Role of CT in differentiation between subtypes of lung cancer; is it possible?

Pleural small cell lung cancer masquerading as malignant mesothelioma: A case report

Nodular soft tissue pleural thickening on imaging is highly suggestive of malignancy, of which pleural malignant mesothelioma and metastatic disease are differentials. We present the case of a 71-year-old male who presented with acute worsening of shortness of breath associated with a recurrent left pleural effusion post-pleurocentesis. He was an ex-smoker with previous asbestos exposure. Computed tomography performed demonstrated left-sided pleural thickening in the hemithorax and hemidiaphragm with complex pleural effusion. 18F-2-deoxy-d-glucose whole body PET scan revealed extensive uptake throughout the left hemithorax in multiple pleural masses. The imaging findings and clinical case were typical of malignant mesothelioma. However, histopathology results revealed small cell lung cancer. We need to be cognisant of this atypical presentation of a common disease entity. Even when all clinical and imaging findings point towards a certain diagnosis, histopathological assessment cannot be ignored.

CT Imaging Patterns in Major Histological Types of Lung Cancer

Lung cancer ranks as the second most prevalent cancer globally and is the primary contributor to neoplastic-related deaths. The approach to its treatment relies on both tumour staging and histological type determination. Data indicate that the prognosis of lung cancer is strongly linked to its clinical stage, underscoring the importance of early diagnosis in enhancing patient outcomes. Consequently, the choice of an appropriate diagnostic method holds significant importance in elevating both the early detection rate and prognosis of lung cancer. This paper aims to assess computer tomography features specific to the most common lung cancer types (adenocarcinoma, squamous cell carcinomas and small cell lung cancer). Data were collected retrospectively from CT scans of 58 patients pathologically diagnosed with lung cancer. The following CT features were evaluated and recorded for each case: location, margins, structure, lymph node involvement, cavitation, vascular bundle-thickening, bronchial obstruction, and pleural involvement. Squamous cell carcinoma (SQCC) and small cell lung cancer (SCLC) showed a higher incidence of central location, while adenocarcinoma (ADC) showed a significant predilection for a peripheral location. Internal cavitation was mostly observed in SQCC, and a solid structure was observed in almost all cases of ADC. These features can provide information about the prognosis of the patient, considering that NSCLCs are more frequent but tend to demonstrate positive results for targetable driver mutations, such as EGFR, thereby increasing the overall survival. In addition, SCLC presents with early distant spreads, which limits the opportunity to investigate the evolution of tumorigenesis and gene alterations at early stages but can have a rapidly positively response to chemotherapy. The location of the lung cancer exhibits distinct forecasts, with several studies suggesting that peripheral lung tumours offer a more favourable prognosis. Cavity formation appears correlate with a poorer prognosis. Histopathological analysis is the gold standard for diagnosing the type of lung cancer; however, using CT scanning for the purpose of a rough, but fast, preliminary diagnosis has the potential to shorten the waiting time for treatment by helping clinicians and patients to know more about the diagnosis and prognosis.

Diagnostic value of FDG PET-CT in differentiating lung adenocarcinoma from squamous cell carcinoma

BACKGROUND

Lung cancer is the leading cause of cancer-related deaths worldwide. The combination of fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG PET) and computed tomography (CT) has a major impact on the diagnosis, staging, treatment planning and follow-up of lung cancer patients. The maximum standardized uptake value (SUVmax) is an easily performed and most widely used semi-quantitative index for the analysis of FDG PET images and estimation of metabolic activity. This study aimed to investigate the role of PET/CT in differentiating adenocarcinoma (ADC), the most common lung cancer, from squamous cell carcinoma (SCC) by comparing FDG uptake measured as SUVmax.

RESULTS

Between 2019 and 2022, 76 patients diagnosed with non-small cell lung cancer (NSCLC) at the Department of Pathology, Atatürk University Faculty of Medicine, with histopathologic evidence of adenocarcinoma or squamous cell carcinoma, underwent retrospective analysis using PET/CT scanning to measure PET parameters of the lesions and compare them with histopathology. Among 76 NSCLC patients included in the study, 43 (57%) were histopathologically diagnosed as ADC and 33 (43%) as SCC. SUVmax, SUVmean, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) values of lesions in patients with SCC were statistically significantly higher than those in patients with ADC (p values 0.007, 0.009, 0.003 and 0.04, respectively).

CONCLUSIONS

Lung SCC has higher metabolic uptake values than ADC, and PET/CT can be used to differentiate them.

Clinical and Paraclinical Characteristics of Endobronchial Pulmonary Squamous Cell Carcinoma-A Brief Review

BACKGROUND

Endobronchial squamous cell carcinoma is one of the most common types of tumors located inside the tracheobronchial tree. Patients often present in advanced stages of the disease, which most often leads to a targeted therapeutic attitude of pneumonectomy. Practicing lung parenchyma-preserving surgery led us to undertake this review.

