Precise Cancer Diagnosis Research Articles

Machine learning framework for breast cancer detection with feature selection with L2 ridge regularization: insights from multiple datasets

Aim: This study aims to investigate and apply effective machine learning techniques for the early detection and precise diagnosis of breast cancer. The analysis is conducted using various breast cancer datasets, including Breast Cancer Wisconsin, Breast Cancer Diagnosis, NKI Breast Cancer, and SEER Breast Cancer datasets. The primary focus is on identifying key features and utilizing preprocessing methods to enhance classification accuracy. Methods: The datasets undergo several preprocessing steps, such as label encoding for categorical variables, linear regression for handling missing values, and Robust scaler normalization for data standardization. To address class imbalance, Tomek Link SMOTE is employed to improve dataset representation. Significant features are selected through L2 Ridge regularization, helping to pinpoint the most important predictors of breast cancer. A range of machine learning models, including decision tree, random forest, support vector machine (SVM), neural network, K-nearest neighbor, naïve bayes, extreme gradient boost (XGBoost), and AdaBoost, are applied for classification tasks. The performance of these models is assessed using metrics such as accuracy, precision, recall, F1-score, and the Kappa statistic. Additionally, the models' effectiveness is further evaluated using the receiver operating characteristic curve and precision-recall curve. Results: The XGBoost model achieved the best performance on both the breast cancer Wisconsin and diagnosis datasets. The SVM model reached 100% accuracy on the NKI breast cancer dataset, while the random forest model performed optimally on the SEER breast cancer dataset. The feature selection process through L2 Ridge regularization was crucial in enhancing the performance of these models. Conclusions: This work emphasizes the critical role of machine learning in improving breast cancer detection. By applying a combination of preprocessing techniques and classification models, the study successfully identifies significant features and boosts model performance. These findings contribute to the development of more accurate diagnostic tools, ultimately enhancing patient outcomes.

An improved soft voting-based machine learning technique to detect breast cancer utilizing effective feature selection and SMOTE-ENN class balancing

Breast cancer is the primary cause of death among women globally, and it is becoming more prevalent. Early detection and precise diagnosis of breast cancer can reduce the disease’s mortality rate. Recent advances in machine learning have benefited in this regard. However, if the dataset contains duplicate or irrelevant features, machine learning-based algorithms are unable to give the intended results. To address this issue, a series of effective strategies such as the Robust Scaler is used for data scaling, Synthetic Minority Over-sampling Technique-Edited Nearest Neighbor (SMOTE-ENN) is utilized for class balancing, and Boruta and Coefficient-Based Feature Selection (CBFS) methods are employed for feature selection. For more accurate and reliable breast cancer classification, this study proposes a soft voting-based ensemble model that harnesses the capabilities of three diverse classifiers: Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). To show the efficiency of the proposed ensemble model, it is compared with its base classifiers using the Wisconsin Diagnosis Breast Cancer Dataset (WDBC). The results of the experiment revealed that the soft voting classifier achieved high scores with an accuracy of 99.42%, precision of 100.0%, recall of 98.41%, F1 score of 99.20%, and AUC of 1.0 when it is trained on optimal features obtained from the CBFS method. However, with tenfold cross-validation (10-FCV), it shows a mean accuracy score of 99.34%. A comprehensive analysis of the results revealed that the suggested technique outperformed the existing state-of-the-art methods due to the efficient data preprocessing, feature selection, and ensemble methods.

Zero-Crosstalk Tumor-Targeting Ratiometric Near-Infrared γ-Glutamyltranspeptidase Probe for Fluorescent-Guided Surgical Resection of Orthotopic Hepatic Tumor.

