Search Results (31)

Breast cancer remains the most frequently diagnosed malignancy in women worldwide, accounting for approximately 2.3 million new cases and 670,000 deaths annually. The diagnostic landscape has undergone a paradigm shift over the past two decades, evolving from morphology-based classification toward molecularly informed, precision-guided strategies. Early and accurate diagnosis is fundamental to improving outcomes; advances in imaging technology, including digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), and abbreviated magnetic resonance imaging (MRI), have improved sensitivity and specificity in diverse patient populations. Simultaneously, the integration of artificial intelligence (AI) and radiomics into screening workflows offers unprecedented potential for risk stratification and a reduction in false-positives. At the pathological level, multi-gene expression profiling assays such as Oncotype DX, MammaPrint, Prosigna, and EndoPredict have refined prognostic classification and guide adjuvant chemotherapy decisions in early-stage hormone receptor-positive disease. The emergence of liquid biopsy, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and exosomal biomarkers provides minimally invasive tools for real-time monitoring of response, residual disease, and the evolution of resistance mechanisms. Precision diagnostics now encompass next-generation sequencing (NGS)-based comprehensive genomic profiling, enabling identification of actionable alterations such as PIK3CA mutations, HER2 amplification, BRCA1/2 pathogenic variants, and NTRK fusions, each linked to approved therapeutic agents. The purpose of this review is to provide a comprehensive synthesis of current and emerging diagnostic modalities in breast cancer—from population-level screening to individualized molecular profiling—and to examine how integrative, multimodal diagnostic platforms are reshaping clinical decision-making in the era of precision medicine. Full article

Background/Objectives: Breast cancer remains the leading cause of cancer-related death among women worldwide. While tools such as Adjuvant Online, PREDICT, OncotypeDx and Mammoprint identify patients at higher risk of relapse who should therefore be offered chemotherapy, there are currently no tools to accurately predict response to chemotherapy, with varied response rates (regardless of subtypes, etc.) of 8–70% reported. Accurately stratifying patients based on their likelihood of benefiting from SoC chemotherapy is therefore critical to guide personalised treatment decisions. Methods: A retrospective cohort of 293 breast cancer patients treated with SoC adjuvant anthracycline-based regimen was analysed. Five biomarkers (TOP2A, PTEN, EGFR, IGF1R, and phospho-mTOR), selected for their prognostic and therapeutic relevance, were assessed using immunohistochemistry (IHC) combined with digital pathology. Results: Biomarker expression was quantified using the digital pathology platform, QuPath, with each marker, when stratified based on high/low expression, demonstrating a significant association with relapse-free survival following SoC chemotherapy in specific subtypes of breast cancer. A composite five-biomarker signature was then generated by integrating the individual biomarker scores to improve prognostic precision. Patients with a five-biomarker signature score greater than zero exhibited a significantly higher likelihood of favourable outcomes following anthracycline-based chemotherapy compared with those with a score of zero or below. Conclusions: This study establishes a novel IHC-based five-biomarker signature capable of predicting patient outcome in the context of SoC chemotherapy. As the signature relies exclusively on IHC, it is simple, cost-effective and readily integratable into routine diagnostic workflows. In addition to its prognostic value, several biomarkers within the panel are potentially actionable, offering opportunities to guide targeted therapies in patients predicted to have poor response to conventional chemotherapy. Full article

Introduction: Oncological therapies have significantly improved patient outcomes but are increasingly associated with renal toxicity, which can markedly influence therapeutic decisions. Integrating early identification of kidney injury into clinical workflows is essential for personalized medicine, allowing treatment tailoring based on individual risk profiles. Aim: To evaluate the incidence of acute kidney injury (AKI) and chronic kidney Disease (CKD); assess indices of renal function recovery in patients who developed AKI; and investigate the incidence of renal immune-related adverse events (irAEs) in patients receiving immunotherapy. Materials: Renal function, serum electrolytes, inflammatory markers, blood gas analysis, and urinalysis were evaluated at baseline before oncological therapy (T0), after approximately 2 weeks (T1), and after 3 months (T2). Results: Seventy patients were analyzed (median age 71.5 years). AKI occurred in 43 patients (61.4%) and CKD in 18 (25.7%). Patients receiving immunotherapy displayed significantly higher blood urea nitrogen (p < 0.01) and creatinine (p < 0.01) levels compared to those undergoing traditional therapies (targeted therapy and chemotherapy). Treatment discontinuation was required in 14 (56%) immunotherapy patients versus 7 (19.4%) receiving traditional therapy (anti-VEGF and cisplatin) (p < 0.01). Among 25 immunotherapy-treated patients, 13 (52%) developed immune-related adverse events (irAEs). Patients with irAEs predominantly experienced AKI (92.3%), whereas those without irAEs showed both AKI and CKD (44.4%) (p < 0.01). Treatment discontinuation occurred in 84.6% of patients with irAEs compared to 11.1% without irAEs (p < 0.001). Conclusions: We showed a high incidence of AKI and CKD among cancer patients; in particular, the majority of patients receiving immunotherapy presented irAEs. CKD also occurs in association with comorbidities, such as previous use of NSAIDs, investigations with contrast agents and episodes of AKI on CKD determined by drugs. It seems necessary for there to be multidisciplinary collaboration between oncologists and nephrologists to individualize treatment plans; thus allowing the non-suspension of therapy, which positively influences the prognosis of patients. Full article

