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According to the International Agency for Research on Cancer (IARC), the mortality rate due to brain tumors is 76%. It is required to detect the brain tumors as early as possible and to provide the patient with the required treatment to avoid any fatal situation. With the recent advancement in technology, it is possible to automatically detect the tumor from images such as Magnetic Resonance Iimaging (MRI) and computed tomography scans using a computer-aided design. Machine learning and deep learning techniques have gained significance among researchers in medical fields, especially Convolutional Neural Networks (CNN), due to their ability to analyze large amounts of complex image data and perform classification. The objective of this review article is to present an exhaustive study of techniques such as preprocessing, machine learning, and deep learning that have been adopted in the last 15 years and based on it to present a detailed comparative analysis. The challenges encountered by researchers in the past for tumor detection have been discussed along with the future scopes that can be taken by the researchers as the future work. Clinical challenges that are encountered have also been discussed, which are missing in existing review articles.

Immuno-positron emission tomography (immunoPET) imaging is a paradigm-shifting imaging technique for whole-body and all-lesion tumor detection, based on the combined specificity of tumor-targeting vectors [e.g., monoclonal antibodies (mAbs), nanobodies, and bispecific antibodies] and the sensitivity of PET imaging. By noninvasively, comprehensively, and serially revealing heterogeneous tumor antigen expression, immunoPET imaging is gradually improving the theranostic prospects for hematological malignancies. In this review, we summarize the available literature regarding immunoPET in imaging hematological malignancies. We also highlight the pros and cons of current conjugation strategies, and modular chemistry that can be leveraged to develop novel immunoPET probes for hematological malignancies. Lastly, we discuss the use of immunoPET imaging in guiding antibody drug development.

Background Symptomatic breast cancers share aggressive clinico-pathological characteristics compared to screen-detected breast cancers. We assessed the association between the method of cancer detection and genomic and clinical risk, and its effect on adjuvant chemotherapy recommendations. Patients and methods Patients with early hormone receptor positive (HR+) HER2neu-negative (HER2-) breast cancer, and known OncotypeDX Breast Recurrence Score test were included. A natural language processing (NLP) algorithm was used to identify the method of cancer detection. The clinical and genomic risks of symptomatic and screen-detected tumors were compared. Results The NLP algorithm identified the method of detection of 401 patients, with 216 (54%) diagnosed by routine screening, and the remainder secondary to symptoms. The distribution of OncotypeDX recurrence score (RS) varied between the groups. In the symptomatic group there were lower proportions of low RS (13% vs 23%) and higher proportions of high RS (24% vs. 13%) compared to the screen-detected group. Symptomatic tumors were significantly more likely to have a high clinical risk (59% vs 40%). Based on genomic and clinical risk and current guidelines, we found that women aged 50 and under, with a symptomatic cancer, had an increased probability of receiving adjuvant chemotherapy recommendation compared to women with screen-detected cancers (60% vs. 37%). Conclusions We demonstrated an association between the method of cancer detection and both genomic and clinical risk. Symptomatic breast cancer, especially in young women, remains a poor prognostic factor that should be taken into account when evaluating patient prognosis and determining adjuvant treatment plans., Highlights • Symptomatic women at diagnosis are often younger, have larger tumors and higher Ki67. • Women with symptomatic tumors have both higher genomic risk and higher clinical risk for disease recurrence. • Symptomatic presentation significantly increases the likelihood of adjuvant chemotherapy recommendation, especially among younger women. • The use of a computational approach (NLP) for extracting medical information was demonstrated to be efficient and valid.

Objectives With the advent of new optical technologies, early pharyngolaryngeal cancerous lesions can be better visualized. Although the conventional transnasal approach offers great views of the hypopharynx and larynx, the visualization of the oropharynx and palatine tonsils is limited. Through the transoral insertion of a flexible video-laryngoscope, direct views of the oropharynx and oral cavity can be obtained. Thus, transoral examination may contribute to primary detection of cancers of unknown primary (CUP). Methods Eighty-five CUP patients from Tokai University were included retrospectively in this study, from 2006 to 2017. Starting in 2010, we employed the transoral examination in addition to our conventional method. The primary detection rates were compared before and after 2010. Oropharyngeal primaries were further analyzed for tumor subsite and p16 status. Results The overall primary detection rate did not improve with the addition of transoral examination. However, greater numbers of oropharyngeal primaries were detected. The oropharyngeal lesions detected by transoral examination were mainly p16 positive, located on the palatine tonsil. Conclusion Transoral examination is a noninvasive, easy method to adopt in an outpatient setting, and a promising technique to improve tumor detection in this era of human papillomavirus-associated head and neck cancers. Level of evidence 3.

