of the approach of segmenting complex images. Utilizing the VGG16 model, features are extracted that are important for classification. Several classification models, such as SVM, Logistic Regression, and Feedforward Neural Network, are trained using the combined features from clustering and VGG16. This research goes beyond the dichotomy of benign and malignant cases, investigating in more detail the subtypes of large-cell carcinoma, squamous cell carcinoma, and adenocarcinoma. Including different classes of lung cancer increases the granularity of the classification process, improving diagnostic accuracy and clinical insights. The results highlight how well the suggested strategy works, which is a major advancement in the accurate identification of distinct kinds of lung cancer.

The study of plain CT combined with contrast-enhanced CT-based models in predicting malignancy of solitary solid pulmonary nodules

To compare the diagnostic performance between plain CT-based model and plain plus contrast CT-based modelin the classification of malignancy for solitary solid pulmonary nodules. Between January 2012 and July 2021, 527 patients with pathologically confirmed solitary solid pulmonary nodules were collected at dual centers with similar CT examinations and scanning parameters. Before surgery, all patients underwent both plain and contrast-enhanced chest CT scans. Two clinical characteristics, fifteen plain CT characteristics, and four enhanced characteristics were used to develop two logistic regression models: model 1 (plain CT only) and model 2 (plain + contrast CT). The diagnostic performance of the two models was assessed separately in the development and external validation cohorts using the AUC. 392 patients from Center A were included in the training cohort (median size, 20.0 [IQR, 15.0–24.0] mm; mean age, 55.8 [SD, 9.9] years; male, 53.3%). 135 patients from Center B were included in the external validation cohort (median size, 20.0 [IQR, 16.0–24.0] mm; mean age, 56.4 [SD, 9.6] years; male, 51.9%). Preoperative patients with 201 malignant (adenocarcinoma, 148 [73.6%]; squamous cell carcinoma, 35 [17.4%]; large cell carcinoma,18 [9.0%]) and 326 benign (pulmonary hamartoma, 118 [36.2%]; sclerosing pneumocytoma, 35 [10.7%]; tuberculosis, 104 [31.9%]; inflammatory pseudonodule, 69 [21.2%]) solitary solid pulmonary nodules were gathered from two independent centers. The mean sensitivity, specificity, accuracy, PPV, NPV, and AUC (95%CI) of model 1 (Plain CT only) were 0.79, 0.78, 0.79, 0.67, 0.87, and 0.88 (95%CI, 0.82–0.93), the model 2 (Plain + Contrast CT) were 0.88, 0.91, 0.90, 0.84, 0.93, 0.93 (95%CI, 0.88–0.98) in external validation cohort, respectively. A logistic regression model based on plain and contrast-enhanced CT characteristics showed exceptional performance in the evaluation of malignancy for solitary solid lung nodules. Utilizing this contrast-enhanced CT model would provide recommendations concerning follow-up or surgical intervention for preoperative patients presenting with solid lung nodules.

Patient-derived organoids and mini-PDX for predicting METN375S-mutated lung cancer patient clinical therapeutic response

Lung cancer as a molecularly and histologically high heterogonous disease, there is an urgent need to predict lung cancer patients' responses to anti-cancer treatment, and patient-derived organoids (PDOs) have been recognized as a valuable platform for preclinical drug screening. In this study, we successfully established 26 PDO lines from various subtypes of lung cancers including benign tumor, adenocarcinoma, squamous cell carcinoma, adenosquamous carcinoma, large-cell carcinoma, and small-cell carcinoma. These PDOs were shown to retain the major genomic and histological characteristics of primary tumors and remain stable during long-term culture. With the help of targeted genomic sequencing, we found that lung cancer that harbors METN375S mutation is selectively sensitive to afatinib, and a combination of afatinib and gemcitabine induced synthetic lethality in PDO and mini-PDX models. In summary, our findings demonstrate the potential of PDO in predicting lung cancer drug response, and reveal a promising strategy for METN375S mutant lung cancer treatment.

