Tumor Detection Research Articles

68Ga]Ga-FAPI-04 PET/MR imaging strategy in management of Krukenberg tumors (KTs) from gastric signet-ring-cell carcinoma: to overcome limitation of [68Ga]Ga-FAPI-04 PET imaging in KTs.

To compare performance of whole-body [68Ga]Ga-FAPI-04 and [18F]FDG PET imaging in the detection of Krukenberg tumors (KTs), primary site and extra-ovarian metastases of gastric signet-ring-cell carcinoma (GSRCC), and evaluate the value of [68Ga]Ga-FAPI-04 PET/MR imaging strategy and its potential impact on the management of KTs from GSRCC. Twelve patients with twenty-three KTs from GSRCC, who underwent both [68Ga]Ga-FAPI-04 pelvic PET/MR and whole-body [68Ga]Ga-FAPI-04 and [18F]FDG PET imaging were retrospectively analyzed. [68Ga]Ga-FAPI-04 and [18F]FDG uptakes were compared by using Wilcoxon signed-rank test or paired t test. McNemar's test was used to compare lesion detectability between two modalities. Two-tailed P<0.05 was considered statistically significant. Immunohistochemistry staining was utilized to analyze the fibroblast activation protein (FAP) expression in KTs. A total of 12 patients with 23 KTs from GSRCC (8 synchronous and 4 metachronous) were evaluated. [68Ga]Ga-FAPI-04 was superior to [18F]FDG PET in detecting primary sites of GSRCC (100% [11/11] vs. 18.2% [2/11], p = 0.002), involved lymph nodes (90.9% [10/11] vs. 54.5% [6/11], p = 0.046) and peritoneal metastases (100% [12/12] vs. 41.7% [5/12], p = 0.008), with higher SUVmax and TBR (all p < 0.005). Both tracers had limited value in identifying KTs, with 100% false negative rate on [68Ga]Ga-FAPI-04 PET and a low detection rate of 8.7% on [18F]FDG PET. Fap immunohistochemistry showed negative or slight FAP expression in neoplastic signet ring cells and ovarian stroma. [68Ga]Ga-FAPI-04 PET/MR imaging strategy greatly improved the detection rate of Krukenberg tumors (87%, 20/23). After adding diffusion-weighted imaging (DWI), the detection rate was further improved (87.5% vs. 100%, p = 0.083). [68Ga]Ga-FAPI-04 PET/MR imaging strategy either upgraded TNM staging or changed treatment management in twelve patients. [68Ga]Ga-FAPI-04 PET outperformed [18F]FDG PET in detecting primary site and most extra-ovarian metastases of GSRCC, but both tracers had limited value in identifying Krukenberg tumors. Pelvis MRI should be applied to compensate the limitation of [68Ga]Ga-FAPI-04 PET imaging to identify Krukenberg tumours. The [68Ga]Ga-FAPI-04 PET/MR imaging strategy has the potential to impact treatment decisions for GSRCC patients with KTs.

SEA Anemone-inspired Conducting Polymer Sensing Platform for Integrated Detection of Tumor Protein Marker and Circulating Tumor Cell.

Combining the detection of tumor protein markers with the capture of circulating tumor cells (CTCs) represents an ultra-promising approach for early tumor detection. However, current methodologies have not yet achieved the necessary low detection limits and efficient capture. Here, we introduced a novel polypyrrole nanotentacles sensing platform featuring anemone-like structures capable of simultaneously detecting protein biomarkers and capturing CTCs. The incorporation of nanotentacles significantly enhanced the electrode surface area, providing abundant active sites for antibody binding. This enhancement allowed detecting nucleus matrix protein22 (NMP22) and bladder tumor antigen (BTA) with 2.39 and 3.12 pg/mL detection limit, respectively. Furthermore, our developed sensing platform effectively captured MCF-7 cells in blood samples with a detection limit of fewer than 10 cells/mL, attributed to the synergistic multivalent binding facilitated by the specific recognition antibodies and the positive charge on the nanotentacles surface. This sensing platform demonstrated excellent detection capabilities and outstanding capture efficiency, offering a simple, accurate, and efficient strategy for early tumor detection. This article is protected by copyright. All rights reserved.

Early detection of pancreatic cancer in the era of precision medicine.

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related mortality and it is often diagnosed at advanced stages due to non-specific clinical presentation. Disease detection at localized disease stage followed by surgical resection remains the only potentially curative treatment. In this era of precision medicine, a multifaceted approach to early detection of PDAC includes targeted screening in high-risk populations, serum biomarkers and "liquid biopsies", and artificial intelligence augmented tumor detection from radiologic examinations. In this review, we will review these emerging techniques in the early detection of PDAC.

