The study, published in BMC Cancer by a team led by Susanne von Kroge of University Medical Center Hamburg-Eppendorf together with collaborators at the Robert Koch Institute and the Hamburg Cancer Registry, drew on harmonized nationwide registry data covering the years 2020 through 2023, with vital status follow-up extending to December 2024. From a total of 16,443 patients diagnosed with a first primary diffuse large B-cell lymphoma, 10,872 began systemic therapy within 56 days of diagnosis and formed the primary analysis cohort. The median age was 71 years, 44.2 percent of patients were female, and the median follow-up lasted about a year and a half.

The researchers divided patients into groups according to the diagnosis-to-treatment interval, a measure epidemiologists call the DTI, ranging from treatment in the first week after diagnosis to initiation between weeks five and eight. Three-year overall survival across these five groups ranged from roughly 63 percent to 75 percent. On its face, that spread might seem to suggest that timing matters enormously. But when the team applied statistical models, the direction of the association was the opposite of what the urgency narrative would predict.

Using Kaplan-Meier survival curves and Cox proportional hazards regression, the investigators found that compared with patients treated in week one, those who started later had progressively lower hazards of death. Patients beginning therapy in week three had a 28 percent lower hazard of dying from any cause, with a hazard ratio of 0.72 and a 95 percent confidence interval of 0.64 to 0.81. Those starting in week four fared better still, at a hazard ratio of 0.63, and patients treated between weeks five and eight showed a hazard ratio of 0.62, or a 38 percent reduction. The confidence intervals in every case excluded one, indicating the associations were statistically robust.

To a clinician, such a pattern immediately raises a red flag of confounding. Doctors do not assign treatment dates at random. Patients with the most aggressive, bulky, or symptomatic disease are rushed into the infusion chair within days, while those with more indolent-appearing disease can safely wait for complete staging, cardiac assessments, and fertility discussions. If sicker patients are treated fastest, then early treatment will appear to worsen survival even if timing itself has no causal effect, a classic example of what epidemiologists call confounding by indication, or in this context, the waiting-time paradox.

The German team addressed this head-on by adjusting their models for patient, tumor, and structural characteristics, most importantly the International Prognostic Index, or IPI, the standard five-factor score that combines age, disease stage, number of extranodal sites, performance status, and lactate dehydrogenase levels. Low-risk IPI strongly predicted better survival, with a hazard ratio of 0.24 compared with high-risk disease. Even after this adjustment, a residual association between longer diagnosis-to-treatment intervals and improved survival persisted, hinting that the interval itself might carry independent prognostic information in patients with less aggressive disease.

Perhaps the most revealing part of the study is what predicted delay in the first place. The researchers modeled which factors pushed treatment later and found that low-risk IPI scores added an average of 7.89 days to the interval, while older age added 0.62 days per standard deviation. Patients who survived the first year had, on average, started treatment 3.26 days later than those who did not. In other words, the patients who waited longest were systematically the ones with the least threatening disease, and the ones treated most urgently were those whose lymphoma demanded immediate action.

Missing IPI data posed a methodological challenge that the authors handled with unusual care. Rather than discarding incomplete records, they treated missing IPI as a separate category and ran both complete-case and multiple-imputation sensitivity analyses to test whether their conclusions depended on how missingness was handled. The consistency of the results across these approaches strengthens the central finding that early initiation is primarily a marker of adverse disease biology rather than a driver of survival in its own right.

The implications for health policy and clinical practice are subtle but significant. In many healthcare systems, time-to-treatment is used as a quality indicator, with targets and audits pressing hospitals to start cancer therapy faster. For solid tumors with validated screening pathways, such metrics can be meaningful. But this study suggests that for diffuse large B-cell lymphoma, raw speed from diagnosis to treatment is a poor proxy for quality of care. Penalizing centers for longer intervals could perversely reward the triage of less sick patients while doing nothing for those with aggressive disease, who are already treated within days.

