Simple Blood Test Score Predicts Which Lung Cancer Patients Benefit From Immunotherapy

Immune checkpoint inhibitors have transformed the treatment of advanced non-small cell lung cancer, turning a disease that was once almost uniformly fatal into one where some patients live for years. Yet oncologists face a persistent and frustrating problem: the drugs work spectacularly for some people and do almost nothing for others, and no single test reliably tells them apart in advance. A new retrospective study from researchers at the Second Affiliated Hospital of Soochow University in Suzhou, China, published in Medical Oncology, offers a strikingly simple answer drawn from two routine blood measurements that nearly every cancer patient already receives.

The research team, led by Yiqing Hu and Zhigang Chen, analyzed data from 340 patients with advanced non-small cell lung cancer who were treated with immune checkpoint inhibitors in real-world clinical settings. Rather than chasing expensive molecular signatures, the investigators turned to hemoglobin, the oxygen-carrying protein in red blood cells, and to the albumin-to-alkaline phosphatase ratio, a calculation that weighs a marker of nutritional status and liver function against an enzyme often elevated by tumor activity in bone and tissue. Both measurements, the team found through Cox regression analysis, stood out as independent predictors of how long patients remained free of cancer progression.

The logic behind these two biomarkers is rooted in the biology of the tumor microenvironment. Anemia in cancer is not merely a symptom of fatigue; recent work has shown that tumors can actively hijack macrophages to secure iron supplies, promoting both metastasis and anemia, and that tumor-induced alterations in erythroid precursor cells can suppress the immune response and blunt the effectiveness of anti-PD-1 and anti-PD-L1 therapies. Low hemoglobin, in other words, may be a visible fingerprint of an immunosuppressive state that undermines the very drugs designed to unleash the immune system.

The albumin-to-alkaline phosphatase ratio tells a complementary story. Albumin reflects the body’s nutritional reserve and synthetic liver capacity, while alkaline phosphatase rises when cancer spreads to bone or when inflammation churns through the biliary system. A low ratio therefore signals a patient whose nutritional reserves are depleted while tumor burden and inflammatory activity are high. Previous meta-analytic work has already linked pretreatment albumin-to-alkaline phosphatase ratios to outcomes in lung cancer, but the Suzhou team went a step further by fusing the two measures into a single, easy-to-compute score.

They named it the Nutritional Inflammatory Hematological Risk Index, or NIHRI. The construction is deliberately simple: hemoglobin is dichotomized at 120 grams per liter, the standard clinical threshold for anemia, and the albumin-to-alkaline phosphatase ratio is dichotomized at 0.56, a cutoff derived from the analysis. Each adverse value earns one point, so every patient lands in one of three groups: low risk with zero points, intermediate risk with one point, or high risk with two points. When the researchers stratified their 340 patients this way, the differences in progression-free survival between the groups were highly statistically significant, with a p-value below 0.001.

What makes the finding clinically meaningful is not just the stratification but the way it performs alongside established prognostic factors. The team built a nomogram, a graphical scoring tool that clinicians can use at the bedside, incorporating the NIHRI together with tumor stage, PD-L1 expression status, and the Eastern Cooperative Oncology Group performance status, the standard measure of a patient’s functional fitness. The combined model achieved a concordance index of 0.70, rising to an optimism-corrected 0.69 after bootstrapping, an internal validation technique that guards against statistical overfitting by repeatedly resampling the data.

The predictive accuracy of the nomogram grew stronger the longer the time horizon. The area under the receiver operating characteristic curve, a standard measure of discrimination, reached 0.81 for one-year progression-free survival, 0.85 for two-year survival, and 0.88 for three-year survival. Decision curve analysis, a method that quantifies the net clinical benefit of using a model to guide treatment choices rather than treating everyone identically, confirmed that the tool offered acceptable clinical utility across a meaningful range of threshold probabilities. In practical terms, the model is accurate enough to be worth acting on.

The implications reach into one of the most consequential decisions in modern oncology: who should receive immunotherapy, in what combination, and with what intensity of monitoring. Patients flagged as high risk by the NIHRI might be candidates for closer surveillance, earlier imaging, combination regimens that pair checkpoint inhibitors with chemotherapy or anti-angiogenic agents, or enrollment in trials of strategies designed to overcome resistance. Conversely, patients in the low-risk tier might be spared the added toxicity of aggressive combination therapy if single-agent immunotherapy is likely to suffice. Because the index requires nothing beyond a complete blood count and a standard chemistry panel, it can be applied at essentially any oncology clinic in the world without new infrastructure or cost.

The study’s real-world design is both its strength and its caveat. Randomized trials enroll carefully selected patients, but this cohort reflects the heterogeneity of everyday clinical practice, where age, comorbidities, and performance status vary widely. That breadth suggests the index could generalize well, though the authors themselves note the retrospective nature of the analysis and the need for external validation in independent, ideally prospective, cohorts before the tool enters routine use. The work was supported by the State Key Laboratory of Radiation Medicine and Protection at Soochow University, and the authors declare no competing interests.

Still, the broader message resonates far beyond lung cancer. As immunotherapy costs strain health systems worldwide and biomarker-guided medicine grows ever more complex, the idea that two numbers from a routine blood draw can meaningfully forecast who will benefit from a hundred-thousand-dollar therapy is a reminder that the body’s basic physiology, nutrition, inflammation, and oxygen transport, remains deeply intertwined with the immune system’s war on cancer. The NIHRI will not replace molecular profiling or PD-L1 testing, but as a cheap, non-invasive first filter it may help clinicians personalize one of oncology’s most important decisions, and it may inspire similar indices across other tumor types where immunotherapy outcomes remain stubbornly unpredictable.

Subject of Research: A prognostic blood-based index combining hemoglobin and albumin-to-alkaline phosphatase ratio for predicting outcomes in advanced non-small cell lung cancer patients receiving immune checkpoint inhibitor therapy.

Article Title: Nutritional inflammatory hematological risk index predicts clinical outcomes in patients with advanced NSCLC receiving immunotherapy

Article References: Hu, Y., Chen, Z., Yao, M., & Gan, L. (2026). Nutritional inflammatory hematological risk index predicts clinical outcomes in patients with advanced NSCLC receiving immunotherapy. Medical Oncology, 43(11), Article 329. https://doi.org/10.1007/s12032-026-03440-1

Image Credits: AI Generated

DOI: 10.1007/s12032-026-03440-1

Keywords: non-small cell lung cancer, immune checkpoint inhibitors, NIHRI, hemoglobin, albumin-to-alkaline phosphatase ratio, nomogram, progression-free survival, PD-L1, prognostic biomarkers, anemia, immunotherapy, ECOG performance status