Tumor detection from Brain MRI images Abstract: Detecting tumors in the human brain has become the most challenging medical science issue. Recognition of tumors in MRIs is vital as it offers the aberrant relevant data for therapeutic interventions. MRI includes details on malignant tissue. An abnormal tissue growing and multiplying in the brain is a brain tumor. Physical examination is the standard approach for brain tumor identification, which takes much time and is not accurate every time. So, automated brain tumor identification methods are establishing to save time. Image segmentation utilizes to detect the brain's abnormal portion, which gives the tumor's location. This work uses the UNETS with VGG16 weights model to see and segment tumors from the rest of the brain tissue. The accurate detection of the tumors helps reduce the delay between diagnostic testing and therapy. Therefore, there is a significant demand for computer algorithms to be precise, speedy, time-efficient, and dependable. The technology described relates to detecting and analyzing brain cancers automatically via U-Net and the VGG16 CNN.

Optical imaging (OI) provides real-time clinical imaging capability and simultaneous molecular, morphological, and functional information of disease processes. In this study, we present a new interventional OI technique, which enables in vivo visualization of three distinct pathologic zones of ablated tumor periphery for immediate detection of residual tumors during a radiofrequency ablation (RFA) session. Rabbits with orthotopic hepatic tumors were divided into two groups (n = 8/group): incomplete RFA and complete RFA. Indocyanine green-based interventional OI was used to differentiate three pathological zones: ablated tumor, transition margin, and residual tumor or surrounding normal liver—with quantitative comparison of signal-to-background ratios among the three zones and between incompletely and completely ablated tumors. Subsequent ex vivo OI and pathologic correlation were performed to confirm the findings of interventional OI. Interventional OI could differentiate incompletely or completely ablated tumor peripheries, thus permitting identification of residual tumor. This technique may open new avenues for immediate assessment of tumor eradication during a single interventional ablation session. Significance: Interventional optical imaging can instantly visualize pathologic zones of ablated tumor peripheries to detect residual tumors, which could revolutionize current image-guided interventional oncologic ablation techniques.

Brain tumor is one type of disease that affects the brain directly. MRI is the finest imaging technique for a brain tumor and features information about tumor size, location, and type. MR images are most appropriate for brain studies because it has the best content in soft tissue. The segmentation, detection, and extraction of contaminated tumor area from magnetic resonance (MR) images are prime concerns, but very tedious tasks for radiologists or medical practitioners, and the accuracy depends on their experience. The automatic brain tumor detection and segmentation of MR images help the clinical experts to carry the treatment in a specific direction. The image segmentation methods play a very important role in automatic segmentation of MR images. The research article emphasises the comparative performance analysis of the different image segmentation algorithms such as Otsu's, watershed, level set, k-means, and DWT for brain tumor detection application. The MATLAB simulation is performed for all these algorithms on online images of brain tumor image segmentation benchmark (BRATS) dataset-2012. The performance of these methods is analysed based on response time and measures such as precision, recall, and accuracy. The predicted accuracy of Otsu's, watershed, level set, k-means and DWT algorithms using machine-learning model are 73.90%, 78.12%. 81.90%, 84.75%, and 88.12%, respectively. DWT has proven the good score for tumor detection applications.

Advances in magnetic resonance imaging (MRI) technology now enable the feasible three-dimensional (3D) acquisition of images. With respect to the imaging of musculoskeletal (MSK) tumors, literature is beginning to accumulate on the use of 3D MRI acquisition for tumor detection and characterization. The benefits of 3D MRI, including general advantages, such as decreased acquisition time, isotropic resolution, and increased image quality, are not only inherently useful for tumor imaging, but they also contribute to the feasibility of more specialized tumor-imaging techniques, such as whole-body MRI, and are reviewed here. Disadvantages of 3D acquisition, such as motion artifact and equipment requirements, do exist and are also discussed. Although further study is needed, 3D MRI acquisition will likely prove increasingly useful in the evaluation of patients with tumors of the MSK system.

Introduction Positron emission tomography (PET) using small ligands of the fibroblast activation protein (FAP) was recently introduced. However, optimal uptake time has not been defined yet. Here, we systematically compare early (~ 10 min p.i.) and late (~ 60 min p.i.) FAPI-46 imaging in patients with various types of cancer. Methods This is a retrospective single-institutional study. Imaging was performed at the Essen University Hospital, Germany. A total of 69 patients who underwent dual time-point imaging for either restaging (n = 52, 75%) or staging (n = 17, 25%) of cancer were included. Patients underwent PET with two acquisitions: early (mean 11 min, SD 4) and late (mean 66 min, SD 9). Mean injected activity was 148 MBq (SD 33). Results In total, 400 lesions were detected in 69 patients. Two of 400 (0.5%) lesions were only seen in early time-point imaging but not in late time-point imaging. On a per-patient level, there was no significant difference between SUVmax of hottest tumor lesions (Wilcoxon: P = 0.73). Organ uptake demonstrated significant early to late decrease in SUVmean (average ∆SUVmean: − 0.48, − 0.14, − 0.27 for gluteus, liver, and mediastinum, respectively; Wilcoxon: P max was observed (average ∆SUVmax: + 0.4, Wilcoxon: P = 0.03). Conclusion In conclusion, early (~ 10 min p.i.) versus late (~ 60 min p.i.) FAPI-46 imaging resulted in equivalent lesion uptake and tumor detection. For improved feasibility and scan volume, we implement early FAPI-46 PET in future clinical and research protocols.