The challenge of "false positive" signals significantly complicates tumor localization and surgical resection, which are pivotal for successful tumor surgeries. Therefore, the development of a method for preoperative tumor localization and intraoperative margin determination holds considerable promise for improving surgical outcomes. In this study, a zero-crosstalk ratiometric tumor-targeting near-infrared (NIR) fluorescent probe was developed for precise cancer diagnosis and intraoperative navigation via NIR fluorescence imaging. This probe integrates a tumor-targeting moiety that selectively homes in on hepatocellular carcinoma cells and exhibits a highly sensitive ratiometric response to γ-glutamyltranspeptidase (GGT), characterized by a substantial emission shift from 830 to 650 nm. This unique NIR ratiometric emission property significantly enhances its effectiveness in early diagnosis and imaging-guided surgery resection. Furthermore, this zero-crosstalk probe successfully monitors GGT activity in blood, cells, and in vivo, endowing its potential for early cancer diagnosis in the clinic. Due to its efficient targeting and sensitive in situ response to GGT in both subcutaneous tumors and orthotopic hepatic tumors, the probe exhibits accurate detection capability for hepatic tumors. Additionally, by leveraging zero-crosstalk ratiometric NIR fluorescence imaging, this tumor-targeting probe can serve as a sprayable tool for delineating tumor margins with precision, thereby furnishing real-time intraoperative imaging guidance during tumor resection surgery.

A dual stimulus-responsive NIR-II photoacoustic probe for precise diagnosis of colon cancer and photothermal-starvation synergistic therapy

A dual stimulus-responsive NIR-II photoacoustic probe for precise diagnosis of colon cancer and photothermal-starvation synergistic therapy

PSMA-Targeted Intracellular Self-Assembled Probe for Enhanced PET Imaging.

Positron-emission tomography (PET) offers high sensitivity for cancer diagnosis. However, small-molecule-based probes often exhibit insufficient accumulation in tumor sites, while nanoparticle-based agents typically have limited delivery efficiency. To address this challenge, this study proposes a novel PET imaging probe, 68Ga-CBT-PSMA, designed for prostate cancer. This probe integrates an intracellular self-assembly strategy to enhance PET imaging signals and significantly improve the signal-to-noise ratio. The glutamate-urea-based prostate-specific membrane antigen (PSMA)-targeting motif enables specific recognition of prostate cancer cells and enhances cellular uptake; then the self-assembly process induced by glutathione reduction effectively accumulates the probe within tumor cells, thereby amplifying PET imaging signals. This approach not only enhances signal intensity and resolution but also facilitates precise cancer localization and diagnosis, offering new avenues for advancing cancer diagnostic techniques.

MXene-based SERS spectroscopic analysis of exosomes for lung cancer differential diagnosis with deep learning.

Lung cancer with heterogeneity has a high mortality rate due to its late-stage detection and chemotherapy resistance. Liquid biopsy that discriminates tumor-related biomarkers in body fluids has emerged as an attractive technique for early-stage and accurate diagnosis. Exosomes, carrying membrane and cytosolic information from original tumor cells, impart themselves endogeneity and heterogeneity, which offer extensive and unique advantages in the field of liquid biopsy for cancer differential diagnosis. Herein, we demonstrate a Gramian angular summation field and MobileNet V2 (GASF-MobileNet)-assisted surface-enhanced Raman spectroscopy (SERS) technique for analyzing exosomes, aimed at precise diagnosis of lung cancer. Specifically, a composite substrate was synthesized for SERS detection of exosomes based on Ti3C2Tx Mxene and the array of gold-silver core-shell nanocubes (MGS), that combines sensitivity and signal stability. The employment of MXene facilitates the non-selective capture and enrichment of exosomes. To overcome the issue of potentially overlooking spatial features in spectral data analysis, 1-D spectra were first transformed into 2-D images through GASF. By using transformed images as the input data, a deep learning model based on the MobileNet V2 framework extracted spectral features from higher dimensions, which identified different non-small cell lung cancer (NSCLC) cell lines with an overall accuracy of 95.23%. Moreover, the area under the curve (AUC) for each category exceeded 0.95, demonstrating the great potential of integrating label-free SERS with deep learning for precise lung cancer differential diagnosis. This approach allows routine cancer management, and meanwhile, its non-specific analysis of SERS signatures is anticipated to be expanded to other cancers.

A Pair of Fluorescent Probes Enabling Precise Diagnosis of Liver Cancer by Complementary Imaging.