Background: Therapeutic drug monitoring (TDM) is a promising strategy to personalize chemotherapy dosing, especially for agents with narrow therapeutic indices such as epirubicin. However, widespread adoption is hindered by logistical challenges associated with venous blood sampling and centralized laboratory workflows. Objective: This study aimed to perform a method-comparison validation of the True Dose^® TD-EPI microsampling kit by verifying analytical agreement between capillary and venous epirubicin measurements in real patient samples. The study focuses on analytical performance and does not constitute validation of the whole decentralized workflow, including unsupervised patient self-sampling. Methods: 13 patients with early-stage breast cancer receiving the first cycle of neoadjuvant or adjuvant epirubicin were enrolled. Capillary samples were collected using the finalized TD-EPI kit (Cap-TD) at 2.5 h (n = 13) and/or 48 h (n = 10) post-infusion and stored at room temperature for 72 h before analysis. Matched venous samples were analyzed using both conventional protein precipitation (“Traditional”) and a modified lab-based True Dose workflow (Lab-TD). Epirubicin concentrations were quantified via validated liquid chromatography–tandem mass spectrometry (LC–MS/MS). Results: Cap-TD concentrations showed strong agreement with Traditional venous values (r = 0.953), with minimal bias (mean difference = 0.013 μM) in Bland–Altman analysis. Passing–Bablok regression confirmed analytical equivalence. Intra-assay variability remained within ICH M10 guidelines (CV ≤ 15%), and recovery was unaffected by 72 h ambient storage. Lab-TD results closely matched Traditional workflows, supporting reproducibility. Conclusions: The TD-EPI kit enables accurate decentralized monitoring of epirubicin, eliminating the need for venous access, cold-chain logistics, or in-clinic sampling. These findings support its integration into personalized oncology care and future applications in home-based TDM. Trial Registration: This study is part of an approved protocol registered in the EU Clinical Trials Register (EUCT Number 2024-514818-12-00; EudraCT Number 2017-000641-44; registration date: 15 June 2017). Full article

Process Mining (PM) effectively diagnoses inefficiencies in complex healthcare workflows, such as chemotherapy protocols. However, current methodologies often remain retrospective or rely on loosely coupled simulations, leaving a critical methodological void: the inability to quantify the aggregate, system-wide operational impact of eliminating specific, diagnosed workflow deviations. This gap prevents decision-makers from forming evidence-based strategies for resource allocation. We address this by introducing the PM²–Predictive Impact Model (PIM) framework, a novel, fully embedded process-native methodology that unifies conformance checking, predictive monitoring, and quantitative scenario analysis within a singular, closed-loop structure. Using event logs from an Iranian Radiotherapy and Oncology Center, we modeled a normative seven-step pathway (Fitness = 0.97, Precision = 1.00) and identified high-impact deviations, including skipped approvals and resequencing, enabling a direct causal linkage between deviation categories and system performance. PIM simulation demonstrated that removing these deviations yields statistically significant reductions in managerially relevant KPIs: Cycle Time (8.00%) and Workload (6.00%), which were robust to parameter uncertainty (p < 0.001). The PM²–PIM framework thus transforms retrospective diagnosis into proactive, quantitatively justified strategic planning, providing oncology services with a reproducible, low-cost, and evidence-rich basis for prioritizing interventions and achieving sustained performance gains. Full article