Deep learning has proved successful in computer-aided detection in interpreting ultrasound images, COVID infections, identifying tumors from computed tomography (CT) scans for humans and animals. This paper proposes applications of deep learning in detecting cancerous cells inside patients via laparoscopic camera on da Vinci Xi surgical robots. The paper presents method for detecting tumor via object detection and classification/localizing using GRAD-CAM. Localization means heat map is drawn on the image highlighting the classified class. Analyzing images collected from publicly available partial robotic nephrectomy videos, for object detection, the final mAP was 0.974 and for classification the accuracy was 0.84.

Detection of brain of tumor is a laborious task as it involves identification, segmentation followed by detection of the tumor. It is a very challenging task to envisage uncommon structures in the image of human brain[15]. An Image processing concept called MRI can be used to visualize different structures of human body. The Magnetic Resonance images (MRI) are used to detect the uncommon portions of human brain. This paper explores different noise removal methods accompanied by Balance-contrast enhancement technique (BCET) which results in increased accuracy. Segmentation followed by canny edge detection is performed on the improved images to detect the fine edges of the abnormalities present. The model attained an accuracy of at most 98% in detecting the tumor or the abnormality in a human brain which determines the effectiveness of the proposed model.

Primary pulmonary lymphoepithelioma-like carcinomas (LELCs) are extremely rare tumors with better prognosis than other types of lung malignancies. In this case, we present 18F-FDG and 68Ga-DOTATATE PET/CT findings in a 52-year-old woman with pathologically confirmed pulmonary LELC. The pulmonary LELC presented mild 18F-FDG but avid 68Ga-DOTATATE uptake, indicating that the somatostatin receptor 2 may be highly expressed in LELCs. Therefore, 68Ga-DOTATATE PET/CT is a useful tool for tumor detection and initial staging in patients with LELCs.

Fluorine-19 (19F) magnetic resonance (MR) molecular imaging is a promising noninvasive and quantitative molecular imaging approach with intensive research due to the high sensitivity and low endogenous background signal of the 19F atom in vivo. Perfluorocarbons (PFCs) have been used as blood substitutes since 1970s. More recently, a variety of PFC nanoparticles have been designed for the detection and imaging of physiological and pathological changes. These molecular imaging probes have been developed to label cells, target specific epitopes in tumors, monitor the prognosis and therapy efficacy and quantitate characterization of tumors and changes in tumor microenvironment noninvasively, therefore, significantly improving the prognosis and therapy efficacy. Herein, we discuss the recent development and applications of 19F MR techniques with PFC nanoparticles in biomedicine, with particular emphasis on ligand-targeted and quantitative 19F MR imaging approaches for tumor detection, oxygenation measurement, smart stimulus response and therapy efficacy monitoring, et al.

Two compact ultrawideband antennas with different values of fidelity factors are introduced in this letter to study the impact of the fidelity factor on the tumor detection of microwave imaging systems. The design procedures are based on achieving the largest possible bandwidth. Antenna 1 (Ant. 1) and Antenna 2 (Ant. 2) have an overall size of 20 × 28 mm2 and 16 × 22 mm2, respectively. Two arrays of the proposed antenna are utilized for breast cancer detection. Two 12-element arrays of the antennas are located around a breast tissue model in two scenarios. The results show the promising performance of Ant. 2, which has a higher fidelity factor in detecting a 4 mm tumor. The investigation was also carried out on a clinical tumor model, and Ant. 2 successfully detected it.

Theranostic nanoplatforms integrating fluorescence imaging and photothermal therapy have shown great potential in cancer research for tumor detection and image-guided treatment. Herein, a unique th...

Brain tumor can be considered to be a fatal and life-threatening disease caused by undesirable cell proliferation in the human brain. Out of the several diseases in the field of medical science, brain tumor turns out to be one of the most uncompromising problems. A tumor can be categorized as benign or malignant types in which benign tumors are non-cancerous while malignant tumors are cancerous tumors. There are numerous tumor detection methods but there are still more research going on in this area since quantitative analysis, an accurate disease diagnosis and detection of brain tumor are very much essential with scientific proofs. Therefore, timely planning can be prepared to save a person’s life with brain tumor. This paper presents a computer aided approach with a 2D convolutional neural network for classifying the brain MRI images into two classes: Normal class and tumor class. In this paper, other classification methods are also used for comparison. The results are compared in terms of precision value, Recall value, F1-Score value. This proposed method shows better accuracy of 97% than the other methods.