Hepatocellular carcinoma (HCC) is by far the predominant malignant liver cancer, with both high morbidity and mortality. Early diagnosis and surgical resections are imperative for improving the survival of HCC patients. However, limited by clinical diagnosis methods, it is difficult to accurately distinguish tumor tissue and its boundaries in the early stages of cancer. Herein, we report two fluorescent probes, cLG and hLR, for the detection of cancer and healthy cells, respectively, enabling the precise diagnosis of liver cancer by providing complementary imaging. These two fluorescent probes could selectively stain the target cells in the liver tissue imaging, which is confirmed by H&E and antibody staining. Moreover, for the first time, the cancerous area and healthy area are clearly identified by the cocktail of these two probes, suggesting its potential to be used in fluorescence-guided surgery. Finally, we identify transporter SLC27A2 as the gating target of cLG through a systematic transporter screen using a CRISPR activation library. SMPD1 was identified as the target of hLR through a thermal proteome profiling. Therefore, the development of these two highly specific probes offers complementary imaging and provides a unique diagnostic tool for cancer disease, even for fluorescence-guided surgery.

A Novel Approach in Cancer Diagnosis: Integrating Holography Microscopic Medical Imaging and Deep Learning Techniques - Challenges and Future Trends.

Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ultrasound, and Positron Emission Tomography (PET), offer vital insights into three-dimensional structures but frequently fall short of delivering a comprehensive and detailed anatomical analysis, capturing only amplitude details. Three-dimensional holography microscopic medical imaging provides a promising solution by capturing the amplitude (brightness) and phase (structural information) details of biological structures. In this study, we investigate the novel collaborative potential of Deep Learning (DL) and holography microscopic phase imaging for cancer diagnosis. The study comprehensively examines existing literature, analyzes advancements, identifies research gaps, and proposes future research directions in cancer diagnosis through the integrated Quantitative Phase Imaging (QPI) and DL methodology. This novel approach addresses a critical limitation of traditional imaging by capturing detailed structural information, paving the way for more accurate diagnostics. The proposed approach comprises tissue sample collection, holographic image scanning, pre-processing in case of imbalanced datasets, and training on annotated datasets using DL architectures like U-Net and Vision Transformer(ViT's). Furthermore, sophisticated concepts in DL, like the incorporation of Explainable AI techniques (XAI), are suggested for comprehensive disease diagnosis and identification. The study thoroughly investigates the advantages of integrating holography imaging and DL for precise cancer diagnosis. Additionally, meticulous insights are presented by identifying the challenges associated with this integration methodology.

Electrochemical Biosensors for Cancer Diagnosis: Multitarget Analysis to Present Molecular Characteristics of Tumor Heterogeneity.

Electrochemical biosensors are gaining attention as powerful tools in cancer diagnosis, particularly in liquid biopsy, due to their high efficiency, rapid response, exceptional sensitivity, and specificity. However, the complexity of intra- and intertumor heterogeneity, with variations in genetic and protein expression profiles and epigenetic modifications, makes electrochemical biosensors susceptible to false-positive or false-negative diagnostic outcomes. To address this challenge, there is growing interest in simultaneously analyzing multiple biomarkers to reveal molecular characteristics of tumor heterogeneity for precise cancer diagnosis. In this Perspective, we highlight recent advancements in utilizing electrochemical biosensors for cancer diagnosis, with a specific emphasis on the multitarget analysis of cancer biomarkers including tumor-associated nucleic acids, tumor protein markers, extracellular vesicles, and tumor cells. These biosensors hold significant promise for improving precision in early cancer diagnosis and monitoring, as well as potentially offering new insights into personalized cancer management.

Breast histopathological imaging using ultra-fast fluorescence confocal microscopy to identify cancer lesions at early stage.

