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Advanced Cancer Diagnosis and Treatment: Second Edition

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A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: closed (28 February 2025) | Viewed by 11044

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Dr. Takaaki Hirotsu

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Hirotsu Bio Science Inc., Chiyoda-Ku, Tokyo 102-0094, Japan
Interests: cancer biodiagnostics; early detection of cancers; cancer biomarkers; neurobiology of olfaction; nematode biology
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Prof. Dr. Hideshi Ishii

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Department of Frontier Science for Cancer and Chemotherapy, Osaka University, Suita, Japan
Interests: pancreatic cancer; gastrointestinal cancer
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Dear Colleagues,

Cancer is the leading cause of death worldwide. The chances of survival increase with early cancer detection, but around half of all cancers are diagnosed at an advanced stage. Significant endeavors have been made to comprehend the mechanisms underlying cancer, create accurate and sensitive diagnostic options, and produce efficient treatments. In the last decade, advancements have been made in enhancing cancer screening programs and introducing innovative technologies to improve the early detection of cancers, with a particular focus on non-invasive methods. Novel approaches in cancer treatment are opening a new avenue for effective therapies.

Acknowledging the significant efforts made in combatting cancer, from academia to the private sector, for this Special Issue, we are seeking submissions of original research articles or reviews that present impactful advances in the development of cancer biology, diagnostics and therapeutics on one or more of, but not limited to, the following topics:

Cancer biology;
Molecular mechanisms of cancers;
Cancer epigenetics;
Mechanism of resistance to conventional therapies;
Cancer biomarkers;
Early detection of cancers;
Clinical investigations;
Diagnostics of cancers;
Novel therapeutics;
Novel paradigms in the diagnosis and treatment of cancers.

Dr. Takaaki Hirotsu
Prof. Dr. Hideshi Ishii
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomedicines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

cancer biology
epigenetics
early detection
diagnosis
biodiagnosis
novel therapeutics
new paradigms

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Published Papers (10 papers)

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10 pages, 3180 KiB

Open AccessArticle

Clinically Uncertain Liver Masses: A Guide to Distinguishing Poorly Differentiated Primary Liver Cancer

by Greta Sökeland, Michael P. Brönnimann, Erik Vassella, Guido Stirnimann, Matteo Montani and Juliane Friemel

Biomedicines 2025, 13(5), 1063; https://doi.org/10.3390/biomedicines13051063 - 28 Apr 2025

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Abstract

Objectives: The distinction of clinically uncertain, poorly differentiated liver masses into primary liver cancer (PLC) of cholangiocytic origin (intrahepatic cholangiocarcinoma; CCA) or hepatocellular origin (hepatocellular carcinoma; HCC) vs. metastasis is highly relevant to guiding patient treatment. Protocols differ in terms of resection, local ablation, liver transplantation, or systemic therapies with immune checkpoint inhibitors (ICIs) and tyrosine kinase inhibitors (TKIs). Methods: This retrospective case series exemplifies a multidisciplinary, practical guide to clinically uncertain liver masses using imaging, histomorphology, immune phenotyping, and mutational testing of telomerase promoter (TERT) combined with a literature review. Results: In 2/3 patients with uncertain liver masses and inconclusive immunohistochemistry profiles, TERT testing supported the diagnosis of poorly differentiated hepatocellular carcinoma. The third case with a history of sclerosing cholangitis and vague adenoid morphology yielded mutations in ARID1a and TP53 and was identified as primary liver cancer, consistent with poorly differentiated intrahepatic cholangiocarcinoma or mixed hepatocellular cholangiocarcinoma (cHCC/CCA). Conclusions: Finding HCC-typical TERT promoter mutations is a useful diagnostic tool in poorly differentiated primary liver cancer. Full article

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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15 pages, 1397 KiB

Open AccessArticle

The Impact of Resection Margins in Primary Resection of High-Grade Soft Tissue Sarcomas: How Far Is Far Enough?

by Julian Miles Steffens, Tymoteusz Budny, Georg Gosheger, Marieke De Vaal, Anna Maria Rachbauer, Andrea Laufer, Nina Myline Engel and Niklas Deventer

Biomedicines 2025, 13(5), 1011; https://doi.org/10.3390/biomedicines13051011 - 22 Apr 2025