Liver cancer has a huge negative impact on the human survival. However, the traditional histopathological diagnostic methods have a large burden on the clinical diagnosis due to the large workload required. To this end, by combining the machine learning method and the whole slide images (WSIs), this paper proposes a novelty method to improve the efficiency of the clinical diagnosis. The core of our proposed method is to use a patch-based convolutional neural network to perform the category prediction (normal or tumor) on the patches extracted from the 60 liver tumor WSIs. Specifically, we design a total of four sets of the comparative experiment for the patch classification in order to screen the classifier with the best classification effect. The best classifier is then used to predict the category of patches in the testing set, and the results are combined to generate a probability heatmap to assist clinical diagnosis in an intuitive way. The experimental comparisons verify the validity of the proposed model which can achieve a classification accuracy rate of 99.94% in the patch classification task.

Radiology is a vast subject and we require more knowledge and understanding for exact detection of tumor in medical science. Thus a need for tumor detection system overcomes the shortage of skilled...

Screening guidelines in patients with inherited endocrine syndromes and others at high risk for developing endocrine cancers have significant implications as early tumor detection is associated with improved outcomes. Given the unique challenges in diagnosis and treatment, patients at high risk for developing endocrine cancers require multidisciplinary care. The complexity of decision making and rarity of these syndromes support referral to high-volume centers with the experience and knowledge to treat these at-risk patients.

OBJECTIVE. The objective of our study was to determine the performance of 3-T multiparametric MRI (mpMRI) for prostate cancer (PCa) detection and localization, stratified by anatomic zone and level, using Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) and whole-mount histopathology (WMHP) as reference. MATERIALS AND METHODS. Multiparametric MRI examinations of 415 consecutive men were compared with thin-section WMHP results. A genitourinary radiologist and pathologist collectively determined concordance. Two radiologists assigned PI-RADSv2 scores and sector location to all detected foci by consensus. Tumor detection rates were calculated for clinical and pathologic (tumor location and zone) variables. Both rigid and adjusted sector-matching models were used to account for fixation-related issues. RESULTS. Of 863 PCa foci in 16,185 prostate sectors, the detection of overall and index PCa lesions in the midgland, base, and apex was 54.9% and 83.1%, 42.1% and 64.0% (p = 0.04, p = 0.02), and 41.9% and 71.4% (p = 0.001, p = 0.006), respectively. Tumor localization sensitivity was highest in the midgland compared with the base and apex using an adjusted match compared with a rigid match (index lesions, 71.3% vs 43.7%; all lesions, 70.8% vs 36.0%) and was greater in the peripheral zone (PZ) than in the transition zone. Three-Tesla mpMRI had similarly high specificity (range, 93.8-98.3%) for overall and index tumor localization when using both rigid and adjusted sector-matching approaches. CONCLUSION. For 3-T mpMRI, the highest sensitivity (83.1%) for detection of index PCa lesions was in the midgland, with 98.3% specificity. Multiparametric MRI performance for sectoral localization of PCa within the prostate was moderate and was best for index lesions in the PZ using an adjusted model.

Purpose To compare tumor motion amplitudes measured with 2D fluoroscopic images (FI) and with an inhale/exhale CT (IECT) technique Materials and methods Tumor motion of 52 patients (39 lung patients and 13 liver patients) was obtained with both FI and IECT. For FI, tumor detection and tracking was performed by means of a software developed by the authors. Motion amplitude and, thus, internal target volume (ITV), were defined to cover the positions where the tumor spends 95% of the time. The algorithm was validated against two different respiratory motion phantoms. Motion amplitude in IECT was defined as the difference in the position of the centroid of the gross tumor volume in the image sets of both treatments. Results Important differences exist when defining ITVs with FI and IECT. Overall, differences larger than 5 mm were obtained for 49%, 31%, and 9.6% of the patients in Superior‐Inferior (SI), Anterior‐Posterior (AP), and Lateral (LAT) directions, respectively. For tumor location, larger differences were found for tumors in the liver (73.6% SI, 27.3% AP, and 6.7% in LAT had differences larger than 5 mm), while tumors in the upper lobe benefitted less using FI (differences larger than 5 mm were only present in 27.6% (SI), 36.7% (AP), and 0% (LAT) of the patients). Conclusions Use of FI with the linac built‐in CBCT system is feasible for ITV definition. Large differences between motion amplitudes detected with FI and IECT methods were found. The method presented in this work based on FI could represent an improvement in ITV definition compared to the method based on IECT due to FI permits tumor motion acquisition in a more realistic situation than IECT.