The authors conclude that early treatment in newly diagnosed DLBCL is primarily driven by adverse disease characteristics, and that the diagnosis-to-treatment interval may function as an independent prognostic factor mainly in less aggressive disease. The study was funded by the Innovationsfonds zur Förderung von Versorgungsforschung under grant number 01VSF25057, and the underlying data came from German state cancer registries used under legal provisions permitting scientific analysis of non-identifiable records. For the roughly 16,000 Germans diagnosed each year with this lymphoma, and for oncologists everywhere who feel the pressure of the clock, the message is a nuanced one: the calendar matters less than the biology, and a few extra days of careful preparation may be exactly what the least aggressive tumors allow, and what the most aggressive ones cannot afford.

Subject of Research: The association between the diagnosis-to-treatment interval and survival in newly diagnosed diffuse large B-cell lymphoma

Article Title: Insights into the survival impact of delayed immunochemotherapy in newly diagnosed diffuse large B-cell lymphoma: results from a nationwide registry study

Article References: von Kroge, S., Steinhaus, N. J., Bokemeyer, C., Tetzlaff, F., Walk, H., Kusche, H., LYSED study group, Klotz, S., Obi, N., Knesebeck, O. V., Beernink, M., Gundler, C., Ückert, F., Schulze, K., Augustin, J., Semmelhaack, R., Sassenberg-Gummert, A., & Peters, F. (2026). Insights into the survival impact of delayed immunochemotherapy in newly diagnosed diffuse large B-cell lymphoma: results from a nationwide registry study. BMC Cancer, 26(1), Article 1144. https://doi.org/10.1186/s12885-026-17024-9

Image Credits: AI Generated

DOI: 10.1186/s12885-026-17024-9

Keywords: diffuse large B-cell lymphoma, diagnosis-to-treatment interval, treatment delay, immunochemotherapy, survival, International Prognostic Index, cancer registry, Cox regression, prognostic markers, waiting-time paradox, hematology, oncology

The research, published in Cancer Causes & Control, analyzed the records of 425 women treated at INCAN between November 2017 and December 2021, a window that captures roughly two and a half years of pre-pandemic care and the first two years of the pandemic itself. All patients were experiencing breast cancer for the first time, and all had complete documentation of the dates of both diagnosis and the start of treatment. The investigators calculated the diagnosis-to-treatment interval, or DTI, for every patient in days, and defined timely care as the initiation of treatment within 30 days of diagnosis, a benchmark consistent with international quality standards for breast cancer management.

On the surface, the headline numbers look almost reassuring. Among the 425 women, the median DTI was 18 days for those who began treatment on time, with an interquartile range of 14 to 24 days, while patients classified as delayed had a median interval of 42 days, spanning an interquartile range of 35 to 55 days. Comparing the two eras of care, the median DTI was 28 days before the pandemic and 23.5 days during it. The proportion of women starting treatment within the 30-day target was statistically indistinguishable between periods: 58.7 percent before the pandemic versus 61.9 percent during it, a difference the investigators tested and found non-significant at P equal to 0.509. If the analysis had stopped there, one might conclude that Mexico City’s flagship cancer hospital weathered the pandemic with its treatment pipelines intact.

But the raw comparison of periods conceals a more complicated reality, and the study’s multivariable analysis is where the pandemic’s fingerprint becomes visible. When the researchers adjusted for other factors influencing timeliness, receiving care during the pandemic was independently associated with roughly half the odds of timely treatment compared with the pre-pandemic era, with an adjusted odds ratio of 0.54 and a 95 percent confidence interval of 0.33 to 0.88, reaching statistical significance at P equal to 0.014. In other words, even though the overall proportion of timely cases appeared stable, women treated during the pandemic faced systematically higher risk of slipping past the 30-day threshold once the composition of the patient population and other influencing variables were taken into account. This kind of divergence between crude and adjusted estimates is a classic signal in epidemiology: it suggests the pandemic disrupted care in ways that were unevenly distributed across patient subgroups rather than uniformly lengthening everyone’s wait.

The most striking determinant of timeliness in the study was not the calendar but the type of first treatment a patient received. Women who began their treatment course with chemotherapy had dramatically higher odds of meeting the 30-day target than those who started with surgery, with an adjusted odds ratio of 19.77 and a 95 percent confidence interval of 5.23 to 74.62, a result significant at P less than 0.001. Hormone therapy as the initial treatment also conferred an advantage, with an adjusted odds ratio of 2.79 and a 95 percent confidence interval of 1.14 to 6.85, significant at P equal to 0.024. The clinical logic behind this gap is rooted in logistics. Chemotherapy and endocrine therapy can be initiated in an outpatient setting once a diagnosis is confirmed and a treatment plan is made, whereas surgery requires an available operating room, anesthesia capacity, a surgical team, and a hospital bed, all resources that the pandemic squeezed hardest.