Active Targeting Hyaluronan Conjugated Nanoprobe for Magnetic Particle Imaging and Near-Infrared Fluorescence Imaging of Breast Cancer and Lung Metastasis.

A major contributing cause to breast cancer related death is metastasis. Moreover, breast cancer metastasis often shows little symptoms until a large area of the organs is occupied by metastatic cancer cells. Breast cancer multimodal imaging is attractive since it integrates advantages from several modalities, enabling more accurate cancer detection. Glycoprotein CD44 is overexpressed on most breast cancer cells and is the primary cell surface receptor for hyaluronan (HA). To facilitate breast cancer diagnosis, we report an indocyanine green (ICG) and HA conjugated iron oxide nanoparticle (NP-ICG-HA), which enabled active targeting to breast cancer by HA-CD44 interaction and detected metastasis with magnetic particle imaging (MPI) and near-infrared fluorescence imaging (NIR-FI). When evaluated in a transgenic breast cancer mouse model, NP-ICG-HA enabled the detection of multiple breast tumors in MPI and NIR-FI, providing more comprehensive images and a diagnosis of breast cancer. Furthermore, NP-ICG-HAs were evaluated in a lung metastasis model. Upon NP-ICG-HA administration, MPI showed clear signals in the lungs, indicating the tumor sites. This is the first time that HA-based NPs have enabled MPI of cancer. NP-ICG-HAs are an attractive platform for noninvasive detection of primary breast cancer and lung metastasis.

Effectiveness of deep learning-based reconstruction for improvement of image quality and liver tumor detectability in the hepatobiliary phase of gadoxetic acid-enhanced magnetic resonance imaging.

To evaluate the effectiveness of deep learning-based reconstruction (DLR) in improving image quality and tumor detectability of isovoxel high-resolution breath-hold fat-suppressed T1-weighted imaging (HR-BH-FS-T1WI) in the hepatobiliary phase (HBP) of Gadoxetic acid-enhanced magnetic resonance imaging (Gd-EOB-MRI). This retrospective evaluated 42 patients with 98 liver tumors who underwent Gd-EOB-MRI between March 2023 and May 2023 using three techniques based on HBP imaging: isovoxel HR-BH-FS-T1WI reconstructed (1) with DLR (BH-DLR +) and (2) without DLR (BH-DLR -) and (3) HR-FS-T1WI scanned with a free-breathing technique using a navigator-echo-triggered technique and DLR (Navi-DLR +). The three techniques were qualitatively and quantitatively compared by the Friedman test and the Bonferroni post-hoc test. Tumor detectability was compared using the McNemar test. BH-DLR + (3.85, average score of two radiologists) showed significantly better qualitative scores for image noise than BH-DLR - (2.84) and Navi-DLR + (3.37) (p < 0.0167), and Navi-DLR + showed significantly better scores than BH-DLR - (p < 0.0167). BH-DLR + (3.77) and BH-DLR - (3.77) showed significantly better qualitative scores for respiratory motion artifact than Navi-DLR + (2.75) (p < 0.0167), but there was no significant difference in scores between BH-DLR + and BH-DLR - (p > 0.0167). BH-DLR + (0.32) and Navi-DLR + (0.33) showed significantly higher lesion-to-nonlesion CR than BH-DLR - (0.29) (p < 0.0167), but there was no significant difference in lesion-to-nonlesion CR between BH-DLR + and Navi-DLR + (p > 0.0167). BH-DLR + (89.8%) showed significantly better tumor detectability than BH-DLR - (76.0%) and Navi-DLR + (77.6%) (p < 0.05). The use of DLR for isovoxel HR-BH-FS-T1WI was effective in improving image quality and tumor detectability.

Novel locally nebulized indocyanine green for simultaneous identification of tumor margin and intersegmental plane.

Segmentectomy, recommended for early-stage lung cancer or compromised lung function, demands precise tumor detection and intersegmental plane identification. While Indocyanine green (ICG) commonly aids in these aspects using near-infrared (NIR) imaging, its separate administrations through different routes and times can lead to complications and patient anxiety. This study aims to develop a lung-specific delivery method by nebulizing low-dose ICG to targeted lung segments, allowing simultaneous detection of lung tumors and intersegmental planes across diverse animal models. To optimizing the dose of ICG for lung tumor and interlobar fissure detection, different doses of ICG (0.25, 0.1 and 0.05mg/kg) were nebulized to rabbit lung tumor models. The distribution of locally nebulized ICG in targeted segments was studied to evaluate the feasibility of detecting lung tumor and intersegmental planes in canine lung pseudotumor models. NIR fluorescence imaging demonstrated clear visualization of lung tumor margin and interlobar fissure using local nebulization of 0.1mg/kg ICG for only 4 min during surgery in the rabbit models. In the canine model, the local nebulization of 0.05mg/kg of ICG into the target segment enabled clear visualization of pseudotumor and intersegmental planes for 30 min. This innovative approach achieves a reduction in ICG dose and prolonged the visualization time of the intersegmental plane and effectively eliminates the need for the hurried marking of tumors and intersegmental planes. We anticipate that lung specific delivery of ICG will prove valuable for image-guided limited resection of lung tumors in clinical practice.