Viewed by 168

Abstract

Background/Objectives: The World Health Organization’s (WHO) classification of tumors contains around 80 entities of soft tissue sarcomas (STSs). Currently, surgery is the standard treatment for patients with localized STS, but the adequacy of resection margins in soft tissue sarcomas (STSs) remains a topic of intense discussion. Methods: This single-center study retrospectively reviewed 203 patients with primary high-grade soft tissue sarcoma, including a follow-up period of at least 24 months. Patients with prior resection, secondary STS, metastasis at presentation, or those who required amputational surgery were excluded from the study. Patients were categorized based on their margin thickness: positive (n = 13, 6.4%), 0–1 mm (n = 67, 33.0%), 1–5 mm (n = 70, 34.5%), and >5 mm (n = 27, 13.3%). Results: A total of 64 out of 203 (31.5%) patients developed a local recurrence. The estimated 5-year local-recurrence-free survival (LRFS) was 11.5% (CI 4–25%) for positive margins, 58% (CI 51–64%) for margins 0–1 mm, 76% (CI 70–81%) for margins > 1–5 mm, and 93% (CI 88–98%) for margins > 5 mm. No local recurrences occurred in patients with margins > 5 mm and adjuvant radiotherapy. Margin status significantly influenced the development of distant metastasis and overall survival. Adjuvant radiotherapy improved both local control and overall survival. Conclusions: To minimize the risk of local recurrence (LR), a resection margin greater than 5 mm should be attained. When adjuvant radiotherapy is applied, the likelihood of LR decreases even more. In scenarios where preserving critical structures is essential, a resection margin of less than 5 mm can be acceptable for ensuring local control. Full article

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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12 pages, 922 KiB

Open AccessArticle

Primary Renal Lymphoma: Report of 32 Cases—A Retrospective Multicenter PLRG Analysis

by Magdalena Witkowska, Joanna Romejko-Jarosińska, Agnieszka Giza, Joanna Drozd-Sokołowska, Damian Mikulski, Janusz Hałka, Anna Morawska-Krekora, Ewa Paszkiewicz-Kozik, Kamil Wdowiak, Dariusz Wołowiec and on behalf of the Polish Lymphoma Research Group

Biomedicines 2025, 13(3), 548; https://doi.org/10.3390/biomedicines13030548 - 21 Feb 2025

Viewed by 441

Abstract

Introduction: Primary renal lymphoma is extremely rare, accounting for less than 1% of all lymphomas. Objectives: The aim of this study was to describe a group of patients with primary renal lymphoma diagnosed in hematology and oncology centers aligned with the Polish Lymphoma Research Group (PLRG). Patients and methods: This was a retrospective analysis of adult patients with primary renal lymphoma diagnosed at PLRG centers. Results: Thirty-two patients were diagnosed in seven centers over 24 years (2000–2023). The most common type of lymphoma was diffuse large B-cell lymphoma (DLBCL). The median progression-free survival (PFS) after first-line treatment was 2.1 (95% CI: 1.07–4.18) years, while the median overall survival (OS) was 6.3 (95% CI: 1.82–6.34) years. The median age at diagnosis was 63.3 years old, and 59.4% of the patients were females. In multivariate Cox regression for PFS, only creatinine > 1.5 mg/dL (HR 10.2, 95% CI: 2.08–50.09, p = 0.004) and hemoglobin (Hgb) < 10 g/dL (HR 8.39, 95% CI: 1.88–37.49) were associated with inferior PFS. Patients who achieved complete remission (CR) after first-line of treatment had significantly longer PFS (median 4.18, 95% CI: 2.02–4.18 vs. median 0.73, 95% CI: 0.50–0.79, p = 0.004) and OS (median not reached vs. median 1.49, 95% CI: 0.43–6.33, p = 0.001). Patients treated with nephrectomy had longer OS (median not reached vs. median 5.07, 95% CI: 1.32–5.08, p = 0.05). However, in multivariate Cox regression for OS, only hypoalbuminemia was an independent factor for inferior survival (HR 5.44, 95% CI: 1.12–26.38, p = 0.04). Conclusions: Primary renal lymphoma is an extremely rare type of lymphoma with a poor prognosis. The prognosis may improve with future advances in treatment, including nephrectomy. Full article

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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36 pages, 6349 KiB

Open AccessArticle

Streamlit Application and Deep Learning Model for Brain Metastasis Monitoring After Gamma Knife Treatment

by Răzvan Buga, Călin Gh. Buzea, Maricel Agop, Lăcrămioara Ochiuz, Decebal Vasincu, Ovidiu Popa, Dragoș Ioan Rusu, Ioana Știrban and Lucian Eva

Biomedicines 2025, 13(2), 423; https://doi.org/10.3390/biomedicines13020423 - 10 Feb 2025