MATERIALS AND METHODS

We used three search platforms-SCIENCE, MEDLINE, and PubMed-in order to identify studies presenting case reports, investigations, and reviews on endobronchial squamous cell carcinoma. We identified the clinical and paraclinical features of endobronchial squamous cell carcinoma. All the selected articles were in English and addressed the clinical criteria of endobronchial squamous cell carcinoma, autofluorescence bronchoscopy in endobronchial squamous cell carcinoma, imaging features of endobronchial squamous cell carcinoma, blood tumor markers specific to lung squamous cell carcinoma, and histopathological features of endobronchial squamous cell carcinoma.

RESULTS

In total, 73 articles were analyzed, from which 48 articles were selected as bibliographic references. We present the criteria used for the identification of endobronchial squamous cell carcinoma in order to highlight its main characteristics and the most reliable technologies that can be used for the detection of this type of cancer.

CONCLUSIONS

The current literature review highlights the clinical and paraclinical characteristics of endobronchial squamous cell carcinoma. It aims to open new paths for research and early detection with respect to the frequent practice of lung parenchymal preservation surgery.

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

Purpose

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.

Methods

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.

Results

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).

Conclusions

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.

Multiple intracranial metastasis from lung adenocarcinoma in a pregnant young woman: A case report

Lung cancer remains one of the leading causes of cancer-related deaths in both men and women worldwide. Although its occurrence during pregnancy is rare, it is fast becoming an emerging issue globally. Lung cancer is exceedingly rare in young individuals but is distinct, with adenocarcinoma and stage IV presentation being the most common features. This study presents the case of a 30-year-old woman who came to the emergency department with headache, loss of sensation in the left side of the body, progressing diplopia, and diabetes insipidus that first appeared when the patient was 6-month pregnant. Clinical examination showed right cranial nerve III paresis, bitemporal hemianopsia, and left hemiparesis, while MRI indicated multiple intracranial metastases proven by pathology anatomy. This study highlights the role of imaging in assessing lung adenocarcinoma with intracranial metastasis in a young pregnant woman.

Pleural metastasis of pulmonary adenocarcinoma mimicking diffuse mesothelioma: A case report and literature study

Conditions on pleura cover a broad range of pathology ranging from benign to malignant, which may potentially carry a poor prognosis and lead to high morbidity and mortality. Radiology plays a pivotal role in the diagnosis of pleural malignancy; however, the diagnostic endeavor can be challenging because of overlapping radiological appearances of one condition to another. This case report presents a 61-year-old male with worsening chronic shortness of breath. Despite early imaging resulting in highly suggested mesothelioma, subsequent biopsy proved the malignancy to be pulmonary adenocarcinoma. The patient underwent Pemetrexed-Cisplatin protocol in accordance with the biopsy result, and follow-up imaging depicted a marked improvement of the pleural condition. This case is a prime example of the challenge radiologists have to face regarding pleural tumors and dictates the necessity of a specialized multidisciplinary team to improve the patient's outcome.

DETECT-LC: A 3D Deep Learning and Textural Radiomics Computational Model for Lung Cancer Staging and Tumor Phenotyping Based on Computed Tomography Volumes

Lung Cancer is one of the primary causes of cancer-related deaths worldwide. Timely diagnosis and precise staging are pivotal for treatment planning, and thus can lead to increased survival rates. The application of advanced machine learning techniques helps in effective diagnosis and staging. In this study, a multistage neurobased computational model is proposed, DETECT-LC learning. DETECT-LC handles the challenge of choosing discriminative CT slices for constructing 3D volumes, using Haralick, histogram-based radiomics, and unsupervised clustering. ALT-CNN-DENSE Net architecture is introduced as part of DETECT-LC for voxel-based classification. DETECT-LC offers an automatic threshold-based segmentation approach instead of the manual procedure, to help mitigate this burden for radiologists and clinicians. Also, DETECT-LC presents a slice selection approach and a newly proposed relatively light weight 3D CNN architecture to improve existing studies performance. The proposed pipeline is employed for tumor phenotyping and staging. DETECT-LC performance is assessed through a range of experiments, in which DETECT-LC attains outstanding performance surpassing its counterparts in terms of accuracy, sensitivity, F1-score and Area under Curve (AuC). For histopathology classification, DETECT-LC average performance achieved an improvement of 20% in overall accuracy, 0.19 in sensitivity, 0.16 in F1-Score and 0.16 in AuC over the state of the art. A similar enhancement is reached for staging, where higher overall accuracy, sensitivity and F1-score are attained with differences of 8%, 0.08 and 0.14.