Comparison of effective imaging modalities for detecting gastric neoplasms: A randomized three-arm phase II trial.

The early detection of gastric neoplasms (GNs) leads to favorable treatment outcomes. The latest endoscopic system, EVIS X1, includes third-generation narrowband imaging (3G-NBI), texture and color enhancement imaging (TXI), and high-definition white-light imaging (WLI). Therefore, this randomized phase II trial aimed to identify the most promising imaging modality for GN detection using 3G-NBI and TXI. Patients with scheduled surveillance endoscopy after a history of esophageal cancer or GN or preoperative endoscopy for known esophageal cancer or GN were randomly assigned to the 3G-NBI, TXI, or WLI groups. Endoscopic observations were performed to detect new GN lesions, and all suspected lesions were biopsied. The primary endpoint was the GN detection rate during primary observation. Secondary endpoints were the rate of missed GNs, early gastric cancer (EGC) detection rate, and positive predictive value (PPV) for a GN diagnosis. The decision rule had a higher GN detection rate between 3G-NBI and TXI, outperforming WLI by >1.0%. Finally, 901 patients were enrolled and assigned to the 3G-NBI, TXI, and WLI groups (300, 300, and 301 patients, respectively). GN detection rates in the 3G-NBI, TXI, and WLI groups were 7.3, 5.0, and 5.6%, respectively. The rates of missed GNs were 1.0, 0.7, and 1.0%, the detection rates of EGC were 5.7, 4.0, and 5.6%, and the PPVs for the diagnosis of GN were 36.5, 21.3, and 36.8% in the 3G-NBI, TXI, and WLI groups, respectively. Compared to TXI and WLI, 3G-NBI is a more promising modality for GN detection.

Beyond boundaries: Feasibility of curved linear array echoendoscope in appendiceal neoplasm detection

We recently read with great interest a study by Zhang et al in the World Journal of Gastroenterology . In our practice, we focus specifically on examining appendiceal mucinous neoplasms (AMNs) with endoscopic ultrasound (EUS) using different scopes. AMNs are rare neoplastic lesions characterized by an accumulation of mucin inside a cystic dilatation of the appendix. Clinically, they can present as nonspecific acute appendicitis. AMNs can turn into a life-threatening condition, termed pseudomyxoma peritonei, in which the ruptured appendix causes accumulation of mucin in the abdomen. Therefore, accurate and rapid diagnosis of AMN is essential. EUS is able to confirm and stage AMNs; although, EUS examination was once limited to the rectal and anal regions due to the conventional oblique-view scopes. With the emergence of new forward-view linear echoendoscopes and instruments like EUS miniprobes and overtubes, the scope of examination is changing. Herein, we discuss the feasibility of using the curved linear array echoendoscopes to examine cecal and appendiceal orifice lesions.

Common Spontaneous Tumors and Tumor-like Lesions in 70 Pet Rodents and Negative MMTV Detection in Mammary Tumors

Compared to the number of studies on the neoplasms of laboratory rodents, fewer studies have focused on spontaneous neoplasms in pet rodents. Notably, the mouse mammary tumor virus (MMTV) is associated with mammary tumors in rodents. In this study, 77 tumors and tumor-like lesions of biopsy samples were collected from 70 pet rodents, including hamsters (n = 47), guinea pigs (n = 16), unknown species (n = 4), rats (n = 2), and a gerbil. Fifty tumors were collected from 47 hamsters, in which the most common tumors were mammary tumors (13/50), followed by fibrosarcoma (9/50), mast cell tumors (4/50), and squamous cell carcinoma (4/50). The collected subtypes of mammary tumors in hamsters included tubular carcinoma (n = 5), tubular adenoma (n = 4), carcinoma and malignant myoepithelioma (n = 1), simple tubular carcinoma (n = 1), adenosquamous carcinoma (n = 1), and tubulopapillary adenoma (n = 1). In addition, twenty tumors were collected from guinea pigs, in which the most common tumor was lipoma (6/20), followed by adenocarcinoma of the mammary gland (4/20), trichofolliculoma (2/20), and collagenous hamartomas (2/20). In guinea pigs, the subtypes of mammary gland tumors were tubular carcinoma (n = 2), tubular and solid carcinoma (n = 1), and tubulopapillary carcinoma (n = 1). In 20 cases of mammary tumors, MMTV was not detected, implicating no evidence of MMTV infection in mammary oncogenesis in pet rodents in Taiwan.

