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IntroductionPopulation cancer registries record primary cancer incidence, mortality and survival for whole populations, but not more timely outcomes such as cancer recurrence, secondary cancers or other complications that disrupt event-free survival. Nonetheless, indirect evidence may be inferred from treatment data to provide indicators of recurrence and like events, which can facilitate earlier assessment of care outcomes. The present study aims to infer such evidence by applying algorithms to linked cancer registry and treatment data obtained from hospitals and universal health insurance claims applicable to the New South Wales (NSW) population of Australia.Materials and methodsPrimary invasive cancers from the NSW Cancer Registry (NSWCR), diagnosed in 2001–2018 with localized or regionalized summary stage, were linked to treatment data for five common Australian cancers: breast, colon/rectum, lung, prostate, and skin (melanomas). Clinicians specializing in each cancer type provided guidance on expected treatment pathways and departures to indicate remission and subsequent recurrence or other disruptive events. A sample survey of patients and clinicians served to test initial population-wide results. Following consequent refinement of the algorithms, estimates of recurrence and like events were generated. Their plausibility was assessed by their correspondence with expected outcomes by tumor type and summary stage at diagnosis and by their associations with cancer survival.ResultsKaplan-Meier product limit estimates indicated that 5–year cumulative probabilities of recurrence and other disruptive events were lower, and median times to these events longer, for those staged as localized rather than regionalized. For localized and regionalized cancers respectively, these were: breast - 7% (866 days) and 34% (570 days); colon/rectum - 15% (732 days) and 25% (641 days); lung - 46% (552 days) and 66% (404 days); melanoma - 11% (893 days) and 38% (611 days); and prostate - 14% (742 days) and 39% (478 days). Cases with markers for these events had poorer longer-term survival.ConclusionsThese population-wide estimates of recurrence and like events are approximations only. Absent more direct measures, they nonetheless may inform service planning by indicating population or treatment sub-groups at increased risk of recurrence and like events sooner than waiting for deaths to occur.

Background: 177 LuPSMA is an effective treatment in metastatic castrate-resistant prostate cancer with trials adopting a standardised dose interval. Adjusting treatment intervals utilising early response biomarkers may improve patient outcomes. Objective: This study evaluated progression-free survival (PFS) and overall survival (OS) based on treatment interval adjustment utilising 177 LuPSMA 24-h SPECT/CT ( 177 Lu-SPECT) and early prostate-specific antigen (PSA) response. Design: Retrospective analysis of a clinical 177 Lu-PSMA-I&T treatment programme. Methods: In all, 125 men were treated with 6-weekly 177 LuPSMA-I&T [median 3 cycles, interquartile range (IQR): 2–4], median dose 8.0 GBq [95% confidence interval (CI): 7.5–8.0]. Imaging screening involved 68 GaPSMA-11 PET/diagnostic CT. 177 Lu-SPECT/diagnostic CT was acquired following each therapy, and clinical assessments 3-weekly. Following dose 2 (week 6), a composite PSA and 177 Lu-SPECT/CT imaging response [partial response (PR), stable disease (SD), and progressive disease (PD)] determined ongoing management. Response group (RG) 1 (marked reduction in PSA/imaging PR) break in treatment until subsequent PSA rise, then re-treatment. RG 2 (stable or reduced PSA and/or imaging SD) 6-weekly treatments until six doses, or no longer clinically benefitting. RG 3 (rise in PSA and/or imaging PD) recommended for an alternative treatment. Results: Overall PSA50% response rate (PSARR) was 60% (75/125), median PSA-PFS 6.1 months (95%CI: 5.5–6.7), and median OS 16.8 months (95%CI: 13.5–20.1). 35% (41/116) were classified as RG 1, 34% (39/116) RG 2, and 31% (36/116) RG 3. PSARRs by RG were 95% (38/41), 74% (29/39), and 8% (3/36); median PSA-PFS rates were 12.1 months (95%CI: 9.3–17.4), 6.1 months (95%CI: 5.8–9.0), and 2.6 months (95%CI: 1.6–3.1); and OS rates were 19.2 months (95%CI: 16.8–20.7), 13.2 months (95%CI: 12.0–18.8), and 11.2 months (95%CI: 8.7–15.6) for RG 1, 2, and 3, respectively. The median months of ‘treatment holiday’ for RG 1 was 6.1 months (IQR: 3.4–8.7). Nine men had received prior 177 LuPSMA-617 and were retreated with 177 LuPSMA-I&T, with a PSARR of 56% on re-treatment. Conclusion: Personalising dosing regimens using early response biomarkers with 177 LuPSMA has the potential to achieve similar treatment responses to continuous dosing while allowing treatment breaks or intensification. Further evaluation of early response biomarker-guided treatment regimens in prospective trials is warranted. Plain Language Summary Lutetium-PSMA therapy is a new therapy for metastatic prostate cancer that is well tolerated and effective. However, not all men respond equally, with some responding very well and others progressing early. Personalising treatments require tools that can accurately measure treatment responses, preferably early in the treatment course, so adjustments to treatment can be made. Lutetium-PSMA can measure tumour sites after each therapy by taking whole body 3D images at 24 h using a small radiation wave from the treatment itself. This is called a SPECT scan. Previous work has shown that both prostate-specific antigen (PSA) response and changes in tumour volume on a SPECT scan can predict how patients will respond to treatment as early as dose 2. This study demonstrates that stratifying how men are treated based on the results of the 6-week SPECT scan and PSA response potentially allows a third of men to have break in treatment and that these men have both longer time to disease progression and OS. Men with an increase in tumour volume and increase in PSA early in treatment (6 weeks) had shorter time to disease progression and OS. Men with early biomarker disease progression were offered alternative treatments early in an attempt to allow the opportunity to allow a more effective potential therapy, if one was available. The study is an analysis of a clinical programme, and was not a prospective trial. As such, there are potential biases that could influence results. Hence, while the study is encouraging for the use of early response biomarkers to guide better treatment decisions, this must be validated in a well-designed clinical trial.