Lung Adenocarcinoma Presenting as a Soft Tissue Metastasis to the Shoulder: A Case Report

Atypical pneumonia (Review)

Atypical pneumonia encompasses diverse pathogens, such as Chlamydia pneumoniae, Mycoplasma pneumoniae and Legionella species, which differ from typical bacterial pneumonia in their extrapulmonary manifestations. Clinical differentiation relies on systemic involvement rather than on standalone symptoms. Despite challenges in distinct diagnosis, syndromic approaches and weighted point systems aid in accurate presumptive diagnoses. Antibiotic treatment, often non-β-lactams due to the unique cell structures of atypical pathogens, targets intracellular processes. Macrolides, tetracyclines, quinolones and ketolides are effective due to their intracellular penetration, crucial for combating these intracellular pathogens. The prevalence of atypical pneumonia varies globally, with Europe, Asia/Africa and Latin America reporting detection rates between 20-28%. Streptococcus pneumoniae remains a primary cause of pneumonia; however, atypical pathogens contribute significantly to this disease, being more prevalent in outpatient settings and among young adults. Legionella stands out in severe hospitalized cases and is associated with higher mortality rates. Diagnosis proves challenging due to overlapping symptoms with other respiratory infections. Differentiation among pathogens, such as Chlamydia pneumoniae, Mycoplasma pneumoniae and Legionella relies on subtle clinical variations and imaging findings. Diagnostic methods include serological studies, cultures and polymerase chain reaction, each with limitations in sensitivity or specificity. Prognosis varies widely. Atypical pneumonia can progress to severe forms with fatal outcomes, causing multi-organ damage. Complications extend beyond the respiratory system, affecting the cardiovascular system, exacerbating conditions such as chronic obstructive pulmonary disease and asthma, and potentially linking to conditions such as lung cancer. Increasing antibiotic resistance poses a significant challenge, influencing treatment outcomes and prolonging illness duration.

Comparative Analysis of Deep Learning Methods on CT Images for Lung Cancer Specification

BACKGROUND

Lung cancer is the leading cause of cancer-related deaths worldwide, ranking first in men and second in women. Due to its aggressive nature, early detection and accurate localization of tumors are crucial for improving patient outcomes. This study aims to apply advanced deep learning techniques to identify lung cancer in its early stages using CT scan images.

METHODS

Pre-trained convolutional neural networks (CNNs), including MobileNetV2, ResNet152V2, InceptionResNetV2, Xception, VGG-19, and InceptionV3, were used for lung cancer detection. Once the disease was identified, the tumor's region was segmented using models such as UNet, SegNet, and InceptionUNet.

RESULTS

The InceptionResNetV2 model achieved the highest detection accuracy of 98.5%, while UNet produced the best segmentation results, with a Jaccard index of 95.3%.

CONCLUSIONS

The study demonstrates the effectiveness of deep learning models, particularly InceptionResNetV2 and UNet, in both detecting and segmenting lung cancer, showing significant potential for aiding early diagnosis and treatment. Future work could focus on refining these models and exploring their application in other medical domains.

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.

Right Thigh Mass Metastasis from Lung Cancer Mimicking Primary Soft Tissue Sarcoma: A Case Report

BACKGROUND Soft tissue metastases (STMs) are less common than bone metastases and sometimes misdiagnosed as primary soft tissue malignancies. Skin, lungs, and breast are the most common primary lesions of STMs and rarely the presenting symptoms. We present an STM from lung adenocarcinoma that became a presenting symptom in nonsmoking woman. CASE REPORT A 47-year-old woman presented to our hospital with a painful mass in her right thigh and weight loss of 10 kg for 4 months. Femoral radiograph revealed a lesion suggestive of bone sarcoma. However, magnetic resonance imaging (MRI) showed it was more likely a primary soft tissue sarcoma. A small mediastinal mass was noticed on preoperative chest radiograph, and the patient denied any symptoms except the mass in the right thigh. Our clinicopathological conference team decided to perform a biopsy of mediastinal and right thigh masses. Histopathology examinations confirmed the right thigh mass as soft tissue metastasis from mediastinal mass, confirmed as lung adenocarcinoma. We treated the patient with palliative care with zoledronic acid and gefitinib. At the 6-month follow-up, the patient's symptoms significantly improved, and MRI showed a marked size reduction. CONCLUSIONS Diagnosis of STM can be difficult when presenting as the primary manifestation. Failure to identify promptly can lead to rapid disease progression and unfavorable prognosis. Failure to diagnose primary malignancy during biopsy occurs in approximately 28% of cases. This report has the potential to facilitate the avoidance of unnecessary procedures and highlight the importance of using a multidisciplinary approach in managing cases with malignancy.

Undifferentiated soft-tissue sarcoma (STS) in a 34-year-old woman: A case report

INTRODUCTION AND IMPORTANCE: soft tissue sarcoma (STS) is a type of cancer that can affect connective tissue. Diagnosis of this malignant tumor is difficult, and complications are related to the pressure it can exert on surrounding body organs. Up to 50 % of STS patients develop metastatic disease, which greatly affects the prognosis and is challenging for the treating physician.

CASE PRESENTATION

This case report is about a 34-year-old woman who was found to have significant growth of malignant tumor in her lower back due to misdiagnosis and negligence about her disease. After the cancer invaded the abdominal cavity, she died from related complications.

CLINICAL DISCUSSION

STS are among the rare malignant tumors, and the mortality rate of these cancers is very high because they are often not properly diagnosed.