Enhancing Brain Tumour Identification through Convolutional Neural Network

A tumor is carried on by rapid and uncontrolled cell proliferation in the brain. If it is not treated in the early stages, it could prove fatal. Despite multiple significant efforts and encouraging outcomes, accurate segmentation and classification still pose a difficulty. Detection of brain tumors is extremely complicated by the distinctions in tumor position, structure, and proportions. Using computational intelligence and statistical image processing techniques, proposed in our project provide multiple approaches to detect brain cancer and tumors. Also shows an evaluation matrix for a specific system using particular systems and type of dataset. Also explains the morphology of brain tumors, accessible data sets, augmentation techniques, component extraction, and classification of Deep Learning (DL), Transfer Learning (TL), and Machine Learning (ML) models. Key Words: brain tumour, image classification, image sementation, convolutional neural networks.

Random effects models of tumour growth for investigating interval breast cancer.

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

Incidental pigmented basal cell carcinoma discovered on positron emission tomography (PET)

Incidental pigmented basal cell carcinoma discovered on positron emission tomography (PET)

Automatic Brain Tumor Detection and Classification Using UNET and Optimized Support Vector Machine

Automatic Brain Tumor Detection and Classification Using UNET and Optimized Support Vector Machine

PSMA-Targeted Therapy: Advancements in Detection and Treatment Modalities with Dr. Scott T. Tagawa

Prostate cancer is one of the most challenging malignancies due to its high incidence and prevalence, as it is the most frequently diagnosed non-skin cancer in men. The timely identification of prostate cancer and its metastasis is paramount for ensuring favorable outcomes for patients. Prostate-specific membrane antigen (PSMA) emerges as a promising biomarker for its detection, due to its specificity. This makes it an ideal target for the early identification of a metastatic phenotype. Situated on the membrane of tumor cells, PSMA facilitates the attachment of PSMA-targeting particles, enabling their detection through positron emission tomography (PET) scans with relative ease. Utilizing these imaging agents in conjunction with PET scans enhances the accuracy of prostate cancer tumor detection compared to PET scans alone. The advancement in prostate cancer imaging has paved the way for innovative treatment modalities. Prostate-specific membrane antigen-targeted radionuclide therapies (PSMA-TRT) exploit PSMA imaging agents to target identified prostate cancer malignancies with precise radiation, thereby reducing or eliminating the tumor mass. PSMA-TRT exhibits significant promise in prostate cancer therapy, evident from the notable declines in prostate-specific antigen (PSA) levels post treatment. However, PSMA-TRT carries both beneficial and adverse effects. While it represents a substantial leap forward in tumor cell imaging, PSMA-based antigens, being larger particles than ligands, offer prolonged imaging capabilities. Yet, the long-term effects of PSMA-TRT remain unknown, with the short-term adverse ones including fatigue, nausea, pain flares, and potential radiation exposure to others.

Enhancing brain tumor detection in MRI images through explainable AI using Grad-CAM with Resnet 50

This study addresses the critical challenge of detecting brain tumors using MRI images, a pivotal task in medical diagnostics that demands high accuracy and interpretability. While deep learning has shown remarkable success in medical image analysis, there remains a substantial need for models that are not only accurate but also interpretable to healthcare professionals. The existing methodologies, predominantly deep learning-based, often act as black boxes, providing little insight into their decision-making process. This research introduces an integrated approach using ResNet50, a deep learning model, combined with Gradient-weighted Class Activation Mapping (Grad-CAM) to offer a transparent and explainable framework for brain tumor detection. We employed a dataset of MRI images, enhanced through data augmentation, to train and validate our model. The results demonstrate a significant improvement in model performance, with a testing accuracy of 98.52% and precision-recall metrics exceeding 98%, showcasing the model’s effectiveness in distinguishing tumor presence. The application of Grad-CAM provides insightful visual explanations, illustrating the model’s focus areas in making predictions. This fusion of high accuracy and explainability holds profound implications for medical diagnostics, offering a pathway towards more reliable and interpretable brain tumor detection tools.

