Translating potential improvement in the precision and accuracy of lung nodule measurements on computed tomography scans by software derived from artificial intelligence into impact on clinical practice:a simulation study

Objectives Accurate measurement of lung nodules is pivotal to lung cancer detection and management. Nodule size forms the main basis of risk categorisation in existing guidelines. However, measurements can be highly variable between manual readers. This paper explores the impact of potentially improved nodule size measurement assisted by generic artificial intelligence (AI)-derived software on clinical management compared with manual measurement. Methods The simulation study created a baseline cohort of people with lung nodules, guided by nodule size distributions reported in the literature. Precision and accuracy were simulated to emulate measurement of nodule size by radiologists with and without the assistance of AI-derived software and by the software alone. Nodule growth was modelled over a 4-year time frame, allowing evaluation of management strategies based on existing clinical guidelines. Results Measurement assisted by AI-derived software increased cancer detection compared to an unassisted radiologist for a combined solid and sub-solid nodule population (62.5% vs 61.4%). AI-assisted measurement also correctly identified more benign nodules (95.8% vs 95.4%), however it was associated with over an additional month of surveillance on average (5.12 vs 3.95 months). On average, with AI assistance people with cancer are diagnosed faster, and people without cancer are monitored longer. Conclusions In this simulation, the potential benefits of improved accuracy and precision associated with AI-based diameter measurement is associated with additional monitoring of non-cancerous nodules. AI may offer additional benefits not captured in this simulation, and it is important to generate data supporting these, and adjust guidelines as necessary. Advances in Knowledge This paper shows the effects of greater measurement accuracy associated with AI assistance compared with unassisted measurement

Dopamine Transporter imaging with Tc-99m TRODAT-1 SPECT in Parkinson’s disease and its correlation with clinical disease severity

Objective(s): To evaluate the role of Tc-99m TRODAT-1 Single Photon Emission Computed Tomography (SPECT) in Parkinson’s Disease (PD) by assessing the correlation of clinical disease severity, disease duration and age at onset of disease with specific uptake ratio of Tc-99m TRODAT-1 in striatum.Methods: The study included 63 patients in age range of 40-72 years with clinical diagnosis of PD and nine controls. Clinical history of patients was obtained regarding age at onset of disease and disease duration. Disease severity in each patient was assessed using H and Y stage and UPDRS. Tc-99m TRODAT-1 SPECT was performed and specific uptake ratios were calculated for six regions in bilateral striata, caudate nuclei and putamina. Difference in specific uptake ratios between different stages of disease was analyzed for statistical significance. Specific uptake ratios were correlated with UPDRS, motor score of UPDRS, duration of disease and age at onset of disease using Pearson’s correlation co-efficient.Results: Median specific uptake ratio was found to be least in contralateral putamen for all H and Y stages. There was a statistically significant difference between specific uptake ratios of controls vs stage 1, stage 1 vs 2, 1 vs 3, 1 vs 4, and 2 vs 4 for all 6 regions. The difference in uptake ratio between 3 and 4 H and Y stages was significant only for contralateralregions. There was no significant difference in uptake ratio between 2 and 3 H and Y stages. The uptake ratios showed a strong negative correlation with UPDRS and motor score, a weak negative correlation with duration of disease and no significant correlation with age at onset of disease.Conclusion: We conclude that Tc-99m TRODAT-1 SPECT can be used to assess the disease severity in PD patients

Communication--Computation Trade-offs in PIR

We study the computation and communication costs and their possible trade-offs in various constructions for private information retrieval (PIR), including schemes based on homomorphic encryption and the Gentry--Ramzan PIR (ICALP\u2705). We improve over the construction of SealPIR (S&P\u2718) using compression techniques and a new oblivious expansion, which reduce the communication bandwidth by 60% while preserving essentially the same computation cost. We then present MulPIR, a PIR protocol leveraging multiplicative homomorphism to implement the recursion steps in PIR. This eliminates the exponential dependence of PIR communication on the recursion depth due to the ciphertext expansion, at the cost of an increased computational cost for the server. Additionally, MulPIR outputs a regular homomorphic encryption ciphertext, which can be homomorphically post-processed. As a side result, we describe how to do conjunctive and disjunctive PIR queries. On the other end of the communication--computation spectrum, we take a closer look at Gentry--Ramzan PIR, a scheme with asymptotically optimal communication rate. Here, the bottleneck is the server\u27s computation, which we manage to reduce significantly. Our optimizations enable a tunable trade-off between communication and computation, which allows us to reduce server computation by as much as 85%, at the cost of an increased query size. We further show how to efficiently construct PIR for sparse databases. Our constructions support batched queries, as well as symmetric PIR. We implement all of our PIR constructions, and compare their communication and computation overheads with respect to each other for several application scenarios

Software using artificial intelligence for nodule and cancer detection in CT lung cancer screening: systematic review of test accuracy studies:artificial intelligence and nodule and lung cancer

Objectives: To examine the accuracy and impact of artificial intelligence (AI) software assistance in lung cancer screening using computed tomography (CT).Methods: A systematic review of CE-marked, AI-based software for automated detection and analysis of nodules in CT lung cancer screening was conducted. Multiple databases including Medline, Embase and Cochrane CENTRAL were searched from 2012 to March 2023. Primary research reporting test accuracy or impact on reading time or clinical management was included. QUADAS2/QUADAS-C were used to assess risk of bias. We undertook narrative synthesis.Results: Eleven studies evaluating six different AI-based programs and reporting on 19,770 patients were eligible. All were at high risk of bias with multiple applicability concerns. Compared to unaided reading, AI-assisted reading was faster and generally improved sensitivity (+5% to +20% for detecting/categorising actionable nodules; +3% to +15% for detecting/categorising malignant nodules), with lower specificity (-7% to -3% for detecting/categorising actionable nodules; -8% to -2% for detecting/categorising malignant nodules). AI assistance tended to increase the proportion of nodules allocated to higher risk categories. Assuming 0.5% cancer prevalence, these results would translate into additional 150 to 750 cancers detected per million participants but lead to an additional 59,700 to 79,600 participants without cancer receiving unnecessary CT surveillance.Conclusions: AI assistance in lung cancer screening may improve sensitivity but increases the number of false positive results and unnecessary surveillance. Future research needs to increase the specificity of AI-assisted reading and minimise risk of bias and applicability concerns through improved study design.<br/