Hasil untuk "Neoplasms. Tumors. Oncology. Including cancer and carcinogens"

Renske van Noortwijk, Petra S. Kroon, Katelijne M. van Vliet-van den Ende et al.

Background and purpose: Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by a brachytherapy (BT) boost. However, BT is not always feasible and magnetic resonance (MR)-guided adaptive radiotherapy on the MR-Linac (MRL) might be an alternative. To investigate the dosimetric feasibility of MRL, BT and MRL treatment plans were compared intra-patient in terms of dosimetric differences, next to anatomical and conformity variations. Materials and methods: Two groups of ten patients with LACC treated with BT boost were selected: group 1 included patients for which at least one clinically established (EMBRACE II) treatment planning constraint was not achieved during BT, in group 2 all planning constraints were achieved.BT treatment plans were compared with MRL treatment plans (based on MRI scans without applicator in place) intra-patient, in terms of dose-volume histogram (DVH) parameters, target-to-OAR (organ at risk) surface distances and conformity ratios. Results: Group 1 resulted in similar prescribed target dose levels for MRL compared to BT, for group 2 all prescribed target dose levels were significantly higher for BT. Rectum D2cm3 was higher for all MRL treatment plans. Volumes of higher dose levels were larger for BT, volumes of lower dose levels were larger for MRL and the CTVHR to OAR (rectum, sigmoid, bowel) surface distance was greater for BT. Conclusion: This retrospective study demonstrates that with an MRL boost plan, in some situations it is possible to achieve established planning constraints. However, as rectum doses are higher and dose distributions are fundamentally different, BT remains the modality of choice. Clinical trials are necessary to investigate the influence of the MRL dose distribution on oncological outcomes.

Bin Xue, Yifan Liu, Aibin Liang et al.

Abstract The field of chimeric antigen receptor (CAR)-T cell therapy is undergoing a paradigm shift from complex ex vivo manufacturing to direct in vivo generation of CAR-T cells. This innovative approach leverages non-viral delivery platforms to reprogram a patient’s own immune cells in situ, promising to overcome critical barriers of cost, scalability, and accessibility. The 2025 American Society of Hematology (ASH) Annual Meeting served as a showcase for groundbreaking preclinical data across a diverse array of non-viral technologies, including advanced lipid nanoparticles (LNPs), virus-like particles (VLPs), and polymeric nanoparticles. This correspondence summarizes the latest reports on these platforms, highlighting their potential to revolutionize the treatment of both autoimmune diseases and hematological malignancies.

Claire Chenwen Zhong, Zhaojun Li, Mingtao Chen et al.

ABSTRACT Introduction Low‐dose computed tomography (LDCT) enables early detection of lung cancer and reduces mortality, yet public willingness to undergo screening remains suboptimal. This study aimed to assess willingness and its associated factors among high‐risk individuals in Hong Kong. Methods A territory‐wide cross‐sectional survey was conducted among adults aged 54 years or above, and those aged 45–54 years with at least one lung cancer risk factor (e.g., smoking, secondhand smoke exposure, or family history) in Hong Kong. Data were collected via self‐administered questionnaires, which included socio‐demographic information, risk exposure, awareness and experience of LDCT, and constructs from the Health Belief Model (HBM). Logistic regression was performed to identify factors associated with willingness to undergo LDCT screening. Results A total of 1100 participants were included in the analysis. Among them, 57.3% expressed willingness to undergo LDCT within the next year. Multivariable logistic regression showed that higher self‐efficacy was the strongest factor of willingness, followed by greater perceived benefits and stronger cues to. Additional significant factors included being a current or former smoker, secondhand smoke exposure, age > 65 years, and being responsible for cooking at home. In contrast, unmarried individuals were significantly less likely to be willing to undergo LDCT (aOR = 0.678; 95% CI: 0.486–0.946; p = 0.022). Conclusion Willingness to undergo LDCT screening was suboptimal among high‐risk individuals in Hong Kong. Key facilitators included higher self‐efficacy, perceived benefits, and cues to action—central domains of the Health Belief Model. Targeted strategies that strengthen these domains may improve screening uptake.

