Bladder cancer, the leading urinary tract cancer, is responsible for 15 deaths daily in the UK. This cancer predominantly manifests as non-muscle-invasive bladder cancer (NMIBC), characterised by tumours not yet penetrating the muscle layer of the bladder wall. NMIBC is plagued by a very high recurrence rate of 70-80% and hence the costliest treatments. Current tools for predicting recurrence use scoring systems that overestimate risk and have poor accuracy. Inaccurate and delayed prediction of recurrence significantly elevates the likelihood of mortality. Accurate prediction of recurrence is hence vital for cost-effective management and treatment planning. This is where Machine learning (ML) techniques have emerged as a promising approach for predicting NMIBC recurrence by leveraging molecular and clinical data. This review provides a comprehensive analysis of ML approaches for predicting NMIBC recurrence. Our systematic evaluation demonstrates the potential of diverse ML algorithms and markers, including radiomic, clinical, histopathological, genomic, and biochemical data in enhancing recurrence prediction and personalised patient management. We summarise various prediction tasks, data modalities, and ML models used, highlighting their performance, limitations, and future directions of incorporating cost-effectiveness. Challenges related to generalisability and interpretability of artificial intelligent models are discussed, emphasising the need for collaborative efforts and robust datasets.

Bladder cancer ranks within the top 10 most diagnosed cancers worldwide and is among the most expensive cancers to treat due to the high recurrence rates which require lifetime follow-ups. The primary tool for diagnosis is cystoscopy, which heavily relies on doctors' expertise and interpretation. Therefore, annually, numerous cases are either undiagnosed or misdiagnosed and treated as urinary infections. To address this, we suggest a deep learning approach for bladder cancer detection and segmentation which combines CNNs with a lightweight positional-encoding-free transformer and dual attention gates that fuse self and spatial attention for feature enhancement. The architecture suggested in this paper is efficient making it suitable for medical scenarios that require real time inference. Experiments have proven that this model addresses the critical need for a balance between computational efficiency and diagnostic accuracy in cystoscopic imaging as despite its small size it rivals large models in performance.

Cancer is one of the most widespread diseases around the world with millions of new patients each year. Bladder cancer is one of the most prevalent types of cancer affecting all individuals alike with no obvious prototypical patient. The current standard treatment for BC follows a routine weekly Bacillus Calmette-Guerin (BCG) immunotherapy-based therapy protocol which is applied to all patients alike. The clinical outcomes associated with BCG treatment vary significantly among patients due to the biological and clinical complexity of the interaction between the immune system, treatments, and cancer cells. In this study, we take advantage of the patient's socio-demographics to offer a personalized mathematical model that describes the clinical dynamics associated with BCG-based treatment. To this end, we adopt a well-established BCG treatment model and integrate a machine learning component to temporally adjust and reconfigure key parameters within the model thus promoting its personalization. Using real clinical data, we show that our personalized model favorably compares with the original one in predicting the number of cancer cells at the end of the treatment, with 14.8% improvement, on average.

We developed an AI-based diagnostic model that did well in detecting lymph node metastases, particularly micrometastases. The LNMDM showed substantial potential for clinical applications in improving the accuracy and efficiency of pathologists' work.

In a small but multi-institutional study, an artificial intelligence-based system improved providers' assessments of whether patients with bladder cancer had complete response to chemotherapy before a radical cystectomy (bladder removal surgery). Yet the researchers caution that AI isn't a replacement for human expertise and that their tool shouldn't be used as such. "If you use the tool smartly, it can help you," said Lubomir Hadjiyski, Ph.D., a professor of radiology at the University of Michigan Medical School and the senior author of the study. When patients develop bladder cancer, surgeons often remove the entire bladder in an effort to keep the cancer from returning or spreading to other organs or areas. More evidence is building, though, that surgery may not be necessary if a patient has zero evidence of disease after chemotherapy.

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is d-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2′-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites.

The invasive and expensive diagnosis process of bladder cancer, which is one of the most common and aggressive cancers in the United States, may be soon helped by a novel non-invasive diagnostic method thanks to advances in machine learning research at the San Diego Supercomputer Center (SDSC), Moores Cancer Center, and CureMatch Incorporated. Research scientists Igor Tsigelny and Valentina Kouznetsova have been working on the development of a machine-learning (ML) model that looks at a patient's metabolites and their chemical descriptors. The model accurately classifies the stages of bladder cancer in a patient, according to the researchers. Tsigelny is the lead author on a recently published study in the Metabolomics journal called'Recognition of Early and Late Stages of Bladder Cancer using Metabolites and Machine Learning'. When a patient experiences early symptoms of bladder cancer (e.g., blood in urine, pain during urination, etc.), the current method of diagnosis is often a painful, invasive series of tests.

NEW YORK: The outcome of robotic-assisted surgery and traditional open surgery are equally effective in treating bladder cancer, say researchers, led by one of an Indian-origin. The results, published in the journal The Lancet, may help patients and doctors to make informed decisions on the use of robotic surgery, which is not cheap, the researchers said. There has been an assumption that patients who receive robotic surgery will perceive a better quality of life than patients who have open surgery. However, the trial showed that both groups had a significant return to their previous quality of life, and there was no advantage of one group over the other at three and six months after surgery. "We have done more than four million surgeries with the robotic approach since the device came into existence, and on average we do close to a million robotic surgeries a year globally," said Dipen J. Parekh, Chief Clinical Officer at the University of Miami, Florida in the US.

Bladder cancer presents a major and increasing health risk worldwide with early diagnosis being recognized as key to its successful management and treatment. This is the first work to propose the use of data extracted from immunofluorescence images together with readily available data collected from patients referred to cystoscopy as a means of stratification and in particular for the reduction in the number of unnecessary further cystoscopies. We present a thorough analysis of the problem and thus a carefully designed machine learning based solution that achieves this goal. Using a real-world data corpus and thorough statistical analysis, we demonstrate that our method is capable of distinguishing patients at high risk of having bladder cancer with an error rate of less than 5%, who can be prioritized for follow-up examination and treatment, while at the same time reducing unnecessary financial and resource burden, as well as substantial patient discomfort, by correctly identifying 66% of low risk patients with cystoscopy associated morbidities.