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We consider the problem of a swarm of nanobots detecting and treating human cancer that is diffuse, that is, dispersed in a region with multiple separate cancer sites in need of treatment. We present a mathematical model of nanobots and their colloidal environment that is inspired by actual chemotactic nanoparticles, involving agents noisily following chemical gradients (both attractively and repellently, depending on the chemical). We present three incrementally sophisticated algorithms that describe additional chemical payloads that agents carry onboard, beyond the cancer-treating drug K, as well as the rules for when agents drop their payloads: Algorithm KM, in which agents simply ascend naturally existing chemical M signals that surround cancer sites; Algorithm KMA, in which agents themselves amplify these natural signals by dropping chemical A payloads upon reaching a site; and Algorithm KMAR, in which agents choose to either amplify the signal by dropping chemical A or counteract/reduce the signal by dropping chemical R, according to the current unsatisfied demand of the site. We present simulation results for all of the algorithms, across a set of distinct cancer arrangements, that track both the achieved treatment success as well as the time/duration of the treatment. KM has generally successful treatment unless the natural M-signals are weak, in which case the treatment progresses too slowly. KMA demonstrates a significant speedup in treatment time (over KM), but also a drop in success except for the most concentrated cancer patterns. KMAR has relatively optimal performance across all types of cancer patterns, demonstrating robustness and adaptability in its mechanisms for nanobot coordination.