Giovani Basso da Silva, Heloíse Benvenutti, Lucas Paulo de Souza et al.

Introdução: O letramento em saúde capacita as crianças a compreenderem e utilizarem informações sobre saúde. A utilização de estratégias educativas adaptadas facilita a compreensão de conteúdos complexos de saúde e contribui para o enfrentamento de doenças. Objetivo: Apresentar o processo de desenvolvimento de uma escala lúdica de letramento em saúde infantil, integrada a um jogo digital voltado para crianças com diagnóstico de câncer, com foco em leucemia linfoide aguda. Método: Estudo metodológico conduzido entre novembro de 2024 e maio de 2025, composto por cinco etapas principais: definição teórica dos domínios da escala, adaptação da linguagem para o público infantil, integração dos elementos avaliativos na estrutura narrativa do jogo, elaboração do banco de perguntas e planejamento da validação. A escala foi inserida em um jogo digital educativo, estruturado em capítulos e minigames, com perguntas lúdicas ao final para avaliação dos domínios do letramento em saúde. Resultados: A escala foi construída com base em quatro domínios principais: (1) Conhecimento sobre o câncer; (2) Tratamento e efeitos colaterais; (3) Autocuidado e hábitos saudáveis; e (4) Emoções e suporte. Foram desenvolvidas 12 perguntas objetivas integradas ao jogo Piratas das Estrelas. A avaliação do letramento ocorre de forma interativa por meio da narrativa do jogo e das escolhas feitas pela criança ao longo da experiência. Conclusão: A construção da escala integrada a um jogo digital representa uma estratégia inovadora para promover e avaliar o letramento em saúde de crianças com câncer. A proposta visa contribuir para o cuidado mais humanizado, compreensível e participativo.

Tian-Qi Zhang, Ji-Dong Cai, Cong Li et al.

Abstract Background The widespread application of colonoscopy screening and genetic testing in colorectal cancer (CRC) treatment has led to the identification of a subset of familial adenomatous polyposis (FAP) patients who lack a family history of the disease but harbor germline gene mutations. Moreover, distinct genotypes may be associated with varied clinical presentations and therapeutic options. This case report describes a male patient with de novo FAP who harbored germline double heterozygosity (GDH) for APC and BRCA2 mutations. The patient underwent total colectomy, and genetic testing enabled personalized surveillance and management strategies for his family members. Case presentation A 43-year-old male with no family history of cancer presented to the outpatient clinic of the Colorectal Surgery Department with complaints of constipation and hematochezia. Colonoscopy revealed hundreds of polyps throughout the colon and a rectal adenocarcinoma located 5 cm from the anal verge. Gastroduodenal endoscopy did not detect any upper gastrointestinal adenomas. The patient underwent laparoscopic total colectomy with abdominoperineal resection of the rectum and end ileostomy. With the consent of the patient and his family, genetic testing was performed. The index patient was found to carry an APC splicing site mutation (exon 15: c.1744-1G > A) and a BRCA2 missense mutation (exon 17: c.7976G > A: p.R2659K). His daughter was found to have inherited the same germline BRCA2 variant. Additionally, the rectal cancer exhibited proficient DNA mismatch repair (pMMR) status, ERBB2 copy number amplification, and a missense mutation, while the KRAS, NRAS, and BRAF genes were wild-type. Based on the genetic testing results and clinical manifestations, the index patient was diagnosed with familial adenomatous polyposis (FAP) and rectal cancer. Personalized surveillance and management strategies were implemented for the patient and his family, focusing on the risks of extra-colonic diseases and potential malignancies in the prostate, pancreas, breast, and ovaries. Conclusion De novo FAP with double germline mutations in APC and BRCA2, along with somatic ERBB2 mutations, is exceptionally rare among hereditary cancer cases. With the rapid advancements in genomics, the detection of multiple gene variants in individuals or families has become increasingly common. Additionally, the application of artificial intelligence (AI) in medical research may provide powerful tools for genetic analysis and clinical decision-making. Consequently, a comprehensive evaluation of family history, a deep understanding of hereditary cancer syndromes, and precise interpretation of genetic mutations are essential for personalized clinical management in the era of precision medicine. However, these tasks pose significant challenges for clinicians and genetic counselors alike.