Variation in mechanical properties is a useful marker for cancer in soft tissue and has been used in clinical diagnosis for centuries. However, to develop such methods as instrumented palpation, there remain challenges in using the mechanical response during palpation to quantify tumor load. This study proposes a computational framework of identification and quantification of cancerous nodules in soft tissue without a priori knowledge of its geometry, size, and depth. The methodology, using prostate tissue as an exemplar, is based on instrumented palpation performed at positions with various indentation depths over the surface of the relevant structure (in this case, the prostate gland). The profile of force feedback results is then compared with the benchmark in silico models to estimate the size and depth of the cancerous nodule. The methodology is first demonstrated using computational models and then validated using tissue-mimicking gelatin phantoms, where the depth and volume of the tumor nodule is estimated with good accuracy. The proposed framework is capable of quantifying a tumor nodule in soft tissue without a priori information about its geometry, thus presenting great promise in clinical palpation diagnosis for a wide variety of solid tumors including breast and prostate cancer. Graphical abstractThis study proposes a computational framework of quantification of cancerous nodules in soft tissue. The methodology is based on instrumental palpation performed at positions with various indentation depths. The profile of force feedback results is then compared with the benchmark in silico models to estimate the size and depth of the cancerous nodule. Electronic supplementary material The online version of this article (10.1007/s11517-020-02168-y) contains supplementary material, which is available to authorized users.

Background: Screening technologists may function as readers in breast cancer screening programs. In the Netherlands, they attend quality assurance sessions. The frequency and characteristics of additional breast cancers detected through these sessions have not been reported.Purpose: To determine the frequency and characteristics of cancers detected through quality assurance sessions.Materials and Methods: This secondary analysis of a prospective cohort included 466 647 screening mammograms obtained between January 1, 2009, and January 1, 2017. Mammograms were single read by certified screening technologists before being double read by two certified screening radiologists who were not blinded to the technologists' reading. The technologists and a coordinating screening radiologist regularly discussed mammograms that the technologists considered suspicious but that did not prompt recall at radiologist double reading. The coordinating radiologist decided whether secondary recall was indicated. During a 2-year follow-up, radiologic and pathologic outcome data for all recalled women were obtained. Characteristics of cancers detected at radiologist double reading and those detected through quality assurance sessions were compared by using chi(2) and Fisher exact tests.Results: A total of 14 142 women (mean age, 59 years +/- 7.8 [standard deviation]; range, 49-75 years) were recalled (recall rate, 3.0% [14 142 of 466 647]): 14 057 after radiologist double reading and 85 by the coordinating radiologists after quality assurance sessions. This resulted in 3156 screening-detected cancers (6.8 cancers detected per 1000 screenings), of which 26 (0.8% of screening-detected cancers [26 of 3156]) were detected after secondary recall through quality assurance sessions. The latter comprised eight ductal carcinomas in situ (88% intermediate or high grade [seven of eight]) and 18 invasive cancers (14 T1a-c and four T2+ cancers, 89% Nottingham grade I or II [16 of 18]). No significant differences in tumor characteristics were found (P values ranging from.22 to.95). Sensitivity of quality assurance sessions for additional cancer detection was 52% (26 of 50; 95% confidence interval: 38%, 66%).Conclusion: The role of quality assurance sessions in additional cancer detection is limited. Tumor characteristics did not differ significantly from those of cancers detected at radiologist double reading. (C) RSNA, 2020.

Magnetic resonance-based amide proton transfer weighted (APTw) imaging is one subset of the chemical exchange saturation transfer (CEST) imaging techniques and could provide a potential novel tool of tumor detection and characterization. The purpose of this study was to compare the reproducibility of APTw imaging with two-dimensional (2D) and three-dimensional (3D) techniques in rat intracranial gliomas. Thirty-two Sprague–Dawley (SD) male rats bearing intracranial C6 glioma underwent 2D and 3D APTw imaging twice with a 3 T scanner, respectively. Paired t test was used to compare the difference of magnetization transfer ratio asymmetry (MTRasym) values obtained from 2D and 3D APTw images with respect to tumor and the contralateral normal-appearing brain tissue. The reproducibility of MTRasym measurement values was evaluated using Bland–Altman plot. Our results showed that the reproducibility of MTRasym measurement values from 3D APTw imaging is superior to 2D APTw technique. Therefore, 3D APTw imaging for the brain tumor would be recommended to use in clinical practice with a clinically relevant time frame and research works considering the good reproducibility compared to 2D APTw imaging.

