Functional magnetic resonance imaging in oncology: state of the art

Systematic review and meta-analysis of prediction models used in cervical cancer

BACKGROUND

Cervical cancer is one of the most common cancers in women with an incidence of around 6.5 % of all the cancer in women worldwide. Early detection and adequate treatment according to staging improve the patient's life expectancy. Outcome prediction models might aid treatment decisions, but a systematic review on prediction models for cervical cancer patients is not available.

DESIGN

We performed a systematic review for prediction models in cervical cancer following PRISMA guidelines. Key features that were used for model training and validation, the endpoints were extracted from the article and data were analyzed. Selected articles were grouped based on prediction endpoints i.e. Group1: Overall survival, Group2: progression-free survival; Group3: recurrence or distant metastasis; Group4: treatment response; Group5: toxicity or quality of life. We developed a scoring system to evaluate the manuscript. As per our criteria, studies were divided into four groups based on scores obtained in our scoring system, the Most significant study (Score > 60 %); Significant study (60 % > Score > 50 %); Moderately Significant study (50 % > Score > 40 %); least significant study (score < 40 %). A meta-analysis was performed for all the groups separately.

RESULTS

The first line of search selected 1358 articles and finally 39 articles were selected as eligible for inclusion in the review. As per our assessment criteria, 16, 13 and 10 studies were found to be the most significant, significant and moderately significant respectively. The intra-group pooled correlation coefficient for Group1, Group2, Group3, Group4, and Group5 were 0.76 [0.72, 0.79], 0.80 [0.73, 0.86], 0.87 [0.83, 0.90], 0.85 [0.77, 0.90], 0.88 [0.85, 0.90] respectively. All the models were found to be good (prediction accuracy [c-index/AUC/R²] >0.7) in endpoint prediction.

CONCLUSIONS

Prediction models of cervical cancer toxicity, local or distant recurrence and survival prediction show promising results with reasonable prediction accuracy [c-index/AUC/R² > 0.7]. These models should also be validated on external data and evaluated in prospective clinical studies.

Generation of peptides using phage display technology for cancer diagnosis and molecular imaging

Cancer is one of the leading causes of mortality worldwide; nearly 10 million people died from it in 2020. The high mortality rate results from the lack of effective screening approaches where early detection cannot be achieved, reducing the chance of early intervention to prevent cancer development. Non-invasive and deep-tissue imaging is useful in cancer diagnosis, contributing to a visual presentation of anatomy and physiology in a rapid and safe manner. Its sensitivity and specificity can be enhanced with the application of targeting ligands with the conjugation of imaging probes. Phage display is a powerful technology to identify antibody- or peptide-based ligands with effective binding specificity against their target receptor. Tumour-targeting peptides exhibit promising results in molecular imaging, but the application is limited to animals only. Modern nanotechnology facilitates the combination of peptides with various nanoparticles due to their superior characteristics, rendering novel strategies in designing more potent imaging probes for cancer diagnosis and targeting therapy. In the end, a myriad of peptide candidates that aimed for different cancers diagnosis and imaging in various forms of research were reviewed.

Dysregulation of butyrylcholinesterase, BCHE gene SNP rs1803274, and pro-inflammatory cytokines in occupational workers

BACKGROUND

People in different occupations are exposed to a variety of xenobiotics which affect the health and physiological processes of the body. Butyrylcholinesterase (BChE), has been reported to play neuronal and non-neuronal roles, though its exact function is yet to be established. This study aimed to find the status and role of BChE in seven different occupational groups; gasoline fillers, auto-mechanics, carpenters, textile shop workers, furniture shop workers, electricians, and office workers.

METHODS

A total of 400 samples were screened. BChE activity was determined by Worek et al. method based on Ellman's principle. Pro-inflammatory cytokines were determined by ELISA. Genotypic analysis of the K-variant of BCHE gene SNP was carried out by standard molecular methods. Among seven groups, office workers were taken as a control to compare the results with all other occupational groups.

RESULTS

The results revealed a significant decrease in BChE activity in gasoline fillers (79.52%) followed by carpenters (73.49%), auto mechanics (39.76%), textile shop workers (18.07%), electricians (10.84%), and furniture shop workers (7.23%). TNF-α, IL-6, and IL1-β were elevated in all groups. IL-6 and IL1-β in gasoline fillers, and electricians were not statistically significantly increased. Binomial regression to determine the odd ratio was found to be significant (p < 0.05) in all groups. However, correlation (Pearson) did not reveal significance between different biochemical parameters. Genotypic analysis of the K-variant SNP of the BCHE gene showed a significant association with occupational groups when compared with control which indicates a possible association with xenobiotics exposure and the physiological role of K-variant in understudied occupational groups.

