No abstract available.
Central Nervous System* ; Solitary Fibrous Tumors*

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

BACKGROUND: Naked cuticle Drosophila 1 (NKD1) has been related to non-small cell lung cancer in that decreased NKD1 levels have been associated with both poor prognosis and increased invasive quality. METHODS: Forty cases of lung adenocarcinoma staged as Tis or T1a were selected. Cases were subclassified into adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and small adenocarcinoma (SAD). Immunohistochemical studies for NKD1 were performed. RESULTS: Forty samples comprised five cases of AIS (12.5%), eight of MIA (20.0%), and 27 of SAD (67.5%). AIS and MIA showed no lymph node metastasis and 100% disease-free survival, whereas among 27 patients with SAD, 2 (7.4%) had lymph node metastasis, and 3 (11.1%) died from the disease. Among the 40 cases, NKD1-reduced expression was detected in 8 (20%) samples, whereas normal expression was found in 15 (37.5%) and overexpression in 17 (42.5%). Loss of NKD1 expression was significantly associated with lymph node metastasis (p=0.001). All cases with predominant papillary pattern showed overexpression of NKD1 (p=0.026). CONCLUSIONS: Among MIA and SAD, MIA had better outcomes than SAD. Down-regulated NKD1 expression was closely associated with nodal metastasis, and overexpression was associated with papillary predominant adenocarcinoma.
Adenocarcinoma ; Carcinoma, Non-Small-Cell Lung ; Disease-Free Survival ; Drosophila ; Humans ; Immunohistochemistry ; Lung ; Lung Neoplasms ; Lymph Nodes ; Neoplasm Metastasis ; Prognosis

Myxoid liposarcoma is a subtype of liposarcoma. This specific subtype can be identified based on its characteristic histological and cytogenetical features. The tumor has a fusion transcript of the CHOP and TLS genes, which is caused by t(12;16)(q13;p11). Most of the fusion transcripts that have been identified fall into three categories, specifically type I (exons 7-2), type II (exons 5-2), and type III (exons 8-2). A total of seven myxoid liposarcomas associated with the rare phenomenon of cartilaginous differentiation have been documented in the literature. Currently, only one of these cases has been cytogenetically analyzed, and the analysis indicated that it was a type II TLS-CHOP fusion transcript in both the typical myxoid liposarcoma and cartilaginous areas. This study presents a second report of myxoid liposarcoma with cartilaginous differentiation, and includes a cytogenetical analysis of both the myxoid and cartilaginous areas.
Cartilage ; Liposarcoma ; Liposarcoma, Myxoid

No abstract available.
Adenocarcinoma* ; Carcinoma, Papillary* ; Neoplasm Metastasis* ; Thyroid Gland*

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Materials and Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusion The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

BACKGROUND: The epidermal growth factor receptor (EGFR) is a surrogate marker for basal-like breast cancer. A recent study suggested that EGFR may be used as a target for breast cancer treatment. METHODS: A total of 706 invasive ductal carcinomas (IDC) of the breast were immunophenotyped, and 82 cases with EGFR protein expression were studied for EGFR gene amplification. RESULTS: EGFR protein was expressed in 121 of 706 IDCs (17.1%); 5.9% were of luminal type, 25.3% of epidermal growth factor receptor 2 (HER-2) type, and 79.3% of basal-like tumors. EGFR gene amplification and high polysomy (fluorescent in situ hybridization [FISH]-positive) were found in 18 of 82 cases (22.0%); 41.2% of the HER-2+, EGFR+, cytokeratin 5/6- (CK5/6-) group, 11.2% of the HER-2-, EGFR+, CK5/6- group, and 19.1% of the HER-2-, EGFR+, CK5/6+ group. FISH-positive cases were detected in 8.3% of the EGFR protein 1+ expression cases, 15.9% of 2+ expression cases, and 38.5% of 3+ expression cases. In group 2, the tumors had a high Ki-67 labeling (>60%), but the patients showed better disease-free survival than those with tumors that co-expressed HER-2 or CK5/6. CONCLUSIONS: EGFR-directed therapy can be considered in breast cancer patients with EGFR protein overexpression and gene amplification, and its therapeutic implication should be determined in HER-2 type breast cancer patients.
Biomarkers ; Breast ; Breast Neoplasms ; Carcinoma, Ductal ; Disease-Free Survival ; Gene Amplification ; Genes, erbB-1 ; Humans ; In Situ Hybridization ; Keratins ; Phenobarbital ; Receptor, Epidermal Growth Factor