Acute lymphoblastic leukemia (ALL) is the most common childhood cancer, with pediatric ALL having a ~90 percent cure rate, while the adult cure rate is considerably lower. B-cell acute lymphoblastic leukemia (B-ALL) is the most common subtype of ALL and is generally treated through a variety of chemotherapy drugs that can cause undesired side effects, adverse events, or other complications. Consequently, there is a need for improved understanding of the shared gene expression profiles and underlying molecular mechanisms shared among various B-ALL subtypes. In this study, 259 publicly available RNA-sequencing samples were evaluated and retrieved from the NCBI Gene Expression Omnibus (GEO) database and then pre-processed using a robust computational workflow. Differential gene expression, pathway enrichment, marker prediction, and drug repurposing analyses were then performed to facilitate a better mechanistic understanding of disease. We found both previously identified as well as novel differentially expressed genes. Specifically, we observed upregulation in the HIST2H2AA3, EPHA7, and MPR1 genes; while downregulation was observed for the IGHA1, ANGPTL1, and CHAD genes. We identified multiple pathways, including “Integrins in Angiogenesis”, to be significantly affected in B-ALL. We then used these significant pathways to predict and rank 306 existing therapeutic targets that could potentially be repurposed for B-ALL, including three that have not been evaluated in human clinical trials. Using a tree-based classification algorithm, we also predicted ADAM28 as a possible mechanistic marker. The results of this study have potential implications for patients who have been diagnosed with B-ALL by providing improved mechanistic understanding and information on possible diagnostics and repurposed therapeutics for B-ALL. Full article

Background/Objectives: Peripheral nerve tumours are commonly encountered in clinical practice. Although most are benign, a subset can exhibit aggressive and invasive behaviour, evolving into malignant peripheral nerve sheath tumours (MPNSTs). Due to their rarity and overlapping features with benign lesions, MPNSTs are frequently misdiagnosed during the initial evaluation. Preoperative biopsy may aid in distinguishing malignant from benign lesions. This single-center study aimed to develop and validate a diagnostic algorithm—based on a systematic literature review and institutional case series—to assess the role of preoperative biopsy in the diagnostic workflow. Methods: A systematic review of the literature was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, covering the period from 1998 to 2024. Additionally, a retrospective case series of patients with peripheral nerve lesions treated at the authors’ institution between January 2018 and June 2024 was analysed. Results: Forty-eight articles met the inclusion criteria and were categorized into five key domains: radiological features of MPNSTs, associated risk factors and genetic conditions, the role of preoperative biopsy, use of radiotherapy, and general clinical management strategies. The proposed diagnostic algorithm was applied to a series of 36 patients, four of whom met the criteria for preoperative biopsy. In three of these cases, early diagnosis of MPNSTs was achieved. Conclusions: Preoperative biopsy appears to be a safe and cost-effective tool for the early identification of MPNSTs. Early diagnosis may facilitate the use of neoadjuvant therapies—such as radiotherapy or chemotherapy—potentially enabling more radical surgical resection and improving overall patient outcomes. Full article

Anal cancer is a rare malignancy with rising incidence. Definitive treatment with radiation and concurrent chemotherapy represent the standard of care for patients with non-metastatic disease. Advances in radiation delivery through the use of intensity-modulated radiotherapy have significantly reduced the toxic effects of treatment. Adaptive radiotherapy (ART) has emerged as a strategy to further enhance treatment precision and individualize therapy in response to patient-specific changes during the course of chemoradiotherapy. The rationale for ART in anal cancer stems from the recognition that significant anatomic and tumor changes can occur throughout the 5–6-week treatment course, including tumor shrinkage, weight loss, and variable rectal/bladder filling. This review discusses the role of ART in contemporary anal cancer management. We overview the principles of ART, delineate the technical workflows (including both computed tomography (CT) and MR-guided approaches), and examine how adaptive techniques are applied in treatment planning and delivery. We also review the clinical evidence to date, including dosimetric studies and emerging clinical trial data on ART in anal cancer, particularly its impact on outcomes and toxicity. Full article