Priyanka Avhad, Vedanti Kshirsagar, Urvi Ranjan et al.

According to the data, the percent of women who underwent screening for cervical cancer, breast and oral cancer in Telangana in the year 2020 was 3.3 percent, 0.3 percent and 2.3 percent respectively. Although early detection is the only way to reduce morbidity and mortality, people have very low awareness about cervical and breast cancer signs and symptoms and screening practices. We developed an ML classification model to predict if a person is susceptible to breast or cervical cancer based on demographic factors. We devised a system to provide suggestions for the nearest hospital or Cancer treatment centres based on the users location or address. In addition to this, we can integrate the health card to maintain medical records of all individuals and conduct awareness drives and campaigns. For ML classification models, we used decision tree classification and support vector classification algorithms for cervical cancer susceptibility and breast cancer susceptibility respectively. Thus, by devising this solution we come one step closer to our goal which is spreading cancer awareness, thereby, decreasing the cancer mortality and increasing cancer literacy among the people of Telangana.

Gabriela Fernandes

Ovarian cancer remains one of the most lethal gynecological malignancies, largely due to late diagnosis and extensive heterogeneity across subtypes. Current diagnostic methods are limited in their ability to reveal underlying genomic variations essential for precision oncology. This study introduces a novel hybrid deep learning pipeline that integrates quantitative nuclear morphometry with deep convolutional image features to perform ovarian cancer subtype classification and gene mutation inference directly from Hematoxylin and Eosin (H&E) histopathological images. Using $\sim45,000$ image patches sourced from The Cancer Genome Atlas (TCGA) and public datasets, a fusion model combining a ResNet-50 Convolutional Neural Network (CNN) encoder and a Vision Transformer (ViT) was developed. This model successfully captured both local morphological texture and global tissue context. The pipeline achieved a robust overall subtype classification accuracy of $84.2\%$ (Macro AUC of $0.87 \pm 0.03$). Crucially, the model demonstrated the capacity for gene mutation inference with moderate-to-high accuracy: $AUC_{TP53} = 0.82 \pm 0.02$, $AUC_{BRCA1} = 0.76 \pm 0.04$, and $AUC_{ARID1A} = 0.73 \pm 0.05$. Feature importance analysis established direct quantitative links, revealing that nuclear solidity and eccentricity were the dominant predictors for TP53 mutation. These findings validate that quantifiable histological phenotypes encode measurable genomic signals, paving the way for cost-effective, precision histopathology in ovarian cancer triage and diagnosis.

Sepinoud Azimi, Louise Spekking, Kateřina Staňková

Personalized cancer treatment is revolutionizing oncology by leveraging precision medicine and advanced computational techniques to tailor therapies to individual patients. Despite its transformative potential, challenges such as limited generalizability, interpretability, and reproducibility of predictive models hinder its integration into clinical practice. Current methodologies often rely on black-box machine learning models, which, while accurate, lack the transparency needed for clinician trust and real-world application. This paper proposes the development of an innovative framework that bridges Kolmogorov-Arnold Networks (KANs) and Evolutionary Game Theory (EGT) to address these limitations. Inspired by the Kolmogorov-Arnold representation theorem, KANs offer interpretable, edge-based neural architectures capable of modeling complex biological systems with unprecedented adaptability. Their integration into the EGT framework enables dynamic modeling of cancer progression and treatment responses. By combining KAN's computational precision with EGT's mechanistic insights, this hybrid approach promises to enhance predictive accuracy, scalability, and clinical usability.