Ultrafast fluorescent confocal microscopy is a hypothetical approach for breast cancer detection because of its potential to achieve instantaneous, high-resolution images of cellular-level tissue features. Traditional approaches such as mammography and biopsy are laborious, invasive, and inefficient; confocal microscopy offers many benefits over these approaches. However, confocal microscopy enables the exact differentiation of malignant cells, the expeditious examination of extensive tissue sections, and the optical sectioning of tissue samples into tiny slices. The primary goal should be to prevent cancer altogether, although detecting it early can help achieve that objective. This research presents a novel Breast Histopathology Convolutional Neural Network (BHCNN) for feature extraction and recursive feature elimination method for selecting the most significant features. The proposed approach utilizes full slide images to identify tissue in regions affected by invasive ductal carcinoma. In addition, a transfer learning approach is employed to enhance the performance and accuracy of the models in detecting breast cancer, while also reducing computation time by modifying the final layer of the proposed model. The results showed that the BHCNN model outperformed other models in terms of accuracy, achieving a testing accuracy of 98.42% and a training accuracy of 99.94%. The confusion matrix results show that the IDC positive (+) class achieved 97.44% accuracy and 2.56% inaccurate results, while the IDC negative (-) class achieved 98.73% accuracy and 1.27% inaccurate results. Furthermore, the model achieved less than 0.05 validation loss. RESEARCH HIGHLIGHTS: The objective is to develop an innovative framework using ultra-fast fluorescence confocal microscopy, particularly for the challenging problem of breast cancer diagnosis. This framework will extract essential features from microscopy and employ a gradient recurrent unit for detection. The proposed research offers significant potential in enhancing medical imaging through the provision of a reliable and resilient system for precise diagnosis of breast cancer, thereby propelling the progression of state-of-the-art medical technology. The most suitable feature was determined using BHRFE optimization techniques after retrieving the features by proposed model. Finally, the features chosen are integrated into a proposed methodology, which is then classified using a GRU deep model. The aforementioned research has significant potential to improve medical imaging by providing a complex and reliable system for precise evaluation of breast cancer, hence advancing the development of cutting-edge medical technology.

Abstract A029: Novel high-purity CTC isolation technology for downstream analysis: A “Cell- Circuit” magnetophoretic-microfluidic hybrid device

Abstract Circulating Tumor Cells are the only blood analytes that contain comprehensive multi-omic information regarding living tumor cells. As such, CTCs are an essential tool for the precise diagnosis of cancer. This has led to increased interest in both enumeration, and more significantly, the downstream analysis of bulk and single CTCs. One primary technical limitation that limits adoption of CTCs into routine clinical practice is the ability to effectively isolate CTCs from non-target cells. While several technologies have been developed and commercialized including initial clinical adoption, most technologies have fundamental limitations in sample purity which limits the quality of downstream analysis. Another limitation is the number of steps needed to complete enrichment, especially in high purity isolation where 2 techniques are usually required to be performed in sequence. The authors propose a novel, single-cell isolation technology called L:Cell™. This technology utilizes precise micro-magnetic patterns to allow immuno-magnetically labeled cells to be isolated individually and directed to user-defined locations. Such “Cell-Circuits” enable parallel, computer-circuit-like manipulation of a large array of single cells – combining both precision and throughput. Passive and active single-cell control is made possible by circuit components such as gates, single-cell capacitors, and transistors. The technology also enables the classification of cells based on size in addition to the presence of magnetic beads. A "passive component" performs its function under an external rotating magnetic field, manipulating cells without the need for an electrical current. The majority of circuitry on-chip are such passive components, which enable the device's key advantage of parallelized control. In contrast, an "active component" is designed in conjunction with passive components to create a local magnetic field at specific locations. This allows cells to be rerouted or manipulated on-demand. Using active components, users can selectively isolate individual cells (e.g., based on size, morphology, or fluorescence). While micro-magnetophoretic technology has been explored by various research teams, the authors have advanced this technology further by micro-magnetic simulation and integrating microfluidic technology. This integration improves the inlet and outlet structures for cell samples and the alignment of particles’ flow, thereby maximizing the commercialization potential of this sophisticated technology. In lab-scale modeling, the authors demonstrated a high separation purity level of 92%. By allowing manipulation at single-cell level using magnetic fields only, the technology minimizes any stress on the cells during isolation processes. The authors developed a user- friendly interface utilizing microfluidic channels for the extraction of CTCs from whole blood using this hybrid device. Additionally, the authors are conducting further adaptation studies to apply this technology to various downstream techniques, such as genomic analysis and protein profiling. Citation Format: Chanhee Lee, Hun Heo, Youngwoo Park, Donghee Ko. Novel high-purity CTC isolation technology for downstream analysis: A “Cell- Circuit” magnetophoretic-microfluidic hybrid device [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A029.