CONCLUSION

The study concluded that BChE and its gene SNP rs 1803274 and proinflammatory cytokines significantly dysregulates under the exposure to cumulative multiple xenobiotics in different occupational groups which may lead to pathophysiological conditions.

Additively manufactured, solid object structures for adjustable image contrast in Magnetic Resonance Imaging

The choice of materials challenges the development of Magnetic Resonance Imaging (MRI) phantoms and, to date, is mainly limited to water-filled compartments or gel-based components. Recently, solid materials have been introduced through additive manufacturing (AM) to mimic complex geometrical structures. Nonetheless, no such manufactured solid materials are available with controllable MRI contrast to mimic organ substructures or lesion heterogeneities. Here, we present a novel AM design that allows MRI contrast manipulation by varying the partial volume contribution to a ROI/voxel of MRI-visible material within an imaging object. Two sets of 11 cubes and three replicates of a spherical tumour model were designed and printed using AM. Most samples presented varying MRI-contrast in standard MRI sequences, based mainly on spin density and partial volume signal variation. A smooth and continuous MRI-contrast gradient could be generated in a single-compartment tumour model. This concept supports the development of more complex MRI phantoms that mimic the appearance of heterogeneous tumour tissues.

A New Nanomaterial Based Biosensor for MUC1 Biomarker Detection in Early Diagnosis, Tumor Progression and Treatment of Cancer

Early detection of cancer disease is vital to the successful treatment, follow-up and survival of patients, therefore sensitive and specific methods are still required. Mucin 1 (MUC1) is a clinically approved biomarker for determining the cancer that is a type I transmembrane protein with a dense glycosylated extracellular domain extending from the cell surface to 200–500 nm. In this study, nanopolymers were designed with a lectin affinity-based recognition system for MUC1 detection as a bioactive layer on electrochemical biosensor electrode surfaces. They were synthesized using a mini emulsion polymerization method and derivatized with triethoxy-3-(2-imidazolin-1-yl) propylsilane (IMEO) and functionalized with Concanavalin a Type IV (Con A) lectin. Advanced characterization studies of nanopolymers were performed. The operating conditions of the sensor system have been optimized. Biosensor validation studies were performed. Real sample blood serum was analyzed and this new method compared with a commercially available medical diagnostic kit (Enzyme-Linked ImmunoSorbent Assay-ELISA). The new generation nanopolymeric material has been shown to be an affordable, sensitive, reliable and rapid device with 0.1–100 U/mL linear range and 20 min response time.

MRI-based differentiation between lymphoma and sarcoidosis in mediastinal lymph nodes

OBJECTIVE

Evaluation of enlarged mediastinal lymph nodes is crucial for patient management. Malignant lymphoma and sarcoidosis are often difficult to differentiate. Our objective was to determine the diagnostic accuracy of MRI for differentiating between sarcoidosis and malignant lymphoma.

METHODS

This was a retrospective study involving 47 patients who underwent chest MRI and were diagnosed with one of the diseases between 2017 and 2019. T1, T2, and diffusion-weighted signal intensity were measured. Apparent diffusion coefficients (ADCs) and T2 ratios were calculated. The diagnostic performance of MRI was determined by ROC analysis.

RESULTS

Mean T2 ratio was significantly lower in the sarcoidosis group than in the lymphoma group (p = 0.009). The T2-ratio cutoff value that best differentiated between lymphoma-related and sarcoidosis-related enlarged lymph nodes was 7.1, with a sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of 58.3%, 95.6%, 76.5%, 93.3%, and 68.7%, respectively. The mean ADC was significantly lower in the lymphoma group than in the sarcoidosis group (p = 0.002). The ADC cutoff value that best differentiated between lymphoma-related and sarcoidosis-related enlarged lymph nodes was 1.205, with a sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 87.5%, 82.6%, 85.1%, 84.0% and 86.3%, respectively. No significant differences were found between the two groups regarding T1 signal intensity, T2 signal intensity, and lymph node diameter.