CONCLUSION

Educating medical personnel, especially primary care physicians, about the symptoms and manifestations of STS can make a significant contribution to successful treatment. Due to the complexity of treatment, any soft-tissue swelling suspected of malignancy is best referred directly to a sarcoma center, where therapeutic management is carefully planned by an experienced multidisciplinary team.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning

PURPOSE

This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs).

METHODS

Data from 841 patients with SPSNs from five centres were collected retrospectively. First, adaptive cross-domain transfer learning was used to construct transfer learning signatures (TLS) under different source domain data and conduct a comparative analysis. The Wasserstein distance was used to assess the similarity between the source domain and target domain data in cross-domain transfer learning. Second, a cross-domain transfer learning radiomics model (TLRM) combining the best performing TLS, clinical factors and subjective CT findings was constructed. Finally, the performance of the model was validated through multicentre validation cohorts.

RESULTS

Relative to other source domain data, TLS based on lung whole slide images as source domain data (TLS-LW) had the best performance in all validation cohorts (AUC range: 0.8228-0.8984). Meanwhile, the Wasserstein distance of TLS-LW was 1.7108, which was minimal. Finally, TLS-LW, age, spiculated sign and lobulated shape were used to build the TLRM. In all validation cohorts, The AUC ranges were 0.9074-0.9442. Compared with other models, decision curve analysis and integrated discrimination improvement showed that TLRM had better performance.

CONCLUSIONS

The TLRM could assist physicians in preoperatively differentiating LGN from LAC in SPSNs. Furthermore, compared with other images, cross-domain transfer learning can extract robust image features when using lung whole slide images as source domain data and has a better effect.

Lung squamous cell carcinoma presenting as rare clustered cystic lesions: A case report and review of literature

BACKGROUND

Lung cancer is the leading cause of cancer-related death. Early diagnosis is critical to improving a patient’s chance of survival. However, lung cancer associated with cystic airspaces is often misdiagnosed or underdiagnosed due to the absence of clinical symptoms, poor imaging specificity, and high risk of biopsy-related complications.

CASE SUMMARY

We report an unusual case of cancer in a 55-year-old man, in which the lesion evolved from a small solitary thin-walled cyst to lung squamous cell carcinoma (SCC) with metastases in both lungs. The SCC manifested as rare clustered cystic lesions, detected on chest computed tomography. There were air-fluid levels, compartments, and bronchial arteries in the cystic lesions. Additionally, there was no clear extrathoracic metastasis. After chemotherapy, the patient achieved a partial response, type I respiratory failure was relieved, and the lung lesions became a clustered thin-walled cyst.

CONCLUSION

Pulmonary cystic lesions require regular imaging follow-up. Lung SCC should be a diagnostic consideration in cases of thin-walled cysts as well as multiple clustered cystic lesions.

Case Report: Lung Adenocarcinoma Initially Presenting With Cutaneous and Subcutaneous Metastases

Cutaneous and subcutaneous soft tissue metastases are rare in lung adenocarcinoma and suggest poor prognosis. We report a patient with lung adenocarcinoma who initially presented with cutaneous and subcutaneous metastases to the abdomen that were initially presumed to be herpes zoster and an occult subcutaneous soft tissue mass. Because the lesions progressed over 3 weeks despite routine herpes zoster treatment, magnetic resonance imaging was performed and showed a presumed sarcoma; however, ¹⁸F-fluourodeoxyglucose positron emission tomography/computed tomography demonstrated pulmonary lesions. Biopsy of the abdominal lesion confirmed poorly differentiated lung adenocarcinoma. Early diagnosis of soft tissue metastasis can be difficult. Clinicians should suspect internal organ malignancy when a progressive cutaneous or subcutaneous soft tissue lesion is encountered.

Limitations and usefulness of biopsy techniques for the diagnosis of metastatic bone and soft tissue tumors

Background

Biopsies are widely used for diagnosing metastatic tumors in the bone and soft tissues; however, their usefulness and limitations remain unclear.

Patients and methods

Biopsies of patients (13 men, 8 women, mean age 76 years) with metastatic tumors in the bone (19 patients) and soft tissues (2 patients) were reviewed retrospectively. Investigators surveyed the lesion sites, medical histories, Eastern Cooperative Oncology Group (ECOG) Performance Status (PS), biopsy sites, methods, comorbidities, diagnoses, treatments, and outcomes.

Results

Five patients had multiple lesions, and 16 patients had one lesion. The ECOG PS scores were PS0 (11 patients), PS1 (7 patients), PS2 (2 patients), and PS3 (1 patient). Biopsy sites included pelvic bone (6 cases), rib bone (5 cases), spinal vertebra (7 cases), soft tissue of the shoulder (2 cases), and inner retroperitoneum (1 case). Diagnostic methods included open biopsy (8 patients), core needle biopsy under general (7 patients) or local (3 patients) anesthesia, and computed tomography-guided core needle biopsy under local anesthesia (3 patients). Histology indicated hematological malignancies (9 cases); breast cancer (3 patients); lung cancer, renal cell cancer, cancer of unknown primary (2 cases each); prostate cancer, endometrial (uterine) cancer, and myxoid liposarcoma (1 case each). The primary site identification rate was 90.5%. Outcomes included three patients "dead of disease."

Conclusion

Biopsies are useful for early diagnosis and for the scrutiny of primary lesions of metastatic bone and soft tissue tumors. If the primary tumor is still unknown after biopsy, evidence-based treatment should be initiated promptly.