Advanced Cancer Diagnosis and Treatment: Second Edition

Background: Prostate cancer is the second most common new cancer diagnosis in the United States. It is usually slow-growing, and when it is low-grade and confined to the prostate gland, it can be treated either conservatively (through active surveillance) or with surgery. However, if the cancer has spread beyond the prostate, such as to the lymph nodes, then that indicates a more aggressive cancer, and surgery may not be adequate. Methods: The challenge is that it is often difficult for radiologists reading prostate-specific imaging such as magnetic resonance images (MRIs) to differentiate malignant lymph nodes from non-malignant ones. An emerging field is the development of artificial intelligence (AI) models, including machine learning and deep learning, for medical imaging to assist in diagnostic tasks. Earlier research focused on implementing texture algorithms to extract imaging features used in classification models. More recently, researchers began studying the use of deep learning for both stand-alone feature extraction and end-to-end classification tasks. In order to tackle the challenges inherent in small datasets, this study was designed as a scalable hybrid framework utilizing pre-trained ResNet-18, a deep learning model, to extract features that were subsequently fed into a machine learning classifier to automatically identify malignant lymph nodes in patients with prostate cancer. For comparison, two texture algorithms were implemented, namely the gray-level co-occurrence matrix (GLCM) and Gabor. Results: Using an institutional prostate lymph node dataset (42 positives, 84 negatives), the proposed framework achieved an accuracy of 76.19%, a sensitivity of 79.76%, and a specificity of 69.05%. Using GLCM features, the classification achieved an accuracy of 61.90%, a sensitivity of 74.07%, and a specificity of 42.86%. Using Gabor features, the classification achieved an accuracy of 65.08%, a sensitivity of 73.47%, and a specificity of 52.50%. Conclusions: Our results demonstrate that a hybrid approach, i.e., using a pre-trainined deep learning model for feature extraction, followed by a machine learning classifier, is a viable solution. This hybrid approach is especially useful in medical-imaging-based applications with small datasets. Full article

(1) Background: Liver metastases (LM) are the leading cause of death in colorectal cancer (CRC) patients. Despite advancements, relapse rates remain high and current prognostic nomograms lack accuracy. Our objective is to develop an interpretable neoadjuvant algorithm based on mathematical models to accurately predict individual risk, ensuring mathematical transparency and auditability. (2) Methods: We retrospectively evaluated 86 CRC patients with LM treated with neoadjuvant systemic therapy followed by complete surgical resection. A comprehensive analysis of 155 individual patient variables was performed. Logistic regression (LR) was utilized to develop the predictive model for relapse risk through significance testing and ANOVA analysis. Due to data limitations, gradient boosting machine (GBM) and synthetic data were also used. (3) Results: The model was based on data from 74 patients (12 were excluded). After a median follow-up of 58 months, 5-year relapse-free survival (RFS) rate was 33% and 5-year overall survival (OS) rate was 60.7%. Fifteen key variables were used to train the GBM model, which showed promising accuracy (0.82), sensitivity (0.59), and specificity (0.96) in predicting relapse. Similar results were obtained when external validation was performed as well. (4) Conclusions: This model offers an alternative for predicting individual relapse risk, aiding in personalized adjuvant therapy and follow-up strategies. Full article

Biomarkers, including proteins, nucleic acids, antibodies, and peptides, are essential for identifying diseases such as cancer and differentiating between healthy and abnormal cells in patients. To date, studies have shown that cancer stem cells have DNA repair mechanisms that deter the effects of medicinal treatment. Experiments with cell cultures and chemotherapy treatments of these cultures have revealed the presence of small cells, with a small amount of cytoplasm that can be intensively stained with azure eosin, called microcells. Microcells develop during sporosis from a damaged tumor macrocell. After anticancer therapy in tumor cells, a defective macrocell may produce one or more microcells. This study aims to characterize microcell morphology in melanoma cell lines. In this investigation, we characterized the population of cancer cell microcells after applying paclitaxel treatment to a Sk-Mel-28 melanoma cell line using immunocytochemical cell marker detection and fluorescent microscopy. Paclitaxel-treated cancer cells show stronger expression of stem-associated ALDH2, SOX2, and Nanog markers than untreated cells. The proliferation of nuclear antigens in cells and the synthesis of RNA in microcells indicate cell self-defense, promoting resistance to applied therapy. These findings improve our understanding of microcell behavior in melanoma, potentially informing future strategies to counteract drug resistance in cancer treatment. Full article

Background: In recent years, many studies have attempted to develop models to predict the recurrence of hepatocarcinoma after liver transplantation. Method: A single-centre, retrospective cohort study analysed patients receiving transplants due to hepatocarcinoma during the 20 years of the transplant programme. We analysed patient survival, hepatocarcinoma recurrence and the influence of the different factors described in the literature as related to hepatocarcinoma recurrence. We compared the results of previous items between the first and second decades of the transplantation programme (1995–2010 and 2010–2020). Results: Of 265 patients, the patient survival rate was 68% at 5 years, 58% at 10 years, 45% at 15 years and 34% at 20 years. The overall recurrence rate of hepatocarcinoma was 14.5%, without differences between periods. Of these, 54% of recurrences occurred early, in the first two years after transplantation. Of the parameters analysed, an alpha-fetoprotein level of >16 ng/mL, the type of immunosuppression used and the characteristics of the pathological anatomy of the explant were significant. A trend towards statistical significance was identified for the number of nodules and the size of the largest nodule. Logistic regression analysis was used to develop a model with a sensitivity of 85.7% and a specificity of 35.7% to predict recurrences in our cohort. Regarding the comparison between periods, the survival and recurrence rates of hepatocarcinoma were similar. The impact of the factors analysed in both decades was similar. Conclusions: Most recurrences occur during the first two years post-transplantation, so closer follow-ups should be performed during this period, especially in those patients where the model predicts a high risk of recurrence. The detection of patients at higher risk of recurrence allows for closer follow-up and may, in the future, make them candidates for adjuvant or neoadjuvant systemic therapies to transplantation. Full article