Noble Harasha, Nancy Lynch

Motile nanosized particles, or "nanobots", promise more effective and less toxic targeted drug delivery because of their unique scale and precision. We consider the case in which the cancer is "diffuse", dispersed such that there are multiple distinct cancer sites. We investigate the problem of a swarm of nanobots locating these sites and treating them by dropping drug payloads at the sites. To improve the success of the treatment, the drug payloads must be allocated between sites according to their "demands"; this requires extra nanobot coordination. We present a mathematical model of the behavior of the nanobot agents and of their colloidal environment. This includes a movement model for agents based upon experimental findings from actual nanoparticles in which bots noisily ascend and descend chemical gradients. We present three algorithms: The first algorithm, called KM, is the most representative of reality, with agents simply following naturally existing chemical signals that surround each cancer site. The second algorithm, KMA, includes an additional chemical payload which amplifies the existing natural signals. The third algorithm, KMAR, includes another additional chemical payload which counteracts the other signals, instead inducing negative chemotaxis in agents such that they are repelled from sites that are already sufficiently treated. We present simulation results for all algorithms across different types of cancer arrangements. For KM, we show that the treatment is generally successful unless the natural chemical signals are weak, in which case the treatment progresses too slowly. For KMA, we demonstrate a significant improvement in treatment speed but a drop in eventual success, except for concentrated cancer patterns. For KMAR, our results show great performance across all types of cancer patterns, demonstrating robustness and adaptability.

Jiahe Wang, Yan Wu, Yuke Hou et al.

Cancer is a complex disease driven by dynamic regulatory shifts that cannot be fully captured by individual molecular profiling. We employ a data-driven approach to construct a coarse-grained dynamic network model based on hallmark interactions, integrating stochastic differential equations with gene regulatory network data to explore key macroscopic dynamic changes in tumorigenesis. Our analysis reveals that network topology undergoes significant reconfiguration before hallmark expression shifts, serving as an early indicator of malignancy. A pan-cancer examination across $15$ cancer types uncovers universal patterns, where Tissue Invasion and Metastasis exhibits the most significant difference between normal and cancer states, while the differences in Reprogramming Energy Metabolism are the least pronounced, consistent with the characteristic features of tumor biology. These findings reinforce the systemic nature of cancer evolution, highlighting the potential of network-based systems biology methods for understanding critical transitions in tumorigenesis.

Xing-long Wang, He-nan Wang, Lei Yang et al.

BackgroundCurrently, there is no standard treatment for relapsed/refractory NK/T-cell lymphoma (NKTCL). Liposomal mitoxantrone (Lipo-MIT) showed good anti-tumor effect in patients with NKTCL, breaking the limitation of natural resistance of NKTCL to anthracyclines. To further improve the efficacy, we tried a combination therapy based on Lipo-MIT in patients with relapsed/refractory NKTCL.Methods12 patients with relapsed/refractory NKTCL were enrolled in this retrospective study, all of whom had previously received pegaspargase-based treatments. The salvage treatment was a combination regimen based on Lipo-MIT. The efficacy was evaluated after every two cycles.Results11 patients had stage IV NKTCL, and all but one patients had an NRI score of ≥3. The median previous lines of treatment was two (range, 1–4), and five patients were refractory to their last line of treatment. The best response rates were as follows: complete response (CR) in five (41.7%) patients, partial response in five (41.7%) patients, stable disease in one (8.3%) patient, and progressive disease in one (8.3%) patient. At a median follow-up of four months (range, 2–14), seven patients died, with a median PFS of five months and a median OS of seven months. The six-month PFS and OS rate was 44.4% and 52.1%, respectively. All patients had suffered from side effects, among which myelosuppression was most reported. Nine patients had grade three or more myelosuppression, and the median recovery time from myelosuppression was 14 days (2–35 days). Five patients had obvious skin hyperpigmentation, and the CR rate was significantly higher compared with those without skin hyperpigmentation (80% vs. 14.3%, p=0.023). Other side effects included liver insufficiency (N=4), coagulation dysfunction (N=4), acute pancreatitis (N=2), and immunotherapy-related adverse effects (irAEs, N=2).ConclusionCombination therapy based on Lipo-MIT has a high remission rate for relapsed/refractory NKTCL, but the duration of remission needs to be further extended. Lipo-MIT has obvious myelosuppression toxicity, and active supportive therapy should be given when combined with other cytotoxic drugs.