318. EFFICACY OF PROPHYLACTIC STEROID THERAPY OF INJECTION AND ADMINISTRATION AGAINST STRICTURE FOR SUBENTIRE CIRCUMFERENTIAL DEFECT AFTER ESOPHAGEAL ENDOSCOPIC SUBMUCOSAL DISSECTION

Abstract Background Esophageal stricture is a major problem after endoscopic submucosal dissection (ESD) for large superficial esophageal squamous cell carcinomas (SESCC). Local triamcinolone injection (LTI) has been reported as the standard prophylactic treatment against stricture, however, it showed that its efficacy was insufficient for subentire circumferential mucosal defect after ESD in the subgroup analysis of randomized controlled trial (JCOG1217). On the other hand, the usefulness of oral prednisolone administration (OPA) has also been reported. Thus, we aimed to compare the efficacy of LTI with combination of LTI and OPA in patients with subentire circumferential mucosal defect after esophageal ESD. Methods This retrospective study enrolled the patients with mucosal defect after ESD larger than 7/8th circumference and not spreading entire who treated with LTI or combination of LTI and OPA between July 2010 to January 2020. Triamcinolone acetonide (50-100 mg) was injected into ulcer bed immediately after ESD, and oral prednisolone (30 mg daily, tapered gradually for 8 weeks) was administrated. We defined as an esophageal stricture in case the endoscope could not pass through the stricture, and then endoscopic balloon dilation (EBD) was required. We evaluated stricture-free survival (SFS), and the number and duration of EBD. Results A total of 42 patients in the LTI group and 26 in the combination group were enrolled. The median longitudinal extension of the lesions was 4.5 cm (range; 2-7) in the LTI group and 4 cm (2-7) in the combination group. Finally, 18 patients (42.9%) in the LTI group and 7 (26.9%) in the combination group developed esophageal stricture, and 12-week SFS was 64.3% and 76.9%. The median number and duration of required ESD were 6 (2-10) in the LTI group and 4 (2-9) in the combination group, and 88 days (15-288) and 66 days (22-176). Conclusion The steroid combination therapy of local triamcinolone injection and oral prednisolone administration showed the effective outcomes comparing with local triamcinolone injection alone for subentire circumferential mucosal defect after esophageal ESD. Thus, this combination therapy can be the most promising treatment and should be compared in the randomized controlled trail with large sample size.

The relationship between lobar distribution and primary lung cancer histological types

Background: Lung cancer is the primary cause of cancer-related deaths globally. Accurate tumor location and subtype characterization are critical for optimizing treatment and predicting outcomes. This study aims to identify the relationship between lobar distribution and primary lung cancer histological types and describe the most recent patterns at the country-level origin. Methods: This cross-sectional study was conducted at a major tertiary care hospital in Bangladesh, including 165 patients recruited using purposive non-probability sampling. Data were collected using a semi-structured case record form and analyzed using SPSS version 28.0. Results: Tumors were most commonly found in the central area (58.2%). Squamous cell carcinoma was the most prevalent type (46.1%), followed by adenocarcinoma (38.8%), small cell carcinoma (13.9%), and large cell carcinoma (1.2%). The incidence of squamous cell carcinoma was significantly higher in the upper lobe of the right lung (55.9%) compared to other forms, particularly in the central region of the right lung (P < 0.001). Squamous cell carcinoma was more common in the lower part of the left lung (42.9%), while adenocarcinoma was exclusively observed in the lingular portion of the left lung (100%). The major bronchus of the left lung primarily exhibited squamous cell carcinoma (60%), but this finding was not statistically significant (p=0.899). Conclusion: The study highlights the significant association between lobar distribution and histological types of primary lung cancer, emphasizing the need for precise tumor characterization for better treatment strategies and prognostic evaluation.

Machine learning enabled classification of lung cancer cell lines co-cultured with fibroblasts with lightweight convolutional neural network for initial diagnosis

