Methods of analyzing data output from artificialintelligence for cancer diagnosis

Cancer is characterized by the rampant proliferation, growth, and infiltration of malignantlytransformed cancer cells past their normal boundaries into adjacent tissues. It is the leadingcause of death worldwide, responsible for approximately ۱۹.۳ million new diagnoses and ۱۰million deaths globally in ۲۰۲۰. In the United States alone, the estimated number of newdiagnoses and deaths is ۱.۹ million and ۶۰۹ ۳۶۰, respectively. Implementation of currentlyexisting cancer diagnostic techniques such as positron emission tomography (PET), X‐raycomputed tomography (CT), and magnetic resonance spectroscopy (MRS), and moleculardiagnostic techniques, have enabled early detection rates and are instrumental not only for thetherapeutic 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 ofaccurate precursor lesion identification; an increased rate of identification allows for earlieronset treatment, thus decreasing the incidence of invasive cancer in the long‐term, andimproving the overall prognosis. Although these diagnostic techniques are advantageous dueto lack of invasiveness and easier accessibility within the clinical setting, several limitationssuch as optimal target definition, high signal to background ratio and associated artifacts hinderthe 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 relatedtechnological advancements, to provide a better understanding of cancer diagnostics,unraveling new opportunities for effective management of cancer

Keywords:

Cancer diagnosis , medical imaging , cancer , medicine , analysis