Dose Optimization of Intravenous Indocyanine Green for Malignant Lung Tumor Localization

Background: Intravenously administered indocyanine green (ICG) accumulates in lung tumors, facilitating their detection via a fluorescence spectrum measurement. This method aids in identifying tumor locations that are invisible to the naked eye. We aim to determine the optimal ICG dose and administration method for accurate tumor identification during lung resection surgeries, utilizing a novel ICG fluorescence spectroscopy system for precise tumor localization. Materials and Methods: ICG should be dissolved in the provided solution or distilled water and administered intravenously approximately 24 h before surgery, beginning with an initial dose of 0.5 mg/kg. If the tumor detection rate is insufficient, the dose may be gradually increased to a maximum of 5.0 mg/kg to determine the optimal dosage for effective tumor detection. This fluorescence spectroscopy during surgery may reveal additional lesions that remain undetected in preoperative assessments. The primary endpoint includes the correct diagnostic rate of tumor localization. The secondary endpoints include the measurement of the intraoperative ICG fluorescence spectral intensity in lung tumors, the assessment of the operability and safety of intraperitoneal ICG administrations, the measurement of the ICG fluorescence spectral intensity in surgical specimens, the comparison of the spectral intensity in lung tissues during collapse and expansion, the correlation between ICG camera images and fluorescence spectral intensity, and the comparison of fluorescence analysis results with histopathological findings. The trial has been registered in the jRCT Clinical Trials Registry under the code jRCTs011230037. Results and Conclusions: This trial aims to establish an effective methodology for localizing and diagnosing malignant lung tumors, thereby potentially improving surgical outcomes and refining treatment protocols.

Speeding Up and Improving Image Quality in Glioblastoma MRI Protocol by Deep Learning Image Reconstruction

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p &lt; 0.001), improved tumor conspicuity and image sharpness (all p &lt; 0.001, respectively), and less image noise (all p &lt; 0.001), while maintaining diagnostic confidence (all p &gt; 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

Occurrence of Residual Breast Tumors and Efficacy of MRI in their Detection after Vacuum-assisted Excisional Biopsy.

Residual breast tumors may remain after vacuum-assisted excisional biopsy (VAEB). To determine the incidence of residual breast tumors in patients after VAEB and the efficacy of magnetic resonance imaging (MRI) in detecting these tumors. This retrospective analysis examined patients who received VAEB before a diagnosis of breast cancer (BC) at our hospital from 2015 to 2019. The incidence of residual tumors after VAEB was determined by MRI and pathological examination. The diagnostic value of MRI in detecting residual tumors was determined for all patients and different subgroups. Logistic regression analysis was used to identify factors associated with residual tumors. We examined 147 patients and obtained pathological samples from 146 patients, including 103 (70.5%) with residual tumors and 43 (29.5%) without residual tumors. The MRI examinations demonstrated the complete tumor resection rate was 48.9%. Compared to the pathological results, MRI had a positive predictive value of 77.8%, negative predictive value of 48.8%, specificity of 65.6%, and sensitivity of 60.7%. Further analysis indicated that MRI had moderate accuracies for patients with stage pT-1 (71.9%), stage pTNM-IA (73.1%), and luminal B subtype (78.3%). Binary logistic regression analysis showed that the risk of tumor residue correlated with the pathological stage. Tumor residue is common after VAEB, and MRI has limited accuracy in detecting these residual tumors. However, for small breast tumors and luminal B subtype BC, MRI had higher accuracy in the detection of residual tumors. The risk of tumor residue is closely associated with the pathological stage.

A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images

A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images

Emergence of Quantum Dots as Innovative Tools for Early Diagnosis and Advanced Treatment of Breast Cancer.

Quantum dots (QDs) semiconducting nanomaterials, have garnered attention due to their distinctive properties, including small size, high luminescence, and biocompatibility. In the context of triple-negative breast cancer (TNBC), notorious for its resistance to conventional treatments, QDs exhibit promising potential for enhancing diagnostic imaging and providing targeted therapies. This review underscores recent advancements in the utilization of QDs in imaging techniques, such as fluorescence tomography and magnetic resonance imaging, aiming at the early and precise detection of tumors. Emphasis is placed on the significance of QD design, synthesis and functionalization processes as well as their use in innovative strategies for targeted drug delivery, capitalizing on their ability to selectively deliver therapeutic agents to cancer cells. As the research in this field advances rapidly, this review covers a classification of QDs according to their composition, the characterization techniques than can be used to determine their properties and, subsequently, emphasizes recent findings in the field of TNBC-targeting, highlighting the imperative need to address challenges, like potential toxicity or methodologies standardization. Collectively, the findings explored thus far suggest that QDs could pave the way for early diagnosis and effective therapy of TNBC, representing a significant stride toward precise and personalized strategies in treating TNBC.