Precision Cancer Therapy Enabled Anti-Epidermal Growth Factor Receptor-Conjugated Manganese Core Phthalocyanine Bismuth Nanocomposite for Dual Imaging-Guided Breast Cancer Treatment.

Cancer remains a formidable global health challenge, demanding the exploration of innovative treatment modalities with minimized side effects. One promising avenue involves the synergistic integration of targeted photothermal/photodynamic therapy (PTT/PDT), utilizing specially designed functional nanomaterials for precise cancer diagnosis and treatment. This study introduces a composite biomaterial, anti-epidermal growth factor receptor-conjugated manganese core phthalocyanine bismuth (anti-EGFR-MPB), synthesized for precise cancer imaging and treatment. The biomaterial, synthesized via a solvothermal process, effectively treats and images breast cancer in mouse models. Its biomimetic design targets cancer cells precisely, with dual imaging for real-time monitoring. The biomimetic design of the composite enables precise targeting of cancer cells, whereas the dual imaging allows for real-time visualization and monitoring of the treatment. Invivo examinations confirm substantial damage to tumor tissues with no recurrence following 808-nm laser irradiation. The composite shows strong fluorescence/photoacoustic imaging (PAI) contrast, aiding malignancy detection. Biological assays and histological analyses confirmed the efficacy of the nanocomposite in inducing apoptosis in cancer cells. The integrated targeted dual image-guided phototherapy offered by this composite substantially enhances the precision and efficacy of cancer therapy, achieving an impressive photothermal efficiency of ~33.8%. Our findings demonstrate the utility of the anti-EGFR-MPB nanocomposite for both invitro and invivo photoacoustic image-guided PTT and PDT. The optimal treatment strategy for triple-negative breast cancer is found to be the use of 250 μg/ml of nanocomposite irradiated with 1.0 W/cm2 808-nm laser for 7 min.

Optimizing Deep Learning Models with Custom ReLU for Breast Cancer Histopathology Image Classification

Purpose: The prompt identification of breast cancer is crucial in preventing the considerable damage inflicted by this dangerous form of cancer, which is widely distributed across the globe. This study seeks to refine the efficacy of a deep learning-driven approach for the precise diagnosis of breast cancer by employing diverse bespoke Rectified Linear Units (ReLU) to improve the model's performance and reduce inaccuracies within the system. Method: This study focuses on analyzing a deep learning approach utilizing the BreakHis dataset with 7,909 images, incorporating changes to the ReLU activation function across different pre-trained CNN models. It then evaluates performance through measures such as accuracy, precision, recall, and F1-Score. Result: Based on our experiment results, show that the DenseNet201 models with a custom LeakyReLU excel beyond the typical ReLU, achieving the highest accuracy, recall, and F1-Score at 99.21%, 99.21%, and 99.11%, respectively. Simultaneously, ResNet152, utilizing LessNegativeReLU ( ), achieved the highest precision at 99.11%. The VGG11 model exhibited the most notable performance enhancement, with improvements ranging from 1.39% to 1.59%. Novelty: The research is original in optimizing a model for accurate breast cancer diagnosis. The proposed model is superior to the model utilizing the default activation function. This finding indicates that the study significantly enhances performance while effectively minimizing errors, thereby necessitating further exploration into the effectiveness of the customized activation function when applied to other medical imaging modalities.

Metabolic interactions of host-gut microbiota: New possibilities for the precise diagnosis and therapeutic discovery of gastrointestinal cancer in the future-a review.