Background: FLT3 mutation testing is a key ancillary molecular assay for diagnosing and managing patients with acute myeloid leukemia (AML), including assessing the utility of FLT3 inhibitors during induction chemotherapy. FLT3 PCR utilizing fluorescently labeled primers and capillary electrophoresis readout is the most used technique for the rapid detection of FLT3 internal tandem duplications (ITDs) (including very small ITDs) and tyrosine kinase domain (TKD) mutations. However, capillary electrophoresis (CE) is a relatively lengthy and technically demanding result readout mode that could potentially be replaced by faster alternatives. Methods: Here, we describe the validation of a modified FLT3 PCR assay that uses the Agilent 4200 TapeStation platform for result readouts. This platform generates quantifiable electropherograms and gel images in under two minutes and at a low cost. We validated its ability to detect FLT3-ITD and -TKD mutations using 22 and 18 previously tested patient samples, respectively. Results: The TapeStation 4200 instrument is 100% sensitive, specific, and highly reproducible for post-PCR fragment analysis in detecting FLT3-ITD (greater than 15 bp in size) and TKD mutations in AML patients. Its results are nearly 100% concordant with those obtained from our previously validated NGS and PAGE methods. However, the limitation of this readout mode is its inability to reliably detect FLT3-ITDs smaller than 15 bp in size. Conclusions: Given the widespread use of TapeStation instruments in molecular diagnostics laboratories as part of next-generation sequencing (NGS) workflows, this modified assay is well-suited as a companion test for rapid NGS platforms to detect larger FLT3-ITDs, which are NGS often miscalledor under-called by the NGS bioinformatics algorithms. However, it is not suitable for use as a standalone assay, as it is unable to reliably detect very short FLT3-ITDs. Full article

Background/Objectives: Predicting symptom escalation during chemotherapy is crucial for timely interventions and improved patient outcomes. This study employs deep learning models to predict the deterioration of 12 self-reported symptoms, categorized into physical (e.g., nausea, fatigue, pain) and mental (e.g., feeling blue, trouble thinking) groups. Methods: The analytical dataset comprises daily self-reported symptom logs from individuals undergoing chemotherapy. To address class imbalance—where 84% of cases showed no escalation—symptoms were grouped into intervals of 3 to 7 days. Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models were trained on 80% of the data and evaluated on the remaining 20%. Results: Results showed that 3-day intervals yielded the best predictive performance. CNNs excelled in predicting physical symptoms, achieving 79.2% accuracy, 84.1% precision, 78.8% recall, and an F1 score of 81.4%. For mental symptoms, GRU outperformed other models, with an accuracy of 77.2%, precision of 71.6%, recall of 62.2%, and an F1 score of 66.6%. Performance declined for longer intervals due to reduced temporal resolution and fewer training samples, though CNNs and GRU remained relatively stable. Conclusions: The findings emphasize the advantage of categorizing symptoms for more tailored predictions and demonstrate the potential of deep learning in forecasting symptom escalation. Integrating these predictive models into clinical workflows could facilitate proactive symptom management, allowing timely interventions and enhanced patient care during chemotherapy. Full article

Background: Peripherally Inserted Central Catheters (PICCs) and midline catheters are crucial for chemotherapy and supportive care in cancer patients. Their use requires ongoing monitoring to prevent late complications such as infections, dislodgements, and replacements. The COVID-19 pandemic challenged healthcare systems, potentially increasing these complications due to reduced outpatient services and limited specialized personnel. Objectives: This study compared the incidence of late complications associated with PICCs and midline catheters in cancer patients during and after the COVID-19 pandemic. Methods: A retrospective observational study was conducted at a Cancer Center in Italy from March 2020 to April 2024. Catheter-related complications were divided into two cohorts: during the pandemic (March 2020–March 2022) and post-pandemic (April 2022–April 2024). The primary outcome was the incidence of late complications requiring device removal, categorized as infections, dislodgements, and replacements. Statistical analyses included the Chi-squared test for categorical variables and the Kruskal–Wallis test for continuous variables. Results: Of 4104 PICC and midline catheter placements, 2291 removals were recorded, with 550 (24%) due to late complications—404 during the pandemic and 146 post-pandemic (p < 0.001). Suspected infections were the most frequent complication, significantly higher during the pandemic (p < 0.001). Dislodgements and replacements also decreased markedly post-pandemic. Limited outpatient services and disrupted healthcare workflows likely contributed to higher complication rates during the pandemic. Conclusions: The COVID-19 pandemic negatively impacted catheter management in cancer patients, increasing late complications. The post-pandemic decline highlights the importance of consistent care, infection prevention, remote monitoring, and stronger healthcare resilience to reduce risks in future crises. Full article