BackgroundIdentification of lung cancer subtypes is critical for successful treatment in patients, especially those in advanced stages. Many advanced and personal treatments require knowledge of specific mutations, as well as up- and down-regulations of genes, for effective targeting of the cancer cells. While many studies focus on individual cell structures and delve deeper into gene sequencing, the present study proposes a machine learning method for lung cancer classification based on low-magnification cancer outgrowth patterns in a 2D co-culture environment.MethodsUsing a magnetic well plate holder, circular pattern lung cancer cell clusters were generated among fibroblasts, and daily images were captured to monitor cancer outgrowth over a 9-day period. These outgrowth images were then augmented and used to train a convolutional neural network (CNN) model based on the lightweight TinyVGG architecture. The model was trained with pairs of classes representing three subtypes of NSCLC: A549 (adenocarcinoma), H520 (squamous cell carcinoma), and H460 (large cell carcinoma). The objective was to assess whether this lightweight machine learning model could accurately classify the three lung cancer cell lines at different stages of cancer outgrowth. Additionally, cancer outgrowth images of two patient-derived lung cancer cells, one with the KRAS oncogene and the other with the EGFR oncogene, were captured and classified using the CNN model. This demonstration aimed to investigate the translational potential of machine learning-enabled lung cancer classification.ResultsThe lightweight CNN model achieved over 93% classification accuracy at 1 day of outgrowth among A549, H460, and H520, and reached 100% classification accuracy at 7 days of outgrowth. Additionally, the model achieved 100% classification accuracy at 4 days for patient-derived lung cancer cells. Although these cells are classified as Adenocarcinoma, their outgrowth patterns vary depending on their oncogene expressions (KRAS or EGFR).ConclusionsThese results demonstrate that the lightweight CNN architecture, operating locally on a laptop without network or cloud connectivity, can effectively create a machine learning-enabled model capable of accurately classifying lung cancer cell subtypes, including those derived from patients, based upon their outgrowth patterns in the presence of surrounding fibroblasts. This advancement underscores the potential of machine learning to enhance early lung cancer subtyping, offering promising avenues for improving treatment outcomes in advanced stage-patients.

Effectiveness of Apparent Diffusion Coefficient Values in Predicting Pathologic Subtypes and Grade in Non-Small-Cell Lung Cancer.

The aim of this study is to evaluate the effectiveness of apparent diffusion coefficient (ADC) values in predicting pathologic subtypes and grade in non-small-cell lung cancer (NSCLC). From January 2018 to March 2020, 48 surgically diagnosed NSCLC cases were included in this study. To obtain ADC values, ADC maps were constructed, and a region of interest was put on the tumor. The values were measured three times from different places of the lesion, and the mean value of these measurements was recorded. All MRI scans were evaluated by two radiologists in consensus. A total of 14 cases were squamous cell cancer, 32 cases were adenocarcinoma, and 2 cases were large cell carcinoma. The mean ADC values of adenocarcinoma, squamous cell carcinoma, and large cell cancer were 1.51 ± 0.19 × 10-3 mm2/s, 1.32 ± 0.15 × 10-3 mm2/s, and 1.39 ± 0.25 × 10-3 mm2/s, respectively. There were 11 grade 1, 27 grade 2, and 10 grade 3 NSCLC cases. The mean ADC value was 1.44 ± 0.14 × 10-3 mm2/s in grade 1 tumors, 1.25 ± 0.10 × 10-3 mm2/s in grade 2 tumors, and 1.07 ± 0.15 × 10-3 mm2/s in grade 3 tumors. The cut-off value to discriminate grade 2 from grade 1 tumors was 1.31 ± 0.11 × 10-3 mm2/s (85% sensitivity, 75% specificity). The cut-off value to discriminate grade 3 from grade 2 tumors was 1.11 ± 0.15 × 10-3 mm2/s (87% sensitivity, 69% specificity). ADC values can accurately predict NSCLC histopathologic subtypes and tumor grade.

An “Older Old” Woman with Large Squamous Cell Carcinoma of the Nasal Pyramid: Excellent Response to Ultra-Hypofractionated Radiation Therapy

A 98-year-old patient with cognitive impairment and a history of squamous cell carcinoma of the nasal pyramid was referred to the radiation oncology department of our institution’s hospital given that surgery was not recommended. The lesion was sized 6 × 6 cm, ulcerated, and bleeding; was significantly impairing the patient’s health-related quality of life, causing pain; and was not responsive to analgesics, including opioids. The patient experienced deterioration of her general conditions, with a Karnofsky performance status of 40. A single radiotherapy (RT) fraction was delivered on a weekly basis for 3 weeks, up to a total dose of 21 Gy, using a VMAT technique (7 Gy/fraction). The patient was given three fractions of radiotherapy, during which she received continuous assistance due to episodes of mental disorientation and an altered sense of consciousness. One month after the conclusion of the treatment, the patient exhibited a nearly complete clinical response, with full pain relief and an improved health-related quality of life. This favourable clinical outcome was maintained for a period of four months following the conclusion of RT. A brief review was performed on the role of hypofractionated radiation therapy in elderly patients with locally advanced skin cancer of the head and neck region.