Doah Cho, Sarah J. Lord, Robyn Ward et al.

Background: Advances in targeted therapy development and tumor sequencing technology are reclassifying cancers into smaller biomarker-defined diseases. Randomized controlled trials (RCTs) are often impractical in rare diseases, leading to calls for single-arm studies to be sufficient to inform clinical practice based on a strong biological rationale. However, without RCTs, favorable outcomes are often attributed to therapy but may be due to a more indolent disease course or other biases. When the clinical benefit of targeted therapy in a common cancer is established in RCTs, this benefit may extend to rarer cancers sharing the same biomarker. However, careful consideration of the appropriateness of extending the existing trial evidence beyond specific cancer types is required. A framework for extrapolating evidence for biomarker-targeted therapies to rare cancers is needed to support transparent decision-making. Objectives: To construct a framework outlining the breadth of criteria essential for extrapolating evidence for a biomarker-targeted therapy generated from RCTs in common cancers to different rare cancers sharing the same biomarker. Design: A series of questions articulating essential criteria for extrapolation. Methods: The framework was developed from the core topics for extrapolation identified from a previous scoping review of methodological guidance. Principles for extrapolation outlined in guidance documents from the European Medicines Agency, the US Food and Drug Administration, and Australia’s Medical Services Advisory Committee were incorporated. Results: We propose a framework for assessing key assumptions of similarity of the disease and treatment outcomes between the common and rare cancer for five essential components: prognosis of the biomarker-defined cancer, biomarker test analytical validity, biomarker actionability, treatment efficacy, and safety. Knowledge gaps identified can be used to prioritize future studies. Conclusion: This framework will allow systematic assessment, standardize regulatory, reimbursement and clinical decision-making, and facilitate transparent discussions between key stakeholders in drug assessment for rare biomarker-defined cancers.

Shixin Ma, Zongqi Li, Lunqing Wang

Abstract Objective To investigate the prognostic significance of the advanced lung cancer inflammation index (ALI) in patients with operable non-small-cell lung carcinoma (NSCLC). By constructing the nomogram model, it can provide a reference for clinical work. Methods A total of 899 patients with non-small cell lung cancer who underwent surgery in our hospital between January 2017 and June 2021 were retrospectively included. ALI was calculated by body mass index (BMI) × serum albumin/neutrophil to lymphocyte ratio (NLR). The optimal truncation value of ALI was obtained using the receiver operating characteristic (ROC) curve and divided into two groups. Survival analysis was represented by the Kaplan-Meier curve. The predictors of Overall survival (OS) were evaluated by the Cox proportional risk model using single factor and stepwise regression multifactor analysis. Based on the results of multi-factor Cox proportional risk regression analysis, a nomogram model was established using the R survival package. The bootstrap method (repeated sampling 1 000 times) was used for internal verification of the nomogram model. The concordance index (C-index) was used to represent the prediction performance of the nomogram model, and the calibration graph method was used to visually represent its prediction conformity. The application value of the model was evaluated by decision curve analysis (DCA). Results The optimal cut-off value of ALI was 70.06, and the low ALI group (ALI < 70.06) showed a poor survival prognosis. In multivariate analyses, tumor location, pathological stage, neuroaggression, and ALI were independently associated with operable NSCLC-specific survival. The C index of OS predicted by the nomogram model was 0.928 (95% CI: 0.904–0.952). The bootstrap self-sampling method (B = 1000) was used for internal validation of the prediction model, and the calibration curve showed good agreement between the prediction and observation results of 1-year, 2-year, and 3-year OS. The ROC curves for 1-year, 2-year, and 3-year survival were plotted according to independent factors, and the AUC was 0.952 (95% CI: 0.925–0.979), 0.951 (95% CI: 0.916–0.985), and 0.939 (95% CI: 0.913–0.965), respectively. DCA shows that this model has good clinical application value. Conclusion ALI can be used as a reliable indicator to evaluate the prognosis of patients with operable NSCLC, and through the construction of a nomogram model, it can facilitate better individualized treatment and prognosis assessment.