Purpose of Review: This review aims to describe the role of limbic system-associated membrane protein (LSAMP) in normal- and pathophysiology, and its potential implications in oncogenesis. We have summarized research articles reporting the role of LSAMP in the development of a variety of malignancies, such as clear cell renal cell carcinoma, prostatic adenocarcinoma, lung adenocarcinoma, osteosarcoma, neuroblastoma, acute myeloid leukemia, and epithelial ovarian cancer. We also examine the current understanding of how defects in LSAMP gene function may contribute to oncogenesis. Finally, this review discusses the implications of future LSAMP research and clinical applications. Recent Findings: LSAMP has been originally described as a surface adhesion glycoprotein expressed on cortical and subcortical neuronal somas and dendrites during the development of the limbic system. It is categorized as part of the IgLON immunoglobulin superfamily of cell-adhesion molecules and is involved in regulating neurite outgrowth and neural synapse generation. LSAMP is both aberrantly expressed and implicated in the development of neuropsychiatric disorders due to its role in the formation of specific neuronal connections within the brain. Additionally, LSAMP has been shown to support brain plasticity via the formation of neuronal synapses and is involved in modulating the hypothalamus in anxiogenic environments. In murine studies, the loss of LSAMP expression was associated with decreased sensitivity to amphetamine, increased sensitivity to benzodiazepines, increased hyperactivity in new environments, abnormal social behavior, decreased aggressive behavior, and decreased anxiety. Findings have suggested that LSAMP plays a role in attuning serotonergic activity as well as GABA activity. Given its importance to limbic system development, LSAMP has also been studied in the context of suicide. In malignancies, LSAMP may play a significant role as a putative tumor suppressor, the loss of which leads to more aggressive phenotypes and mortality from metastatic disease. Loss of the LSAMP gene facilitates epithelial-mesenchymal transition, or EMT, where epithelial cells lose adhesion and gain the motile properties associated with mesenchymal cells. Additionally, LSAMP and the function of the RTK pathway have been implicated in tumorigenesis through the modulation of RTK expression in cell membranes and the activation of second messenger pathways and β-catenin. Summary: Beyond its many roles in the limbic system, LSAMP functions as a putative tumor suppressor protein. Loss of the LSAMP gene is thought to facilitate epithelial-mesenchymal transition, or EMT, where cells lose adhesion and migrate to distant organs. LSAMP’s role in modulating RTK activity and downstream ERK and Akt pathways adds to a large body of data investigating RTK expression in oncogenesis. The characteristics of LSAMP defects and their association with aggressive and metastatic disease are evident in reports on clear cell renal cell carcinoma, prostatic adenocarcinoma, lung adenocarcinoma, osteosarcoma, neuroblastoma, acute myeloid leukemia, and epithelial ovarian cancer. Full article

Background: Rosette-Forming Glioneuronal Tumors (RGNTs) are rare, typically benign central nervous system tumors primarily located in the fourth ventricle and pineal region. Despite being classified as WHO grade I with generally favorable prognoses, RGNTs present complexities in their molecular mechanisms, occasional malignant transformation, and epidemiological characteristics that require further investigation. Method: This study systematically reviews the existing literature to analyze the epidemiological patterns, MRI characteristics, pathological features, diagnostic challenges, and molecular mechanisms associated with RGNTs, aiming to provide a comprehensive theoretical foundation for clinical practice and future research. Results: Through an in-depth review of recent studies, key molecular mechanisms, including mutations in FGFR1, PIK3CA, TERT, and IDH1/2, are highlighted. Additionally, the challenges in accurate diagnosis and the potential for misdiagnosis are discussed, emphasizing the importance of thorough molecular analysis in clinical settings. The literature indicates that RGNTs predominantly affect young adults and adolescents, with a slight female predominance. MRI typically reveals mixed cystic–solid lesions, often accompanied by hydrocephalus. Pathologically, RGNTs are characterized by a combination of neuronal and glial components, with immunohistochemical staining showing positivity for Synaptophysin and GFAP. High frequencies of FGFR1 and PIK3CA mutations underscore the significance of these pathways in RGNT pathogenesis and progression. Although RGNTs generally exhibit low malignancy, the TERT mutations identified in some cases suggest a risk of malignant transformation. Conclusions: This study concludes that while current treatment strategies focus on surgical resection, integrating molecular diagnostics and targeted therapies may be essential for managing recurrent or refractory RGNTs. Future research should explore the impact of various gene mutations on tumor behavior and their correlation with clinical outcomes, to optimize individualized therapeutic strategies and improve patient survival and quality of life. Full article