Clinical Presentation and Treatment Strategies for Laryngeal Neuroendocrine Carcinoma: Insights from a Rare Case

Intriduction -Neuroendocrine neoplasms (NEN) of the larynx are very rare, accounting for <1% of all laryngeal carcinomas. Based on the 2022 IARCH/WHO classification, NENs are characterised as well-differentiated neuroendocrine tumours (NET), which can further be divided into grades 1-3 based on Ki-67 levels and mitotic count, or poorly differentiated neuroendocrine carcinomas (NEC) consisting of small cell carcinoma and large cell carcinoma. In this case report, we describe a rare presentation of NEC of the larynx encountered in a 54-year-old man and how diagnostic and therapeutic dilemmas unfolded. Case description: A 6-month history of breathing and swallowing difficulties led to a tracheostomy due to stridor, in the emergency department. Imaging revealed a soft tissue mass involving the supraglottic larynx but no significant associated cervical lymphadenopathy. The biopsy from the growth was suggestive of poorly differentiated NEC as an AJCC stage cT3N0M0. However, there was local progression post-initial treatment with vincristine, doxorubicin and cyclophosphamide (VAC). The chemotherapy regimen was switched to carboplatin and etoposide. The patient received definitive radiation therapy and completed the planned dose of radiation.After 10 months of disease-free interval, there was local recurrence with lung nodules that prompted rechallenge with platinum and etoposide. The patient is doing well and remains clinically and radiologically stable to maintenance therapy with carboplatin & etoposide. Conclusion: NENs of the larynx and especially NECs, are difficult to diagnose as they are rare and have varied molecular profiles. Its management frequently calls for multimodal approaches with chemotherapy and radiation therapy. Due to the developments in diagnostic and therapeutic modalities, our knowledge of NENs has been enhanced which contributed to better practice standards. Nevertheless, standardized treatment strategies remain to be determined by large cohort studies and comprehensive molecular profiling that can determine improved clinical outcomes.

3D Culture Analysis of Cancer Cell Adherence to Ex Vivo Lung Microexplants.

Ex vivo 3D culture of human tissue explants addresses many limitations of traditional monolayer cell culture techniques, namely the lack of cellular heterogeneity and absence of 3D intercellular spatial relationships, but presents challenges with regard to repeatability owing to the difficulty of acquiring multiple tissue samples from the same donor. In this study, we used a cryopreserved bank of human lung microexplants, ∼1 mm3 fragments of peripheral lung from donors undergoing lung resection surgery, and a liquid-like solid 3D culture matrix to describe a method for the analysis of non-small-cell lung cancer adhesion to human lung tissue. H226 (squamous cell carcinoma), H441 (lung adenocarcinoma), and H460 (large cell carcinoma) cell lines were cocultured with lung microexplants. Confocal fluorescence microscopy was used to visualize the adherence of each cell line to lung microexplants. Adherent cancer cells were quantified following filtration of nonadherent cells, digestion of cultured microexplants, and flow cytometry. This method was used to evaluate the role of integrins in cancer cell adherence. A statistically significant decrease in the adherence of H460 cells to lung microexplants was observed when anti-integrins were administered to H460 cells before coculture with lung microexplants.

In Vitro Anticancer Activity of Amphiroa Fragilissima on Lung Cancer Cell Line (A549)

The primary cause of cancer-related deaths worldwide is lung cancer. Still smoking is the biggest risk factor. Adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and other types of lung cancer are examples of non-small cell carcinomas. The prognosis and available treatments are determined using these classifications. This study illustrates the application of Amphiroa fragilissima in cancer therapy. A. fragilissima inhibits the growth of the lung cancer cell line A549. It is remarkable that A. fragilissima has a more cytotoxic effect on the lung cancer cell line A549, although other cell lines are needed to confirm this conclusion. To fully comprehend how anti-apoptotic agents stimulate the production of the apoptotic pathway proteins, more investigation is required gene deprivation.