INTRODUCTION: Current methods for diagnosing cancer can be invasive and costly. In recent years, extensive research has been conducted on using exosomes as biomarkers for cancer detection. Exosomes are 40-150 nm-sized extracellular vesicles released by all cell types, including tumor cells. Exosomes are stable in body fluids due to their lipid bilayer member and often contain DNA, RNA, and proteins. These exosomes can be harvested from blood, plasma, serum, urine or saliva and analyzed for tumor-relevant mutations. Thus, providing an alternative to current methods of tumor detection. AREAS COVERED: This review discusses the use of exosomal diagnostics in various tumor types as well as their examination in various clinical trials. The authors also discuss the limitations of exosome-based diagnostics in the clinical setting and provide examples of several studies in which the development and usage of microfluidic chips and nano sensing devices have been utilized to address these obstacles. EXPERT COMMENTARY: In recent years, exosomes and their contents have exhibited potential as novel tumor detection markers despite the labor involved in their harvest and isolation. Despite this, much work is being done to optimize exosome capture and analysis. Thus, their roles as biomarkers in the clinical setting appears promising.

Convolutional Neural Networks (CNNs) is a deep learning model used for image classification. The objective of our work is to analyze the different CNN models in the classification of benign tumor slices from MRI brain volumes and select a suitable CNN model for brain tumor detection. In this paper, we have developed six CNN models and trained using BraTS2013 dataset and tested with the WBA dataset. We have inferred the results for all the six models. The best model for classification of tumor slices is found among the six models. The accuracy of each and every model is recorded. Our models have attained about 96–99% of accuracy.

The patients with high microsatellite instability (MSI-H)/mismatch repair deficient (dMMR) tumors recently have been reported that can benefit from immunotherapy, and MSI can be used as a genetic instability of a tumor detection index. However, many studies have shown that there are many heterogeneous phenomena in patients with MSI tumors in terms of immunotherapy, prognosis and chemotherapy sensitivity. Here we mainly review the research results of MSI detection methods, the mechanisms of MSI occurrence and its relationship with related tumors, aiming to make a brief analysis of the current research status of MSI and provide comparable reference and guidance value for further research in this field.

Herein, we developed fluorinated nanoemulsions with significantly enhanced in vitro and in vivo129Xe hyper-CEST MRI, 19F MRI and fluorescence imaging signals for selective and sensitive tumor detection and NIR-activated photodynamic therapy.

Lung cancer is a fatal type of cancer and it causes of severe deaths of approximately 422 people every day, worldwide. However, an early diagnosis is an expedient requirement for increasing the chances of human survival. In this regard the existing techniques of tumor detection, CT scans are mostly utilized to recognize the infected regions, nevertheless, the major challenges of CT images are low visibility of tumor regions, negative rates, to name but a few. In this work, we propose a novel design of contrast stretching based classical features fusion process for localizing the of lungs cancer classification. The proposed method encompasses three significant steps: firstly, contrast of original CT images is improved by gamma correction max intensity weights approach. Secondly, multiple texture, point, and geometric features are extracted from contrast images, which are later subjected to a serial canonical correlation-based fusion. Thirdly, zero values and negative features are replaced by an entropy-based approach, followed by weighted NCA for selection. Most discriminate features are fed into ensemble classifier for final classification. The validation of the proposed method is conducted on publicly available dataset: Lungs Data Science Bowl 2017 to achieving maximum accuracy of 99.4%. The numerical results clearly show that the performance of our proposed method outperforms in comparison with several existing methods with greater accuracy.

In recent years, the issue of tumor detection for Magnetic Resonance Imaging (MRI) brain images has become a research hotspot in the field of medical imaging, multimedia and pattern recognition. In this paper, we propose a tumor detection method based on saliency modeling for MRI brain images. Firstly, in order to overcome the influence of the skull, we utilize the morphological method to strip the skull of the MRI brain images. Then, we introduce a principal local contrast based saliency-detection method to enhance the foreground regions which facilitates to get the leision region. Finally, the results are further improved by denoising, segmentation and morphological operations. Experiments performed on MRI brain images show that the proposed method is useful and effective.

Purpose: Strain-promoted azide-alkyne cycloaddition (SPAAC) is a straightforward and multipurpose conjugation strategy. Use of SPAAC to link different functional elements to prostate specific membrane antigen (PSMA) ligands would facilitate the development of a modular platform for PSMA-targeted imaging and therapy of prostate cancer (PCa). As a first proof-of-concept for the SPAAC chemistry platform we synthesized and characterized four dual-labeled PSMA ligands for intraoperative radiodetection and fluorescence imaging of PCa. Methods: Ligands were synthesized using solid phase chemistry and contained a chelator for 111In or 99mTc labeling. The fluorophore IRDye800CW was conjugated using SPAAC chemistry or conventional N-hydroxysuccinimide (NHS)-ester coupling. LogD values were measured and PSMA-specificity of these ligands was determined in LS174T-PSMA cells. Tumor targeting was evaluated in BALB/c nude mice with subcutaneous LS174T-PSMA and LS174T wildtype tumors using µSPECT/CT imaging, fluorescence imaging, and biodistribution studies. Results: SPAAC chemistry increased lipophilicity of the ligands (range LogD: -2.4 to -4.4). In vivo, SPAAC chemistry ligands showed high and specific accumulation in s.c. LS174T-PSMA tumors up to 24 hours after injection, enabling clear visualization using µSPECT/CT and fluorescence imaging. Overall, no significant differences between the SPAAC chemistry ligands and their NHS-based counterparts were found (2 h p.i., p > 0.05), while 111In-labeled ligands outperformed the 99mTc ligands. Conclusion: Here we demonstrate that our newly developed SPAAC-based PSMA ligands show high PSMA-specific tumor targeting. Use of click-chemistry in PSMA ligand development opens up the opportunity for fast, efficient and versatile conjugations of multiple imaging moieties and/or drugs.

