Background: Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantly transformed cancer cells past their normal boundaries into adjacent tissues. It is the leading cause of death worldwide, responsible for approximately 19.3 million new diagnoses and 10 million deaths globally in 2020. In the United States alone, the estimated number of new diagnoses and deaths is 1.9 million and 609 360, respectively. Implementation of currently existing cancer diagnostic techniques such as positron emission tomography (PET), X-ray computed tomography (CT), and magnetic resonance spectroscopy (MRS), and molecular diagnostic techniques, have enabled early detection rates and are instrumental not only for the therapeutic management of cancer patients, but also for early detection of the cancer itself. The effectiveness of these cancer screening programs are heavily dependent on the rate of accurate precursor lesion identification; an increased rate of identification allows for earlier onset treatment, thus decreasing the incidence of invasive cancer in the long-term, and improving the overall prognosis. Although these diagnostic techniques are advantageous due to lack of invasiveness and easier accessibility within the clinical setting, several limitations such as optimal target definition, high signal to background ratio and associated artifacts hinder the accurate diagnosis of specific types of deep-seated tumors, besides associated high cost. In this review we discuss various imaging, molecular, and low-cost diagnostic tools and related technological advancements, to provide a better understanding of cancer diagnostics, unraveling new opportunities for effective management of cancer, particularly in low-and middle-income countries (LMICs).Recent Findings: Herein we discuss various technological advancements that are being utilized to construct an assortment of new diagnostic techniques that incorporate hardware, image reconstruction software, imaging devices, biomarkers, and even artificial intelligence algorithms, thereby providing a reliable diagnosis and analysis of the tumor. Also, we provide a brief account of alternative low cost-effective cancer therapy devices (CryoPop ® , LumaGEM ® , MarginProbe ® ) and picture archiving and communication systems (PACS), emphasizing the need for multi-disciplinary

Objective: Racial and ethnic minority groups are underrepresented in clinical research. Racially diverse individuals that speak languages other than English or have limited proficiency may be hindered from participation in randomized clinical trials (RCTs) through eligibility criteria. This study sought to assess English language requirements for enrollment in registered and published RCTs. Design: In a cross-sectional design, we searched for RCTs in the top 10 first-quartile general and internal medicine journals in 2017 on May 4, 2022, with at least one US site comparing heart disease, stroke, cancer, asthma, influenza and pneumonia, diabetes, HIV/AIDS, and COVID-19 drug interventions with standard or usual care or placebo with ClinicalTrials.gov registration and protocols. We assessed whether English or another language was required for trial enrollment in the eligibility criteria in protocols and ClinicalTrials.gov records. Good agreement was achieved by independent selection by two reviewers for inclusion (κ = 0.85; 95% CI, 0.75-0.95) and both the identification of language requirements and data extraction in RCTs (κ = 0.98; 95% CI, 0.87-1.00) from a sample of 50 RCTs. The primary outcome was the frequency of RCTs with English language requirements in eligibility criteria in protocols and ClinicalTrials.gov records by disease and funder type (industry funders had at least one industry funder, while non-industry funders had no industry funding). Secondary outcomes were readability of eligibility criteria in ClinicalTrials.gov records and reporting of race as a demographic variable. Readability was assessed with Flesch-Kincaid grade (FKG) level (ranges from grades 0 to 18 [college graduate]) and Gunning-Fog (GF) (ranges from grades 0 to 20 [college graduate]), where lower grades correspond to easier readability. Mann-Whitney tests compared readability with a 2-tailed P-value set at less than 0.05. Results: A total of 39 of 5995 RCTs from Annals of Internal Medicine (n = 2), JAMA (n = 14), JAMA Internal Medicine (n = 3), Lancet (n = 11), PLoS Medicine (n = 1), and New England Journal of Medicine (n = 8) were found. Trials mostly studied COVID-19 (n=18/39, 46%) and were industry-funded (n=23/39, 59%). The eligibility criteria in publications or ClinicalTrials.gov made no explicit statements about English or any other language required for enrollment. The lack of explicit statements about languages required for enrollment were common in both industry-funded (n=17/39, 44%) and non-industry funded (n=8/39, 21%) described in protocols. Eligibility criteria in protocols of 3 out of 39 (8%) non-industry funded RCTs restricted participation to English-speaking participants. Ten (26%) industry-funded and non-industry funded trials (both n=5/39, 13%) mentioned providing non-English languages. Participant race was reported in 37 (95%) articles and ClinicalTrials.gov records that comprised American Indian (median [interquartile range (IQR)], 1 [0-6]), Asian (14 [5-69]), Black (44 [36-100]), Latinx (45 [5-117]), Native Hawaiian (0 [0-1]), and White (229 [106-207]) participants. There were 17/39 (44%) RCTs with at least one difference in the reporting of race in the article and ClinicalTrials.gov. Eligibility criteria in protocols had a median (IQR) FKG of 11.5 (10.7-13.0) and GF of 13.0 (11.7-14.5) and in ClinicalTrials.gov, the median (IQR) FKG was 13.0 (11.0-14.0) and GF was 13.7 (IQR 11.7-14.7). In protocols, readability did not differ by funder (FKG for non-industry; 12.1 (11.4-13.3) vs. FKG for industry; 11.0 (10.3-12.6) and GF for non-industry; 13.4 (12.2-14.7) vs. GF for industry; 12.90 (11.6-14.5)), P=0.092 and, (P=0.567), respectively. In ClinicalTrials.gov, readability did not differ by funder (FKG for non-industry; 12.9 (11.7-13.9) vs. FKG for industry; 13.5 (10.7-14.6) and GF for non-industry; 14.5 (11.7-15.1) vs. GF for industry; 13.4 (12.2-15.7), P=0.575 and GF P=0.338, respectively. Conclusions: There was low explicit reporting of required languages in RCT eligibility criteria, and readability levels of eligibility criteria were low. Ethics committees and funders should obligate the inclusion of the explicit reporting of languages and high readability of information for participants. Accordingly, responsibility rests with ethics committees, funders, and trialists to conceive inclusive trials to strive toward health equity.