The study also quantified, with unusual precision, the cost of referral friction. Each additional day that elapsed between a patient’s first consultation and her referral to medical oncology reduced the odds of timely treatment by roughly 1 percent, with a per-day odds ratio of 0.99 and a 95 percent confidence interval of 0.98 to 0.99, significant at P equal to 0.005. That may sound like a small effect for a single day, but compounded across the weeks that often separate a first visit from a specialist handoff, the cumulative impact is substantial. Referral pathways are among the least glamorous components of cancer care, yet this analysis shows they are among the most consequential: the journey of a paperwork packet between departments can determine whether a woman begins potentially curative therapy within the recommended month or watches the clock run past it.

Socioeconomic status emerged as another meaningful axis of inequality. In the bivariate analysis, women of high socioeconomic status had lower odds of timely treatment than those of low status, with an odds ratio of 0.36 and a 95 percent confidence interval of 0.14 to 0.96, significant at P equal to 0.042. The authors note this counterintuitive pattern, and while the underlying mechanisms warrant further investigation, it underscores that timeliness in a centralized referral system does not map neatly onto wealth. Patients arriving at a national reference center come through many routes, and the study’s broader message is that institutional process, not patient resources alone, drives how quickly treatment begins once someone enters the system.

The findings sit within a global literature documenting pandemic-era disruption of cancer services. Studies from the Netherlands, France, Australia, and the United States reported surgical delays, shifts toward neoadjuvant systemic therapy, and temporary drops in screening diagnoses during 2020 and 2021. What distinguishes the INCAN analysis is its setting and its methodological candor. In a middle-income country where diagnosis-to-treatment delays were already recognized as a persistent challenge before 2020, the pandemic arrived as a stress test on a system operating without slack. The fact that median intervals actually shortened slightly during the pandemic years, while adjusted odds of timeliness fell, illustrates how aggregate statistics can mask the experiences of the subset of patients who bore the brunt of disruption.

That subset matters clinically. Prior research cited in the study, including population-based cohorts from Queensland, Australia, and surgical oncology analyses from the United States, links longer treatment intervals with inferior survival in breast cancer. A delayed patient in this Mexican cohort faced a median wait of 42 days, two full weeks beyond the timeliness threshold, and the authors describe these delays as clinically relevant. International guideline bodies, including EUSOMA and ESMO, treat time to first treatment as a core quality indicator precisely because it is modifiable: it reflects scheduling, coordination, and decision-making speed rather than tumor biology.

Perhaps the most practical takeaway from the INCAN team is their argument that the fixes identified require no new infrastructure. Because the dominant predictors of delay were referral logistics and the choice of first treatment modality, streamlining the pathway from first consultation to medical oncology, and protecting surgical capacity so that patients whose optimal first treatment is an operation are not funneled into prolonged queues, could restore timeliness even under conditions of severe system stress. As health systems worldwide continue absorbing the aftershocks of the pandemic, this Mexico City cohort study serves as a reminder that resilience in cancer care is built in the seams between departments, in the speed of a referral, and in the days that pass, each one quietly eroding a patient’s odds, between diagnosis and the first dose of treatment.

Subject of Research: The effect of the SARS-CoV-2 pandemic on the diagnosis-to-treatment interval for breast cancer patients at a reference cancer center in Mexico City

Article Title: Effect of the SARS-CoV-2 pandemic on the opportunity to treat patients with breast cancer: experience at a reference cancer center in Mexico City

Article References: Effect of the SARS-CoV-2 pandemic on the opportunity to treat patients with breast cancer: experience at a reference cancer center in Mexico City. (n.d.). https://doi.org/10.1007/s10552-026-02235-z

Image Credits: AI Generated

DOI: 10.1007/s10552-026-02235-z

Keywords: breast cancer, SARS-CoV-2, COVID-19 pandemic, treatment delay, diagnosis-to-treatment interval, Mexico, National Cancer Institute, oncology, health services research, surgery, chemotherapy, health equity