Hongkang Song, Zihui Zhang, Yanpeng Zhou et al.

Spinal cord tumors significantly contribute to neurological morbidity and mortality. Precise morphometric quantification, encompassing the size, location, and type of such tumors, holds promise for optimizing treatment planning strategies. Although recent methods have demonstrated excellent performance in medical image segmentation, they primarily focus on discerning shapes with relatively large morphology such as brain tumors, ignoring the challenging problem of identifying spinal cord tumors which tend to have tiny sizes, diverse locations, and shapes. To tackle this hard problem of multiclass spinal cord tumor segmentation, we propose a new method, called BATseg, to learn a tumor surface distance field by applying our new multiclass boundary-aware loss function. To verify the effectiveness of our approach, we also introduce the first and large-scale spinal cord tumor dataset. It comprises gadolinium-enhanced T1-weighted 3D MRI scans from 653 patients and contains the four most common spinal cord tumor types: astrocytomas, ependymomas, hemangioblastomas, and spinal meningiomas. Extensive experiments on our dataset and another public kidney tumor segmentation dataset show that our proposed method achieves superior performance for multiclass tumor segmentation.

Anitha Ramakrishnan, Indrani Datta, Sukanya Panja et al.

IntroductionChronological aging is a well-recognized diagnostic and prognostic factor in multiple cancer types, yet the role of biological aging in manifesting cancer progression has not been fully explored yet.MethodsGiven the central role of chronological aging in prostate cancer and AML incidence, here we investigate a tissue-specific role of biological aging in prostate cancer and AML progression. We have employed Cox proportional hazards modeling to associate biological aging genes with cancer progression for patients from specific chronological aging groups and for patients with differences in initial cancer aggressiveness.ResultsOur prostate cancer-specific investigations nominated four biological aging genes (CD44, GADD45B, STAT3, GFAP) significantly associated with time to disease progression in prostate cancer in Taylor et al. patient cohort. Stratified survival analysis on Taylor dataset and validation on an independent TCGA and DKFZ PRAD patient cohorts demonstrated ability of these genes to predict prostate cancer progression, especially for patients with higher Gleason score and for patients younger than 60 years of age. We have further tested the generalizability of our approach and applied it to acute myeloid leukemia (AML). Our analysis nominated three AML-specific biological aging genes (CDC42EP2, CDC42, ALOX15B) significantly associated with time to AML overall survival, especially for patients with favorable cytogenetic risk score and for patients older than 56 years of age.DiscussionComparison of the identified PC and AML markers to genes selected at random and to known markers of progression demonstrated robustness of our results and nominated the identified biological aging genes as valuable markers of prostate cancer and AML progression, opening new avenues for personalized therapeutic management and potential novel treatment investigations.

Yuhao Zhou, Fei Li, Bangyu Zou et al.

Abstract Purpose To investigate the influence of β-arrestin2 on the docetaxel resistance in castration-resistant prostate cancer (CRPC) and elucidate the underlying molecular mechanisms. Methods PC3 and DU145 cells with stable β-arrestin2 overexpression and C4-2 cells with stable β-arrestin2 knockdown, were constructed via using lentivirus and puromycin selection. MTT and colony formation assays were carried out to investigate the effect of β-arrestin2 expression on the docetaxel resistance of CRPC cells. Glycolysis analysis was used to assess the glycolytic capacity modulated by β-arrestin2. GO enrichment analysis, gene set enrichment analysis and Spearman correlation test were carried out to explore the potential biological function and mechanism via using public data from GEO and TCGA. The expressions of PKM2, Phospho-PKM2, Phospho-ERK1/2 and hnRNP A1 were detected by western blot. Functional blocking experiments were carried out to confirm the roles of PKM2 and hnRNP A1 in the regulation of β-arrestin2’s biological functions via silencing PKM2 or hnRNP A1 expression in cells with stable β-arrestin2 overexpression. Finally, nude mice xenograft models were established to confirm the experimental results of cell experiments. Results β-Arrestin2 significantly decreased the sensitivity of CRPC cells to docetaxel stimulation, through enhancing the phosphorylation and expression of PKM2. Additionally, β-arrestin2 increased PKM2 phosphorylation via the ERK1/2 signaling pathway and induced PKM2 expression in a post-transcriptional manner through an hnRNP A1-dependent PKM alternative splicing mechanism, rather than by inhibiting its ubiquitination degradation. Conclusion Our findings indicate that the β-arrestin2/hnRNP A1/PKM2 pathway could be a promising target for treating docetaxel-resistant CRPC.

