Cancer remains a formidable global health challenge due to its complex pathophysiology and resistance to conventional treatments. In recent years, the convergence of nanotechnology and oncology has paved the way for innovative therapeutic platforms that address the limitations of traditional modalities. This review examines how nanoparticle (NP)-based strategies enhance the efficacy of chemotherapy, radiotherapy, phototherapy, immunotherapy, and gene therapy by enabling targeted delivery, controlled drug release, and tumor-specific accumulation via the enhanced permeability and retention (EPR) effect. We discuss the design and functionalization of various organic, inorganic, and hybrid NPs, highlighting their roles in improving pharmacokinetics, overcoming multidrug resistance, and modulating the tumor microenvironment. Particular emphasis is placed on dual and multimodal therapies, such as chemo-phototherapy, chemo-immunotherapy, and gene-radiotherapy, that leverage nanoparticle carriers to amplify synergistic effects, minimize systemic toxicity, and improve clinical outcomes. We also explore cutting-edge advances in gene editing and personalized nanomedicine, as well as emerging strategies to address biological barriers and immunosuppressive mechanisms in the tumor niche. Despite the undeniable promise of nanoparticle-based cancer therapies, challenges related to toxicity, scalable manufacturing, regulatory oversight, and long-term biocompatibility must be overcome before they can fully enter clinical practice. By synthesizing recent findings and identifying key opportunities for innovation, this review provides insight into how nanoscale platforms are propelling the next generation of precision oncology.

Carbon‐based nanomaterials (CBNs), including graphene oxide (GO), carbon nanospheres (CNSs), carbon nitrides (CNs), carbon nanotubes (CNTs), carbon dots (CDs), nanoporous carbon, and nanocomposites, possess exceptional thermal, mechanical, electrical, and optical properties with highly versatile surface chemistries. Their tunable size, shape, and surface functionalities facilitate strong π–π interactions and semiconductor‐like behavior, enabling efficient light absorption and intimate biomolecular interfacing. These attributes have positioned CBNs as leading candidates in biomedical engineering, inspiring biomimetic designs that integrate organic and inorganic functions within unified architectures. This review highlights recent advances in the use of CBNs for next‐generation biosensing and theranostics. Approaches such as physicochemical engineering, deoxyribonucleic acid (DNA) origami templating, peptide‐ or enzyme‐assisted assembly, polysaccharide anchoring, and lipid modification have enhanced their biocompatibility, selectivity, and catalytic activity. Innovations in fluorescence switching, aptamer–CNT photophysics, and spacer‐controlled energy transfer enable ultra‐sensitive detection of metal ions, metabolites, neurotransmitters, pathogens, drugs, and cancer biomarkers. Furthermore, integrated CBN‐based platforms, including field‐effect transistors and laser‐scribed graphene electrodes, demonstrate capabilities for single‐virus or single‐cell diagnostics and responsive therapeutic intervention. Finally, translational challenges related to scalable synthesis, biosafety, regulatory harmonization, and public acceptance are discussed, and interdisciplinary strategies combining flexible electronics, organ‐on‐chip models, and AI‐guided design to advance clinical translation of CBN‐enabled precision diagnostics and personalized theranostics are proposed.

A new biosensor for detecting cancer involved sentinel lymph nodes has been developed via the electrochemical tracing of fatty acid oxidation as a distinct metabolism of malignant cells invading lymph nodes (LNs). The system included integrated platinum needle electrodes that were decorated by carbon nanotubes (as hydrophobic agents) through laser-assisted nanowelding. It was applied to record the dielectric spectroscopy data from LN contents via electrochemical impedance spectroscopy. The system was applied for dielectric spectroscopy of LN contents via electrochemical impedance approach. The reduced lipid content of involved LNs, due to fat metabolism by invasive cancer cells, would decrease the charge transfer resistance (R_CT) of the LNs with respect to their normal counterparts. Multi-walled carbon nanotubes (MWCNTs) with superhydrophobic properties were used to enhance the interaction of Pt needle electrodes with the lipidic contents of lymph nodes. This is the first time that a fatty acid metabolism-based sensing approach has been introduced to detect involved LNs. Moreover, a novel electrode decorating method was applied to enhance the interfacial contact of this lipid detection probe (LDP). In order to avoid doubt about the biocompatibility of ferrocyanide, [Fe(CN)₆]⁴⁻ and ferricyanide, [Fe(CN)₆]³⁻, a biocompatible injectable metal ion-based material, ferric carboxymaltose, was selected and applied as the electrolyte for the first time. Rabbit LNs were tested using the LDP in the animal model phase. The system was then used in vitro on 122 dissected human LNs in the operating room. Calibration of the results showed an excellent match between the dielectric response of the LDP (known as charge transfer resistance (R_CT)) and the final pathological diagnoses. The LDP may have a promising future after further clinical investigations for intra-operative distinction between normal and cancerous LNs.

Introduction Pancreatic cancer is thought to have an extremely dismal prognosis. Most cancer-related deaths occur from metastasis rather than the primary tumor, although individuals with tumors smaller than 1 cm in diameter have more than 80% 5-year survival. Thus, the current protocol introduces PanCanAID project which intends to develop several computer-aided-diagnosis (CAD) systems to enhance pancreatic cancer diagnosis and management using CT scan imaging. Methods and analysis Patients with pathologically confirmed pancreatic ductal adenocarcinoma (PDAC) or pancreatic neuroendocrine tumor (PNET) will be included as pancreatic cancer cases. The controls will be patients without CT evidence of abdominal malignancy. A data bank of contrast-enhanced abdominopelvic CT scans, survival data, and demographics will be collected from ten medical centers in four provinces. Endosonography images and clinical data, if available, will be added to the data bank. Annotation and manual segmentation will be handled by radiologists and confirmed by a second expert radiologist in abdominal imaging. PanCanAID intelligent system is designed to (1) detect abdominopelvic CT scan phase, (2) segment pancreas organ, (3) diagnose pancreatic cancer and its subtype in arterial phase CT scan, (4) diagnose pancreatic cancer and its subtype in non-contrast CT scan, (5) carry out prognosis (TNM stage and survival) based on arterial phase CT scan, (6) and estimate tumor resectability. A domain adaptation step will be handled to use online data and provide pancreas organ segmentation to reduce the segmentation time. After data collection, a state-of-the-art deep learning algorithm will be developed for each task and benchmarked against rival models. Conclusion PanCanAID is a large-scale, multidisciplinary AI project to assist clinicians in diagnosing and managing pancreas cancer. Here, we present the PanCanAID protocol to assure the quality and replicability of our models. In our experience, the effort to prepare a detailed protocol facilitates a positive interdisciplinary culture and the preemptive identification of errors before they occur.