Gastrointestinal (GI) cancer continues to pose a significant global health challenge. Recent advances in our understanding of the complex relationship between the host and gut microbiota have shed light on the critical role of metabolic interactions in the pathogenesis and progression of GI cancer. In this study, we examined how microbiota interact with the host to influence signalling pathways that impact the formation of GI tumours. Additionally, we investigated the potential therapeutic approach of manipulating GI microbiota for use in clinical settings. Revealing the complex molecular exchanges between the host and gut microbiota facilitates a deeper understanding of the underlying mechanisms that drive cancer development. Metabolic interactions hold promise for the identification of microbial signatures or metabolic pathways associated with specific stages of cancer. Hence, this study provides potential strategies for the diagnosis, treatment and management of GI cancers to improve patient outcomes.

An effective and specific cancer theranostic nanoplatform with NIR-II fluorescence imaging-guided photothermal/chemodynamic therapy

Nanomaterial-based second near-infrared region (NIR-II) fluorescent probes have deep tissue penetration. However, it is limited by low catalytic efficiency and specific recognition in the tumor microenvironment (TME). Herein, Ag2S-Cu(II)-Cu2-xSe diagnostic nanoparticles have been established by Cu2-xSe and Ag2S QDs, which can serve as diagnostic agents. They can improve the precise diagnosis of cancer via a NIR-II imaging-guided multimodal photothermal/chemodynamic therapy strategy under near-infrared (808 nm) light irradiation. Under neutral conditions, the fluorescence of Ag2S-Cu(II)-Cu2-xSe nanoplatforms is in the “off” mode, which is selectively turned on only in the tumor microenvironment. Ag2S-Cu(II)-Cu2-xSe NPs not only emit fluorescence specifically in the TME but also have efficient photothermal conversion properties. Significantly, Ag2S-Cu(II)-Cu2-xSe has experimentally demonstrated a strong combined therapeutic effect and can consume excess intracellular glutathione (GSH) through redox reactions. This work presents an effective strategy for achieving precise and safe multifunctional nanodiagnostic platforms.

Size Matters: Curvature and Antigen-Mediated Dual Recognition of Size-Specific Tumor-Derived Exosomes.

Accurate identification of tumor-derived exosomes is crucial for advancing cancer diagnosis and therapies. However, distinguishing tumor-derived exosomes is challenging due to the heterogeneity of exosomes, which reflect different sizes and cells of origin. To address this challenge, we introduce the curvature and antigen-mediated proximity ligation assay for tumor-derived exosomes (CAPTURE) strategy, which leverages the size-selective properties of curvature-sensing peptides and specific antigen binding of aptamers. CAPTURE enables highly specific identification and precise quantification of the PD-L1+ exosomes in plasma samples. CAPTURE is proven to be simple, homogeneous, rapid, and highly selective, achieving a 100% specificity in discriminating colorectal cancer (CRC) patients from healthy donors. Overall, the CAPTURE strategy presents a promising avenue for precise and noninvasive cancer diagnosis.

ESR Essentials: staging and restaging with FDG-PET/CT in oncology-practice recommendations by the European Society for Hybrid, Molecular and Translational Imaging.