Introduction: The increase in the number of new head and neck cancer (HNC) cases is expected in the coming decades; therefore, an investigation of novel and noninvasive markers for tumor detection, prediction and prognosis is an urgency for cancer management. Among the putative and universal markers of the tumor, microRNAs (miRNAs) seem to be the most promising.Areas covered: In the following review paper, the recent studies analyzing potential utility of blood circulating miRNAs for HNC diagnosis, prediction, and prognosis were summarized and discussed.Expert opinion: Circulating miRNAs demonstrate numerous advantages and potentially high applicability in routine clinical practice for HNC diagnosis, prediction, and prognosis. However, currently the particular attention should be devoted to the disadvantages, such as methodological aspects, number of known miRNAs, tumor heterogeneity or tests validation and possible ways of their solution.

Although the recent development and widespread use of image-enhanced endoscopy and magnifying endoscopy have improved endoscopic diagnosis of gastric cancer, it is somewhat complicated, requires a higher level of expertise, and is still subjective. Photodynamic endoscopic diagnosis (PDED) is based on the fluorescence of photosensitizers that accumulate in tumors, which enables objective evaluation independent of the endoscopist’s experience, and is useful for tumor detection. The objective of this work was to perform a narrative review of PDED for gastric tumors and to introduce our approach to PDED in gastric tumors in our hospital. In our review there have been case reports of PDED for gastric cancer, but its usefulness has not been established because no prospective studies evaluating its usefulness have been performed. In our previous study, 85.7% (42/49) of gastric tumors exhibited fluorescence in PDED. PDED may be useful in the diagnosis of early gastric cancer. Our previous studies were pilot studies in cancer patients; therefore, future prospective studies are required to verify the usefulness of PDED.

Simple Summary One of the hottest areas in deep learning is computerized tumor diagnosis and treatment. The identification of tumor markers, the outline of tumor growth activity, and the staging of various tumor kinds are frequently included. There are several deep learning models based on convolutional neural networks that have high performance and accurate identification, with the potential to improve medical tasks. Breakthroughs and updates in computer algorithms and hardware devices, and intelligent algorithms applied in medical images have a diagnostic accuracy that doctors cannot match in some diseases. This paper reviews the progress of tumor detection from traditional computer-aided methods to convolutional neural networks and demonstrates the potential of the practical application of convolutional neural networks from practical cases to transform the detection model from experiment to clinical application. Abstract Tumors are new tissues that are harmful to human health. The malignant tumor is one of the main diseases that seriously affect human health and threaten human life. For cancer treatment, early detection of pathological features is essential to reduce cancer mortality effectively. Traditional diagnostic methods include routine laboratory tests of the patient’s secretions, and serum, immune and genetic tests. At present, the commonly used clinical imaging examinations include X-ray, CT, MRI, SPECT scan, etc. With the emergence of new problems of radiation noise reduction, medical image noise reduction technology is more and more investigated by researchers. At the same time, doctors often need to rely on clinical experience and academic background knowledge in the follow-up diagnosis of lesions. However, it is challenging to promote clinical diagnosis technology. Therefore, due to the medical needs, research on medical imaging technology and computer-aided diagnosis appears. The advantages of a convolutional neural network in tumor diagnosis are increasingly obvious. The research on computer-aided diagnosis based on medical images of tumors has become a sharper focus in the industry. Neural networks have been commonly used to research intelligent methods to assist medical image diagnosis and have made significant progress. This paper introduces the traditional methods of computer-aided diagnosis of tumors. It introduces the segmentation and classification of tumor images as well as the diagnosis methods based on CNN to help doctors determine tumors. It provides a reference for developing a CNN computer-aided system based on tumor detection research in the future.