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Abstract

Background/Objective: This study explores the use of AI-powered radiomics to classify and monitor brain metastasis progression and regression following Gamma Knife radiosurgery (GKRS) based on MRI imaging. A clinical decision support application was developed using Streamlit to provide real-time, AI-driven predictions for treatment monitoring. Methods: MRI scans from 60 patients (3194 images) were analyzed using a transfer learning-enhanced AlexNet deep learning model. Class imbalance was mitigated through dynamic class weighting and data augmentation to ensure equitable performance across all classes. Optimized preprocessing pipelines ensured dataset standardization. Model performance was evaluated using accuracy, precision, recall, F1-scores, and AUC, with 95% confidence intervals. Additionally, a comparative analysis of Gamma Knife radiosurgery (GKRS) outcomes and predictive modeling demonstrated strong correlations between tumor volume evolution and treatment response. The AI predictions and visualizations were integrated into a Streamlit-based application to ensure clinical usability and ease of access. The AI-driven approach effectively classified progression and regression patterns, reinforcing its potential for clinical integration. Results: The transfer learning model achieved flawless classification accuracy (100%; 95% CI: 100–100%) along with perfect precision, recall, and F1-scores. The AUC score of 1.0000 (95% CI: 1.0000–1.0000) indicated excellent discrimination between progression and regression cases. Compared to the baseline AlexNet model (99.53% accuracy; 95% CI: 98.90–100.00%), the TL-enhanced model resolved all misclassifications. Tumor volume analysis identified the baseline size as a key predictor of progression (Pearson r = 0.795, r = 0.795, r = 0.795, p < 0.0001, p < 0.0001, and p < 0.0001). The training time (420.12 s) was faster than ResNet-50 (443.38 s) and EfficientNet-B0 (439.87 s), while achieving equivalent metrics. Despite 100% accuracy, the model requires multi-center validation for generalizability. Conclusions: This study demonstrates that transfer learning with dynamic class weighting provides a highly accurate and reliable framework for monitoring brain metastases post-GKRS. The Streamlit-based AI application enhances clinical decision-making by improving diagnostic precision and reducing variability. Explainable AI techniques, such as Grad-CAM visualizations, improve interpretability and support clinical adoption. These findings emphasize the transformative potential of AI in personalized treatment strategies, extending applications to genomic profiling, survival modeling, and longitudinal follow-ups for brain metastasis management. Full article

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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18 pages, 4549 KiB

Open AccessArticle

A Transfer Learning-Based Framework for Classifying Lymph Node Metastasis in Prostate Cancer Patients

by Suryadipto Sarkar, Teresa Wu, Matthew Harwood and Alvin C. Silva

Biomedicines 2024, 12(10), 2345; https://doi.org/10.3390/biomedicines12102345 - 15 Oct 2024

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Abstract

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

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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17 pages, 1498 KiB

Open AccessArticle

Neoadjuvant Statistical Algorithm to Predict Individual Risk of Relapse in Patients with Resected Liver Metastases from Colorectal Cancer

by Ángel Vizcay Atienza, Olast Arrizibita Iriarte, Oskitz Ruiz Sarrias, Teresa Zumárraga Lizundia, Onintza Sayar Beristain, Ana Ezponda Casajús, Laura Álvarez Gigli, Fernando Rotellar Sastre, Ignacio Matos García and Javier Rodríguez Rodríguez

Biomedicines 2024, 12(8), 1859; https://doi.org/10.3390/biomedicines12081859 - 15 Aug 2024

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Abstract

(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

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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19 pages, 28195 KiB

Open AccessArticle

Morphological and Immunocytochemical Characterization of Paclitaxel-Induced Microcells in Sk-Mel-28 Melanoma Cells

by Zane Simsone, Tālivaldis Feivalds, Līga Harju, Indra Miķelsone, Ilze Blāķe, Juris Bērziņš and Indulis Buiķis

Biomedicines 2024, 12(7), 1576; https://doi.org/10.3390/biomedicines12071576 - 16 Jul 2024

Viewed by 1627

Abstract

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

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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13 pages, 670 KiB

Open AccessArticle

Liver Transplantation for Hepatocarcinoma: Results over Two Decades of a Transplantation Programme and Analysis of Factors Associated with Recurrence

by María Martínez Burgos, Rocío González Grande, Susana López Ortega, Inmaculada Santaella Leiva, Jesús de la Cruz Lombardo, Julio Santoyo Santoyo and Miguel Jiménez Pérez

Biomedicines 2024, 12(6), 1302; https://doi.org/10.3390/biomedicines12061302 - 12 Jun 2024

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Abstract

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

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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Review

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14 pages, 1129 KiB

Open AccessReview

A Review of Limbic System-Associated Membrane Protein in Tumorigenesis

by Kayleigh Wittmann Sinopole, Kevin Babcock, Albert Dobi and Gyorgy Petrovics

Biomedicines 2024, 12(11), 2590; https://doi.org/10.3390/biomedicines12112590 - 13 Nov 2024

Viewed by 1479

Abstract

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

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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17 pages, 1209 KiB

Open AccessReview

The Clinical and Molecular Landscape of Rosette-Forming Glioneuronal Tumors

by Zijiang Yang and Xiaobiao Zhang

Biomedicines 2024, 12(10), 2325; https://doi.org/10.3390/biomedicines12102325 - 12 Oct 2024

Cited by 1 | Viewed by 1171

Abstract

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

(This article belongs to the Special Issue Advanced Cancer Diagnosis and Treatment: Second Edition)

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