This second part of a commentary examines the role of numbers and measurements in oncology, arguing that blind adherence to numerical cutoffs can harm patient care.
Ovarian cancer is used as a primary example, though similar issues arise across oncology. Clinical trials in platinum-resistant ovarian cancer commonly limit the number of prior regimens allowed for enrollment. There is no clear scientific principle that justifies any single numeric cutoff. Patients declared “platinum-resistant” may have received several platinum-based regimens earlier in their disease course and still meet that classification. Conversely, a patient who recurred quickly after one platinum course may have different prior exposure but not necessarily a different likelihood of responding to a novel agent in the resistant setting.
Differences are more likely to appear in toxicity risk than in efficacy. Multiple prior regimens can reduce bone marrow reserve and increase the risk of cumulative neurotoxicity, affecting tolerance of further therapy. Numeric limits on prior treatments are therefore often used to create clinically homogeneous populations for assessing both efficacy and toxicity, particularly in phase 3 randomized trials. Such inclusion criteria—for example, excluding patients with more than two prior regimens—are appropriate for trial validity. However, trial-based restrictions should not be interpreted as scientific or clinical justification to deny potentially beneficial therapies to patients who fall outside those limits in routine care.
Other examples illustrate the uneven application of numerical rules. Many hospice programs require physician certification that a patient is expected to live less than six months to qualify for services. While this aims to target end-of-life benefits, predicting survival for patients with advanced cancer over a period of months is often imprecise, limiting the policy’s clinical usefulness beyond the terminal days.
Prognosis discussions with patients also demonstrate limitations of numerical data. Oncologists rightly emphasize the statistical nature of survival estimates, but published outcome information is frequently anchored only to the time of initial diagnosis. Patients who have already survived specified intervals need updated, conditional survival estimates reflecting their current status. Investigators have begun publishing conditional survival analyses, but expanded effort is required to keep survival measurements current, objective, and clinically meaningful as therapies improve.
A further example concerns the largest residual tumor mass after primary cytoreductive surgery for stage III or IV ovarian cancer. The standard criterion for “small-volume residual disease” is no mass greater than 1 cm in maximum diameter. Requiring busy surgeons to measure every visible unresectable lesion precisely is impractical; audits of surgical records for intraperitoneal chemotherapy trials found multiple instances of “0.99 cm” recorded as the maximum diameter to meet eligibility. This practice underscores the practical and conceptual problems of rigid numeric cutoffs.
Numbers and measurements serve important roles in oncology research and care, but they must be applied thoughtfully. Rigid adherence to arbitrary numeric thresholds can impede optimal treatment decisions; clinicians and researchers should use numeric criteria to inform, not dictate, individualized care and should work to make outcome and eligibility measures more clinically relevant.
Editor’s note: The first part of this commentary was published previously.
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