CONCLUSIONS

MRI parameters such as ADC, diffusion, and T2 ratio can be useful in the differentiation between sarcoidosis and lymphoma in the evaluation of enlarged lymph nodes.

MR-Guided Radiotherapy for Prostate Cancer

External beam radiotherapy remains the primary treatment modality for localized prostate cancer. The radiobiology of prostate carcinoma lends itself to hypofractionation, with recent studies showing good outcomes with shorter treatment schedules. However, the ability to accurately deliver hypofractionated treatment is limited by current image-guided techniques. Magnetic resonance imaging is the main diagnostic tool for localized prostate cancer and its use in the therapeutic setting offers anatomical information to improve organ delineation. MR-guided radiotherapy, with daily re-planning, has shown early promise in the accurate delivery of radiotherapy. In this article, we discuss the shortcomings of current image-guidance strategies and the potential benefits and limitations of MR-guided treatment for prostate cancer. We also recount present experiences of MR-linac workflow and the opportunities afforded by this technology.

Artificial Intelligence in Lung Cancer Pathology Image Analysis

OBJECTIVE

Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection.

MATERIALS AND METHODS

In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer.

RESULTS

We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis.

DISCUSSION AND CONCLUSION

With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Combining Diagnostic Imaging and Pathology for Improving Diagnosis and Prognosis of Cancer

In the era of personalized medicine, the management of oncological patients requires a translational and multidisciplinary approach. During early phases of cancer development, biochemical alterations of cell metabolism occur much before the formation of detectable tumour masses. Current molecular imaging techniques, targeted to the study of molecular kinetics, employ molecular tracers capable of detecting cancer lesions with both high sensitivity and specificity while also providing essential information for both prognosis and therapy. On the contrary, complementary and crucial information is provided by histopathological examination and ancillary techniques such as immunohistochemistry. Thus, the successful collaboration between diagnostic imaging and anatomic pathology can represent a fundamental step in the "tortuous" but decisive path towards personalized medicine.

Employment of enhanced permeability and retention effect (EPR): Nanoparticle-based precision tools for targeting of therapeutic and diagnostic agent in cancer

In tumorous tissues, the absence of vasculature supportive tissues intimates the formation of leaky vessels and pores (100 nm to 2 μm in diameter) and the poor lymphatic system offers great opportunity to treat cancer and the phenomenon is known as Enhanced permeability and retention (EPR) effect. The trends in treating cancer by making use of EPR effect is increasing day by day and generate multitudes of possibility to design novel anticancer therapeutics. This review aimed to present various factors affecting the EPR effect along with important things to know about EPR effect such as tumor perfusion, lymphatic function, interstitial penetration, vascular permeability, nanoparticle retention etc. This manuscript expounds the current advances and cross-talks the developments made in the of EPR effect-based therapeutics in cancer therapy along with a transactional view of its current clinical and industrial aspects.

Evaluation of the indications for performing magnetic resonance imaging of the female pelvis at a referral center for cancer, according to the American College of Radiology criteria

Objective

To evaluate the indications for performing magnetic resonance imaging of the female pelvis at a referral center for cancer.

Materials and Methods

This was a retrospective, single-center study, conducted by reviewing medical records and imaging reports. We included 1060 female patients who underwent magnetic resonance imaging of the pelvis at a cancer center between January 2013 and June 2014. The indications for performing the examination were classified according to the American College of Radiology (ACR) criteria.

Results

The mean age of the patients was 52.6 ± 14.8 years, and 49.8% were perimenopausal or postmenopausal. The majority (63.9%) had a history of cancer, which was gynecologic in 29.5% and nongynecologic in 34.4%. Of the patients evaluated, 44.0% had clinical complaints, the most common being pelvic pain (in 11.5%) and bleeding (in 9.8%), and 34.7% of patients had previously had abnormal findings on ultrasound. Most (76.7%) of the patients met the criteria for undergoing magnetic resonance imaging, according to the ACR guidelines. The main indications were evaluation of tumor recurrence after surgical resection (in 25.9%); detection and staging of gynecologic neoplasms (in 23.3%); and evaluation of pelvic pain or of a mass (in 17.1%).

Conclusion

In the majority of the cases evaluated, magnetic resonance imaging was clearly indicated according to the ACR criteria. The main indication was local recurrence after surgical treatment of pelvic malignancies, which is consistent with the routine protocols at cancer centers.