Positron emission tomography (PET) stands as the paramount clinical molecular imaging modality, especially in oncology. Unlike conventional anatomical-morphological imaging methods such as computed tomography (CT) and magnetic resonance imaging (MRI), PET provides detailed visualizations of internal activity at the molecular and cellular levels. 18-fluorine-fluorodeoxyglucose ([18F]FDG)-PET combined with contrast-enhanced CT (ceCT) significantly improves the detection of various cancers. Appropriate patient selection is crucial, and physicians should carefully assess the appropriateness of [18F]FDG-PET/CT based on specific clinical criteria and evidence. Due to its high diagnostic accuracy, [18F]FDG-PET/CT is indispensable for evaluating the extent of disease, staging, and restaging known malignancies, and assessing the response to therapy. PET/CT imaging offers significant advantages in patient management, particularly by identifying occult metastases that might otherwise go undetected. This can help prevent unnecessary surgeries, allowing many patients to be redirected to systemic chemotherapy instead. However, it is important to note that the gold standard for surgical planning remains CT and/or MRI, depending on the body region. These imaging modalities, with or without associated angiography, provide superior contrast and spatial resolution, essential for detailed surgical preparation and planning. [18F]FDG-PET/CT has a central role in the precise and early diagnosis of cancer, contributing significantly to personalized treatment plans. However, it has limitations, including non-tumor-specific uptake and the potential to inaccurately capture the metabolic activity of certain tumor types due to low uptake in some well-differentiated tumor cell lines. Therefore, it should be utilized in clinical scenarios where it offers crucial diagnostic insights not readily available with other imaging modalities. KEY POINTS: Use [18F]FDG-PET/CT selectively based on clinical appropriateness criteria and existing evidence to optimize resource utilization and minimize patient exposure. Employ [18F]FDG-PET/CT in treatment planning and monitoring, particularly for assessing chemotherapy or radiotherapy response in FDG-avid lymphoma and solid tumors. When available, [18F]FDG-PET/CT can be integrated with other diagnostic tools, such as MRI, to enhance overall diagnostic accuracy.

Imaging in France: 2024 Update.

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.

β-galactosidase instructed in situ self-assembly fluorogenic probe for prolonged and accurate imaging of ovarian tumor

Fluorescent probes are essential for optical imaging and have been extensively employed for precise cancer diagnosis studies. β-galactosidase (β-gal) serves as a primary biomarker for ovarian cancer and has been utilized to develop imaging probes for accurate tumor diagnosis. However, traditional small molecular probes have limitations in terms of rapid diffusion and metabolic clearance from the target lesion, resulting in a short imaging window and compromised tumor-to-background ratios (TBR). Herein, we integrated an enzyme-instructed in situ self-assembly strategy to construct Gal-IRFF, a small molecule-based activatable near-infrared (NIR) fluorogenic probe. Upon cleavage by endogenous β-gal overexpressed in ovarian cancer cells, IRFF exhibited enhanced NIR fluorescence signals and self-assembled into nanoparticles through intermolecular interactions of the Phe-Phe (FF) dipeptide moiety, which facilitated probe accumulation and retention within the tumor lesion. Compared with the small molecule probe Gal-IR, our proposed self-assembly probe Gal-IRFF demonstrated a lower limit of detection (LOD) towards β-gal and showed remarkable improvements in distribution and retention time within SKOV3 cells in vitro and tumors in vivo, thereby providing a long-term imaging window for real-time monitoring β-gal levels in ovarian tumors. Therefore, this study highlights the potential of an enzyme-instructed self-assembly fluorogenic probe design approach for achieving precise tumor diagnosis in vivo.

Strategies for the treatment of hormone receptor-positive HER2-low breast cancer based on clinical practice: a round table discussion.

Human epidermal growth factor receptor 2 (HER2)-low breast cancer is a newly identified targetable subset of breast tumors, and its clinical characteristics and treatment strategies are controversial. The emergence of novel anti-HER2 antibody-drug conjugate (ADC) has brought promising approaches for HER2-low breast cancer treatment. Several clinical trials have validated the efficacy and safety of trastuzumab deruxtecan (T-Dxd) in HER2-low breast cancer at different treatment settings. The treatment timing, candidate identification, long-term management, and overcoming drug resistance are crucial questions to improve breast cancer patient survival. Here we present a clinical case of hormone receptor-positive (HR+) HER2-low breast cancer patient who experienced neoadjuvant chemotherapy, surgery, adjuvant, and first-line endocrine therapy with limited effectiveness. After the treatment failure of CDK4/6 inhibitors, the utilization of T-Dxd brought a long-term disease response and tolerable low toxicities. In this round table discussion, we summarized opinions and recommendations from breast cancer surgeons and oncologists on treatment strategies for this patient. The discussion mainly focused on the precise diagnosis of HER2-low breast cancer, treatment design at different disease status, regimens selection according to drug response, strategies consideration for overcoming drug resistance and the management of adverse events in long-term survival. These opinions would provide critical insights to improve HER2-low breast cancer treatment and offer valuable suggestions for clinical practice.