Jhonalbert Aponte, Álvaro Ruiz, Jacksson Sánchez et al.

Tumor vascularity detection and quantification are of high relevance in the assessment of cancer lesions not only for disease diagnostics but for therapy considerations and monitoring. The present work addressed the quantification of pharmacokinetic parameters derived from the two-compartment Brix model by analyzing and processing Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI) of prostate cancer lesions. The 3D image sets were acquired at regular time intervals, covering all the phases implied in contrast injection (wash-in and wash-out phases), and the standardized image intensity is determined for each voxel, conforming to a 4D data set. Previous voxel classification was carried out by the three-time-point method proposed by Degani et al. (1997) and Furman-Haran et al. (1998) to identify regions of interest. Relevant pharmacokinetic parameters, such as kel, the vascular elimination rate, and kep, the extravascular transfer rate, are extracted by a novel interpolation method applicable to compartment models. Parameter distribution maps were obtained for either pathological or unaffected glandular regions indicating that a three-compartment model, including fast and slow exchange compartments, provides a more suitable description of the contrast kinetics. Results can be applied to prostate cancer diagnostic evaluation and therapy follow-up.

Giacomo Lancia, Claudio Durastanti, Cristian Spitoni et al.

An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming clear that physical properties, including structural conformation, stiffness, and shape, as well as biological processes, such as methylation, can be pivotal to recognize DNA modifications. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated--silicon nanowires, to investigate genomic DNA from subtypes of melanoma and colon cancers and to efficiently discriminate tumor and healthy cells, as well as the different tumor subtypes. The diagnostic information is obtained by performing label--free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat, and leveraging the classification ability of learning models to reveal the specific and distinct interaction of healthy and tumor DNA molecules with nanowires.

Yue Wang

Cancer, as the uncontrollable cell growth, is related to many branches of biology. In this review, we will discuss three mathematical approaches for studying cancer biology: population dynamics, gene regulation, and developmental biology. If we understand all biochemical mechanisms of cancer cells, we can directly calculate how the cancer cell population behaves. Inversely, just from the cell count data, we can use population dynamics to infer the mechanisms. Cancer cells emerge from certain genetic mutations, which affect the expression of other genes through gene regulation. Therefore, knowledge of gene regulation can help with cancer prevention and treatment. Developmental biology studies acquisition and maintenance of normal cellular function, which is inspiring to cancer biology in the opposite direction. Besides, cancer cells implanted into an embryo can differentiate into normal tissues, which provides a possible approach of curing cancer. This review illustrates the role of mathematics in these three fields: what mathematical models are used, what data analysis tools are applied, and what mathematical theorems need to be proved. We hope that applied mathematicians and even pure mathematicians can find meaningful mathematical problems related to cancer biology.

Navid Mohammad Mirzaei, Leili Shahriyari

Computational modeling of cancer can help unveil dynamics and interactions that are hard to replicate experimentally. Thanks to the advancement in cancer databases and data analysis technologies, these models have become more robust than ever. There are many mathematical models which investigate cancer through different approaches, from sub-cellular to tissue scale, and from treatment to diagnostic points of view. In this study, we lay out a step-by-step methodology for a data-driven mechanistic model of the tumor microenvironment. We discuss data acquisition strategies, data preparation, parameter estimation, and sensitivity analysis techniques. Furthermore, we propose a possible approach to extend mechanistic ODE models to PDE models coupled with mechanical growth. The workflow discussed in this article can help understand the complex temporal and spatial interactions between cells and cytokines in the tumor microenvironment and their effect on tumor growth.