Gangliosides are glycosphingolipids composed of a sialylated glycan head group and a ceramide backbone. These anionic lipids form lipid rafts and play crucial roles in regulating various proteins involved in signal transduction, adhesion, and cell–cell recognition. Neuroblastoma, a pediatric cancer of the sympathetic nervous system, is treated with intensive chemotherapy, radiation, and an antibody targeting the GD2 ganglioside. Gangliosides are critical in neuroblastoma development and serve as therapeutic targets, making it essential to establish a reliable, rapid, and cost-effective method for profiling gangliosides, particularly one capable of isomeric separation of intact species. In this study, liquid chromatography–mass spectrometry (LC-MS) was optimized using standard gangliosides, followed by the optimization of sphingolipid extraction methods from cell lines by comparing Folch and absolute methanol extraction techniques. Percent recovery and the number of identified sphingolipids were used to evaluate the analytical merits of these methods. A standard gangliosides calibration curve demonstrated excellent linearity (R² = 0.9961–0.9975). The ZIC-HILIC column provided the best separation of ganglioside GD1 isomers with a 25 min runtime. GD1a elutes before GD1b on the ZIC-HILIC column. Absolute methanol yielded better percent recovery (96 ± 7) and identified 121 different sphingolipids, the highest number between the two extraction methods. The optimized method was applied to profile gangliosides in neuroblastoma (COG-N-683), pancreatic cancer (PSN1), breast cancer (MDA-MB-231BR), and brain tumor (CRL-1620) cell lines. The ganglioside profile of the neuroblastoma cell line COG-N-683 showed an inverse relationship between GD1 and GD2. Ceramide, Hex1Cer, GM1, and GM3 were highly abundant in CRL-1620, PSN1, and MDA-MB-231BR, respectively. These results suggest that our method provides a sensitive, reliable, and high-throughput workflow for ganglioside profiling across different cell types. Full article

Introduction: Artificial intelligence (AI) is revolutionizing medical workflows, with self-learning systems like ChatGPT showing promise in therapy recommendations. Our study evaluated ChatGPT’s performance in suggesting treatments for 30 breast cancer cases. AI’s role in healthcare is expanding, particularly with tools like ChatGPT becoming accessible. However, understanding its limitations is vital for safe implementation. Material and Methods: We used 30 breast cancer cases from our medical board, assessing ChatGPT’s suggestions. The input was standardized, incorporating relevant patient details and treatment options. ChatGPT’s output was evaluated by oncologists based on a given questionnaire. Results: Treatment recommendations by ChatGPT were overall rated sufficient with minor limitations by the oncologists. The HER2 treatment category was the best-rated therapy option, with the most accurate recommendations. Primary cases received more accurate recommendations, especially regarding chemotherapy. Conclusions: While ChatGPT demonstrated potential, difficulties were shown in intricate cases and postoperative scenarios. Challenges arose in offering chronological treatment sequences and partially lacked precision. Refining inputs, addressing ethical intricacies, and ensuring chronological treatment suggestions are essential. Ongoing research is vital to improving AI’s accuracy, balancing AI-driven suggestions with expert insights and ensuring safe and reliable AI integration into patient care. Full article

Cancer is defined as the uncontrolled proliferation of heterogeneous cell cultures in the body that develop abnormalities and mutations, leading to their resistance to many forms of treatment. Left untreated, these abnormal cell growths can lead to detrimental and even fatal complications for patients. Radiation therapy is involved in around 50% of cancer treatment workflows; however, it presents significant recurrence rates and normal tissue toxicity, given the inevitable deposition of the dose to the surrounding healthy tissue. Chemotherapy is another treatment modality with excessive normal tissue toxicity that significantly affects patients’ quality of life. To improve the therapeutic efficacy of radiotherapy and chemotherapy, multiple conjunctive modalities have been proposed, which include the targeting of components of the tumour microenvironment inhibiting tumour spread and anti-therapeutic pathways, increasing the oxygen content within the tumour to revert the hypoxic nature of the malignancy, improving the local dose deposition with metal nanoparticles, and the restriction of the cell cycle within radiosensitive phases. The tumour microenvironment is largely responsible for inhibiting nanoparticle capture within the tumour itself and improving resistance to various forms of cancer therapy. In this review, we discuss the current literature surrounding the administration of molecular and nanoparticle therapeutics, their pharmacokinetics, and contrasting mechanisms of action. The review aims to demonstrate the advancements in the field of conjugated nanomaterials and radiotherapeutics targeting, inhibiting, or bypassing the tumour microenvironment to promote further research that can improve treatment outcomes and toxicity rates. Full article

Background: The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. Aim: To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. Methods: Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models’ performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. Result: Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. Conclusions: The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow. Full article