Purpose: Glioblastoma Multiform (GBM) is one of the most common and deadly malignant brain tumors. Surgery is the primary treatment, and careful surgery can minimize recurrence odds. Magnetic Resonance Imaging (MRI) imaging with Magnetic Resonance Spectroscopy (MRS) is used to diagnose various types of tumors in the Central Nervous System (CNS). In this study, several classification methods were used to separate tumor and healthy tissue. Materials and Methods: This study examined the MRI and MRS results of seven people enrolled in this study in 2018. The data was obtained with a prescription from a neurologist and neurosurgeon. Choline (Cho) and N-Acetylaspartate (NAA) metabolite signals were selected as the reference signal after preprocessing and removing the water signal. With the support of 3 radiologists, each tumor and healthy vesicles were identified for every patient. Then, tumor and healthy voxels were separated based on Multilayer Perceptron (MLP), linear Support Vector Machine (SVM), Gaussian SVM, and Fuzzy system using the obtained values and four different methods. Results: Data extracted from Cho and NAA metabolites were fed into MLP, linear SVM, Gaussian and Fuzzy SVM as input, and the amounts of accuracy, sensitivity, and specificity were determined for each method. The maximum accuracy for training mode and test mode was equal to 89.7% and 87%, respectively, specific to classification using Gaussian SVM. The results also showed that the classification accuracy can be significantly increased by increasing the number of fuzzy membership functions from 2 to 6. Conclusion: The results of this study suggested that a more complex classification system, such as SVM with a Gaussian kernel and fuzzy system can be more efficient and reliable when it comes to separating tumor tissue from healthy tissues from MRS data.

AIM: To evaluate the effectiveness of complementary imaging of high-resolution ultrasound including CEUS with PET/CT for tissue characterization and tumor detection. MATERIAL AND METHODS: 100 patients were examined with PET/CT and US/CEUS between January 2018 until February 2020. All patients underwent PET/CT followed by selective US/CEUS within 4 weeks. Comparison regarding concordant or diverging findings in PET/CT and US. Analysis of the differences concerning the lesions number of found by PET/CT and US/CEUS or the possibility of a secured diagnosis following ultrasound causing therapeutic changes. RESULTS: Diverging findings regarding the number of liver lesions in PET/CT and CEUS were found in 35 out of 64 patients (54%). Regarding renal lesions, a more definite diagnosis following ultrasound, causing a change of therapeutic approach, was achieved in 89%. Concordant results in PET/CT and US were found in 83% of patients with splenic and nodal findings. In 78% of patients with increased musculoskeletal or soft tissue tracer uptake, US was able to make a secured diagnosis with therapeutic changes. CONCLUSION: The present results indicate a strong benefit of complementary imaging of PET/CT and selective, high-resolution ultrasound especially in patients with liver, renal and musculoskeletal or soft tissue findings.

Objective This study aimed to investigate the most accurate magnetic resonance (MR) sequence for tumor detection, maximal tumor diameter, and parametrial invasion compared with histopathologic diagnoses. Methods Fifty-one patients with International Federation of Gynecology and Obstetrics 2018 IB1 to IIB cervical cancer underwent preoperative MR imaging and surgical resection. Two radiologists independently evaluated the tumor detection, parametrial invasion, and tumor size in each of T2-weighted image, diffusion-weighted image, and contrast-enhanced T1-weighted image. Results obtained for squamous cell carcinoma (SCC) and adenocarcinoma were also compared. Results Neither the tumor detection rate nor parametrial invasion was found to be significantly different among sequences. Tumor size assessment using MR imaging with pathology showed good correlation: r = 0.63-0.72. The adenocarcinoma size tended to be more underestimated than SCC in comparison with the pathologic specimen. Conclusions Cervical cancer staging by MR images showed no significant difference among T2-weighted image, diffusion-weighted image, and contrast-enhanced T1-weighted image. Adenocarcinoma was prone to be measured as smaller than the pathologic specimen compared with SCC.

Diffusion-weighted imaging (DWI), a key component in multiparametric MRI (mpMRI), is useful for tumor detection and localization in clinically significant prostate cancer (csPCa). The Prostate Imaging Reporting and Data System versions 2 and 2.1 (PI-RADS v2 and PI-RADS v2.1) emphasize the role of DWI in determining PIRADS Assessment Category in each of the transition and peripheral zones. In addition, several recent studies have demonstrated comparable performance of abbreviated biparametric MRI (bpMRI), which incorporates only T2-weighted imaging and DWI, compared with mpMRI with dynamic contrast-enhanced MRI. Therefore, further optimization of DWI is essential to achieve clinical application of bpMRI for efficient detection of csPC in patients with elevated PSA levels. Although DWI acquisition is routinely performed using single-shot echo-planar imaging, this method suffers from such as susceptibility artifact and anatomic distortion, which remain to be solved. In this review article, we will outline existing problems in standard DWI using the single-shot echo-planar imaging sequence; discuss solutions that employ newly developed imaging techniques, state-of-the-art technologies, and sequences in DWI; and evaluate the current status of quantitative DWI for assessment of tumor aggressiveness in PC.